MICA: InterdisciPlInary Collaboration for efficienT and effective Use of clinical images in big data health care RESearch: PICTURES
Lead Research Organisation:
University of Dundee
Department Name: Population Health and Genomics
Abstract
Clinical imaging including X-rays, CT, MRI, ultrasound and nuclear medicine scans are core diagnostic technologies. These images can support many important areas of research to improve any or all of diagnosis, monitoring of disease progression and response to treatment. Currently most research using images is based on those collected specifically for a particular research project. The images are of "research" quality. That means that they are captured at high resolution using standardised procedures to reduce the variability of images. Research data collection is expensive so studies tend to be small, and the people who take part in research studies are different to those seen in normal clinical care. It is therefore often uncertain whether research findings can be translated to "real world" images or patients.
Each year millions of clinical images are generated in Scotland through routine examinations at hospitals and stored in a huge database. The Scottish national imaging database currently has ~23 million different images collected since 2010. Access to these "real world" images would be extremely valuable for research, but there are a number of big challenges. Firstly, it is very important that all data is kept confidential. Secondly, imaging datasets are very large which it technically challenging. Thirdly, the software which manages these images is optimised for retrieval of images by NHS staff specifically for an individual patient's clinical care (e.g. return Mrs Jones' scan taken on the 28th of May 2016) rather than for research (e.g. return all the CT chest scans of smokers between age 55 and 65 where a contrast agent has been used).
What will be delivered? This 5 year programme will enable secure access to routinely collected imaging data for research. Using the foundation blocks already in place from previous research grants, PICTURES will extend, scale and enhance innovative open source software to query a research copy of the Scottish National imaging database securely hosted by the University of Edinburgh and provide anonymised extracts of hundreds of thousands of images for research. PICTURES will also develop this software to query imaging data linked to genomic data securely hosted by the University of Dundee.
There are 3 main areas of research required within the core programme: (1) Data science research for complex cohort building from real-world, messy data. (2) Engineering required for scaling and handling big data within a Safe Haven environment. (3) Cybersecurity research needed to ensure that the patient data is securely held and de-identified appropriately for research.
PICTURES will support 2 major exemplar research projects to guide and shape the underpinning resources. Exemplar one will develop a method to detect lung nodules and coronary artery calcification using hundreds of thousands of CT chest scans provided by the core programme. It will also predict the risk of getting lung cancer based upon the presence of lung nodules and the risk of cardiovascular disease based upon the presence of coronary artery calcification. This exemplar will work in partnership with an industrial partner, Aidence, to validate and test the method directly in NHS clinical workstations within the course of the programme.
Exemplar 2 will predict individual risk of dementia in people with diabetes using MRI brain scans, genetic data and medical records. The most important variables will be found. The predictive tool will be validated on the large image dataset provided by the core programme.
Both of our exemplars will determine new information from routinely collected data that would otherwise have been ignored. Predicting and therefore treating diseases at an early stage improves patient outcomes and reduces the cost to the NHS.
PICTURES is truly interdisciplinary requiring expertise in Radiomics, AI, Cybersecurity, Software Engineering, Data Science, Data Governance and Medicine.
Each year millions of clinical images are generated in Scotland through routine examinations at hospitals and stored in a huge database. The Scottish national imaging database currently has ~23 million different images collected since 2010. Access to these "real world" images would be extremely valuable for research, but there are a number of big challenges. Firstly, it is very important that all data is kept confidential. Secondly, imaging datasets are very large which it technically challenging. Thirdly, the software which manages these images is optimised for retrieval of images by NHS staff specifically for an individual patient's clinical care (e.g. return Mrs Jones' scan taken on the 28th of May 2016) rather than for research (e.g. return all the CT chest scans of smokers between age 55 and 65 where a contrast agent has been used).
What will be delivered? This 5 year programme will enable secure access to routinely collected imaging data for research. Using the foundation blocks already in place from previous research grants, PICTURES will extend, scale and enhance innovative open source software to query a research copy of the Scottish National imaging database securely hosted by the University of Edinburgh and provide anonymised extracts of hundreds of thousands of images for research. PICTURES will also develop this software to query imaging data linked to genomic data securely hosted by the University of Dundee.
There are 3 main areas of research required within the core programme: (1) Data science research for complex cohort building from real-world, messy data. (2) Engineering required for scaling and handling big data within a Safe Haven environment. (3) Cybersecurity research needed to ensure that the patient data is securely held and de-identified appropriately for research.
PICTURES will support 2 major exemplar research projects to guide and shape the underpinning resources. Exemplar one will develop a method to detect lung nodules and coronary artery calcification using hundreds of thousands of CT chest scans provided by the core programme. It will also predict the risk of getting lung cancer based upon the presence of lung nodules and the risk of cardiovascular disease based upon the presence of coronary artery calcification. This exemplar will work in partnership with an industrial partner, Aidence, to validate and test the method directly in NHS clinical workstations within the course of the programme.
Exemplar 2 will predict individual risk of dementia in people with diabetes using MRI brain scans, genetic data and medical records. The most important variables will be found. The predictive tool will be validated on the large image dataset provided by the core programme.
Both of our exemplars will determine new information from routinely collected data that would otherwise have been ignored. Predicting and therefore treating diseases at an early stage improves patient outcomes and reduces the cost to the NHS.
PICTURES is truly interdisciplinary requiring expertise in Radiomics, AI, Cybersecurity, Software Engineering, Data Science, Data Governance and Medicine.
Technical Summary
The core programme focuses on 3 key areas:
(1) Complex cohort building from noisy, heterogeneous data: we will develop algorithms for text mining and standardising imaging metadata. We will use machine learning classification techniques to group images and natural language processing to pre-process and mine knowledge features from the free-text data and remove the identifiable data. We will utilise imaging processing algorithms to search for features within the core dataset to build new cohorts based upon pixel data.
(2) Scaling and handling big data: we will develop algorithms and optimisation processes for handling petabytes of imaging data and investigate how to optimise the use of GPUs within a virtual environment.
(3) Cybersecurity: we will ensure that our systems are secure but also meet the requirements of the research community.
Exemplar 1: Using CT scans provisioned from the national resource, we will use Deep Learning to train an algorithm to detect lung nodules and coronary artery calcification. The algorithm will be implemented as a Medical Device and made available within NHS Clinical PACS reporting workstations for Clinical performance evaluation and validation. To determine the risk of lung cancer and cardiovascular events we will use the nationally available longitudinal health outcomes data linked to the results from the CT scans.
Exemplar 2: To develop a risk score for dementia and an understanding of which data is most important for prediction, we will use voxel based feature selection and support vector machines within a cross-validation framework for MRI brain images. Non-imaging analyses will be primarily cross-sectional and longitudinal including time variable exposure clinical covariates as well as genomic covariates. Risk predictions using image, genetics and clinical data for individual patients will be combined using decision tree methods to create a best overall predictor. Algorithm validation will use the national resource.
(1) Complex cohort building from noisy, heterogeneous data: we will develop algorithms for text mining and standardising imaging metadata. We will use machine learning classification techniques to group images and natural language processing to pre-process and mine knowledge features from the free-text data and remove the identifiable data. We will utilise imaging processing algorithms to search for features within the core dataset to build new cohorts based upon pixel data.
(2) Scaling and handling big data: we will develop algorithms and optimisation processes for handling petabytes of imaging data and investigate how to optimise the use of GPUs within a virtual environment.
(3) Cybersecurity: we will ensure that our systems are secure but also meet the requirements of the research community.
Exemplar 1: Using CT scans provisioned from the national resource, we will use Deep Learning to train an algorithm to detect lung nodules and coronary artery calcification. The algorithm will be implemented as a Medical Device and made available within NHS Clinical PACS reporting workstations for Clinical performance evaluation and validation. To determine the risk of lung cancer and cardiovascular events we will use the nationally available longitudinal health outcomes data linked to the results from the CT scans.
Exemplar 2: To develop a risk score for dementia and an understanding of which data is most important for prediction, we will use voxel based feature selection and support vector machines within a cross-validation framework for MRI brain images. Non-imaging analyses will be primarily cross-sectional and longitudinal including time variable exposure clinical covariates as well as genomic covariates. Risk predictions using image, genetics and clinical data for individual patients will be combined using decision tree methods to create a best overall predictor. Algorithm validation will use the national resource.
Planned Impact
Industry: There are a range of companies who will benefit from access to the imaging data e.g. Imaging Equipment Companies (Canon, GE, Siemens, and Philips), Imaging Contrast Media Companies (GE, Guerbet, Bracco) and Medical and Surgical Device Companies (Medtronic, Baxter).
Costs for the academic community: Scalable access to large quantities of routinely collected de-identified images via automated, reproducible processes will reduce the effort of obtaining the data required to answer research questions at scale. These resources will also reduce the effort of obtaining governance.
Widening and optimising access to data: Access to such large numbers of real world images has previously been very challenging. These resources should accelerate research in the field.
Patients: Increasing the availability of large scale routinely collected images linked to other forms of health data for both industry and academic use will lead to a greater likelihood of achieving results translatable into diagnoses and treatments.
Policy makers: There are many advantages of using a Safe Haven model for access to sensitive data. The models developed on the safe handling of big data for research may become an example of good practice worldwide, further raising the profile of UK healthcare research.
HDRUK: The UK wishes to be an internationally recognised centre for population based data research. These resources will add to the complement of excellent data available internationally.
Capacity Building and training: This programme will train the team in data science. There is a recognised shortage of expertise in this field (see Life Sciences Industrial Strategy and the ABPI Bridging the Skills Gap report).
Supporting Learning Healthcare Systems: This model has become a widely recognised approach applying a key principle of a feedback loop from the outcomes of research to directly improve clinical care. The work within PICTURES strongly supports this model.
Both exemplars will generate valuable knowledge in the use of routine clinical imaging as a source of potential clinically useful biomarkers. This will have a major impact on the cost effectiveness of clinical imaging in the NHS as potentially more clinically relevant information will be extracted beyond the clinical indication for the image.
Exemplar 1: We anticipate that the existing lung nodule detection tool will seamlessly interact with the coronary artery calcification tool, which will be developed. This will be relatively straightforward to integrate into PACS reporting workstations, allowing for immediate reporting of these findings during chest CT studies. Examples already exist of this and similar software tools, integrated within a variety of PACS vendor workstations. Once this integration is rolled out throughout the NHS, this should significantly improve the efficiency of radiology reporting, reduce errors of reporting and allow better individualised risk profiling and management options for patient care.
Exemplar 2: There are 3 significant impacts: (1) Enabling the identification of diabetics at risk of developing dementia affording clinicians the opportunity of optimising preventative measures. (2) Potentially facilitating the identification and recruitment of high risk individuals prior to dementia onset into clinical trials of prevention, an urgent need in dementia where trials of treating dementia once it has occurred have not been successful. This process will be augmented by integrating with the SHARE programme in Scotland. (3) The use of feature selection techniques will allow the identification of biological targets as disease biomarkers and/or therapeutic surrogate targets, this is in contrast to deep learning approaches whereby the discrimination is agnostic to biological structure or function.
Costs for the academic community: Scalable access to large quantities of routinely collected de-identified images via automated, reproducible processes will reduce the effort of obtaining the data required to answer research questions at scale. These resources will also reduce the effort of obtaining governance.
Widening and optimising access to data: Access to such large numbers of real world images has previously been very challenging. These resources should accelerate research in the field.
Patients: Increasing the availability of large scale routinely collected images linked to other forms of health data for both industry and academic use will lead to a greater likelihood of achieving results translatable into diagnoses and treatments.
Policy makers: There are many advantages of using a Safe Haven model for access to sensitive data. The models developed on the safe handling of big data for research may become an example of good practice worldwide, further raising the profile of UK healthcare research.
HDRUK: The UK wishes to be an internationally recognised centre for population based data research. These resources will add to the complement of excellent data available internationally.
Capacity Building and training: This programme will train the team in data science. There is a recognised shortage of expertise in this field (see Life Sciences Industrial Strategy and the ABPI Bridging the Skills Gap report).
Supporting Learning Healthcare Systems: This model has become a widely recognised approach applying a key principle of a feedback loop from the outcomes of research to directly improve clinical care. The work within PICTURES strongly supports this model.
Both exemplars will generate valuable knowledge in the use of routine clinical imaging as a source of potential clinically useful biomarkers. This will have a major impact on the cost effectiveness of clinical imaging in the NHS as potentially more clinically relevant information will be extracted beyond the clinical indication for the image.
Exemplar 1: We anticipate that the existing lung nodule detection tool will seamlessly interact with the coronary artery calcification tool, which will be developed. This will be relatively straightforward to integrate into PACS reporting workstations, allowing for immediate reporting of these findings during chest CT studies. Examples already exist of this and similar software tools, integrated within a variety of PACS vendor workstations. Once this integration is rolled out throughout the NHS, this should significantly improve the efficiency of radiology reporting, reduce errors of reporting and allow better individualised risk profiling and management options for patient care.
Exemplar 2: There are 3 significant impacts: (1) Enabling the identification of diabetics at risk of developing dementia affording clinicians the opportunity of optimising preventative measures. (2) Potentially facilitating the identification and recruitment of high risk individuals prior to dementia onset into clinical trials of prevention, an urgent need in dementia where trials of treating dementia once it has occurred have not been successful. This process will be augmented by integrating with the SHARE programme in Scotland. (3) The use of feature selection techniques will allow the identification of biological targets as disease biomarkers and/or therapeutic surrogate targets, this is in contrast to deep learning approaches whereby the discrimination is agnostic to biological structure or function.
Organisations
- University of Dundee (Lead Research Organisation)
- Engineering and Physical Sciences Research Council (Co-funder)
- University College London (Collaboration)
- British Society of Thoracic Imaging (BSTI) (Collaboration)
- IMPERIAL COLLEGE LONDON (Collaboration)
- UNIVERSITY OF CAMBRIDGE (Collaboration)
- UNIVERSITY OF EDINBURGH (Collaboration)
- UNIVERSITY OF OXFORD (Collaboration)
- ROYAL SURREY COUNTY HOSPITAL NHS FOUNDATION TRUST (Collaboration)
- Alan Turing Institute (Collaboration)
- EMBL European Bioinformatics Institute (EMBL - EBI) (Collaboration)
- UNIVERSITY OF BIRMINGHAM (Collaboration)
- SWANSEA UNIVERSITY (Collaboration)
- KING'S COLLEGE LONDON (Collaboration)
- Aidence B.V. (Project Partner)
Publications
Steele JD
(2019)
Pragmatic neuroscience for clinical psychiatry.
in The British journal of psychiatry : the journal of mental science
Shi L
(2021)
Identification of plasma proteins relating to brain neurodegeneration and vascular pathology in cognitively normal individuals.
in Alzheimer's & dementia (Amsterdam, Netherlands)
Reel P
(2021)
Using machine learning approaches for multi-omics data analysis: A review
in Biotechnology Advances
Mansouri-Benssassi E
(2023)
Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities.
in Heliyon
Kavianpour S
(2022)
Next-Generation Capabilities in Trusted Research Environments: Interview Study.
in Journal of medical Internet research
Jefferson E
(2019)
Computational Retinal Image Analysis
Jacob J
(2020)
Using imaging to combat a pandemic: rationale for developing the UK National COVID-19 Chest Imaging Database.
in The European respiratory journal
Description | Application of AI SDC in Scotland |
Geographic Reach | National |
Policy Influence Type | Contribution to new or improved professional practice |
Impact | Information governance and data teams managing access to patient data are now better informed regarding the potential additional risks posed by AI/ML. |
Description | D2EDPM CoE Interoperability |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Description | Digital Health Research and Policy in the UK and Switzerland |
Geographic Reach | Europe |
Policy Influence Type | Participation in a guidance/advisory committee |
Impact | Lessons from the Past, Plans for the Future - Invite from the British-Swiss ambassador and the UK Science and Innovation Network to discuss future collaborations between our countries. |
Description | Invited External Advisory Board Member EurOPDX (H2020 Project) |
Geographic Reach | Europe |
Policy Influence Type | Participation in a guidance/advisory committee |
Description | Invited Member of MRC Population Health Sciences Group (PHSG) |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Impact | Oversee population health sciences investment across MRC Boards and panels. Advise MRC Strategy Board, boards and panels on development and implementation of strategies and policies. Advise on strategic funding initiatives and partnership activities. Carry out gap analyses and horizon scanning. |
Description | Leading HDR UK short life working group for imaging data interoperability and integration |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Impact | Chairing a series of workshops with leading centres across the UK on behalf of HDR UK and working with Innovate UK. Developing a strategy for creating a UK-wide Imaging AI Ecosystem. |
Description | NIHR Imaging Group Setup |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Description | Scottish Imaging AI Steering Group |
Geographic Reach | National |
Policy Influence Type | Contribution to new or Improved professional practice |
Description | Translational Research Group Workshop: Enabling pathways for AI. |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Description | Alleviate: Hub for Pain |
Amount | £2,032,575 (GBP) |
Funding ID | MR/W014335/1 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 06/2021 |
End | 06/2024 |
Description | Building the Knowledge Graph for UK Healthcare Data Science |
Amount | £272,657 (GBP) |
Funding ID | 76000 |
Organisation | Health Data Research UK |
Sector | Private |
Country | United Kingdom |
Start | 02/2019 |
End | 11/2019 |
Description | Cambridge Mathematics of Information in Healthcare (CMIH) |
Amount | £1,275,504 (GBP) |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 05/2020 |
End | 06/2023 |
Description | Centre for Antimicrobial Resistance |
Amount | £2,253,124 (GBP) |
Organisation | National Institute for Health Research |
Sector | Public |
Country | United Kingdom |
Start | 09/2018 |
End | 10/2020 |
Description | Creating a national platform for powerful molecular studies of multiple conditions: the HDRUK multiomics consortium |
Amount | £1,088,605 (GBP) |
Organisation | Health Data Research UK |
Sector | Private |
Country | United Kingdom |
Start | 07/2020 |
End | 08/2022 |
Description | Data standardisation using the OMOP common data model |
Amount | £86,489 (GBP) |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 03/2020 |
End | 03/2021 |
Description | Defining & Redefining Disease Using Multimodal Data on a National Scale: the HDR UK Phenomics Resource |
Amount | £1,087,168 (GBP) |
Organisation | Health Data Research UK |
Sector | Private |
Country | United Kingdom |
Start | 03/2020 |
End | 04/2023 |
Description | Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMatter) |
Amount | £315,488 (GBP) |
Funding ID | MC_PC_21033 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 01/2022 |
End | 08/2022 |
Description | Multimorbidity and clinical guidelines: using epidemiology to quantify the applicability of trial evidence to inform guideline development |
Amount | £356,667 (GBP) |
Organisation | Chief Scientist Office |
Sector | Public |
Country | United Kingdom |
Start | 03/2019 |
End | 03/2022 |
Description | Programme for Government: Data, Statistics and Digital Identity Division |
Amount | £80,000 (GBP) |
Funding ID | THE IMAGING AI PROJECT |
Organisation | Government of Scotland |
Sector | Public |
Country | United Kingdom |
Start | 05/2019 |
End | 06/2020 |
Description | Semi-Automated Checking of Research Outputs (SACRO) |
Amount | £637,821 (GBP) |
Organisation | United Kingdom Research and Innovation |
Sector | Public |
Country | United Kingdom |
Start | 01/2023 |
End | 10/2023 |
Description | TRE-FX: Delivering a federated network of TREs to enable safe analytics |
Amount | £562,457 (GBP) |
Organisation | United Kingdom Research and Innovation |
Sector | Public |
Country | United Kingdom |
Start | 01/2023 |
End | 10/2023 |
Description | The Safe HavEn MetAdata (SHEMA) Project |
Amount | £20,000 (GBP) |
Organisation | Chief Scientist Office |
Sector | Public |
Country | United Kingdom |
Start | 03/2020 |
End | 03/2021 |
Description | Trusted Research Environment and Enclave for Hosting Open Original Science Exploration (TREEHOOSE) |
Amount | £202,664 (GBP) |
Funding ID | MC_PC_21032 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 01/2022 |
End | 08/2022 |
Title | Algorithm developed to derive labels from pixel data |
Description | A machine learning algorithm has been developed and trained on image data, to enable identification and labelling of images from pixel data. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2021 |
Provided To Others? | No |
Impact | Images can be classified and retrieved based on indicators within the pixel data itself, that cannot otherwise be derived through DICOM tags or clinicians notes in the Radiology Reports. The intention is to publish this tool and make it available to others once it has been fully validated and packaged. |
Title | Code Ingress mechanism |
Description | The automation of safe code ingress into a secure Trusted Research Environment (TRE), to replace and / or complement existing manual processes. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2021 |
Provided To Others? | No |
Impact | The solution enables automated scanning of incoming code for malicious content and quarantining of that code should risks be identified. This drastically speeds up the process, supporting software development inside a TRE. The intention is to publish this tool and make it available to others as part of the broader PICTURES software stack. |
Title | IsIdentifiable tools |
Description | This service evaluates 'data' for personally identifiable values (e.g. names). It can source data from a veriety of places (e.g. databases, file system). |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | This tool is designed to identify personally identifiable information in data from DICOM files, allowing manual verification and creation of 'rules' that facilitate automated redaction of PII. |
URL | https://github.com/SMI/SmiServices/blob/master/src/microservices/Microservices.IsIdentifiable/README... |
Title | Open source software to manage real-world clinical radiology data linked to other health data |
Description | Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of that data, link it to other clinical or social data sets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. We have released the software open source for other groups to use. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2019 |
Provided To Others? | Yes |
Impact | New service provide by National Service Scotland to support academics and industry to access to clinical imaging data at scale. |
URL | https://github.com/SMI |
Title | Project Private Zone (PPZ) |
Description | A Project Private Zone (PPZ) is an isolated project zone within the Safe Haven Services hosting environment dedicated to a single project. Multiple VMs for a project may be hosted in a PPZ, however shared Safe Haven resources such as parallel file systems, compute clusters, and managed R Server and JupyterHub services are not directly available in a PPZ. Project storage is zone-local by default and access to shared data assets hosted on shared storage platforms is indirect via data access gateways. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2021 |
Provided To Others? | No |
Impact | The primary objective of the PPZ design is effective management of unquantifiable risk associated with software stacks that have not been created by the Safe Haven systems admin team. The PPZ does not alter the vast majority of the standard Safe Haven environment security controls, however it does allow access to a small number of nominated external resources using a pull model through an authenticating http proxy server. |
Title | Scottish Medical Imaging |
Description | Radiological Images from Scottish Population |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | Yes |
Impact | THis will be available for many research groups to use. |
URL | https://github.com/SMI |
Title | Supporting data for "An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population" |
Description | To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish National population. This includes 30 million different radiological examinations from a population of 5.4 million and over 2 petabytes of data collected since 2010. Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of that data, link it to other clinical or social data sets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. An extensive software platform has been developed to host, extract and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. The data available is from a range of radiological modalities, scanner types and collected under different environmental conditions. This "real-world", heterogenous data is highly valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using Artificial Intelligence and Machine Learning technologies as well as enabling discovery science.
If you would like to access the SMI dataset for a research project, please contact eDRIS in the first instance. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | http://gigadb.org/dataset/100780 |
Description | British Society of Thoracic Imaging (BSTI) - NCCID |
Organisation | British Society of Thoracic Imaging (BSTI) |
Country | United Kingdom |
Sector | Charity/Non Profit |
PI Contribution | Collaborated on NCCID project, and then on the NCCID Validation Project |
Collaborator Contribution | Expertise in medical imaging and AI |
Impact | New funding for NCCID validation project and also creation of a UK wide database of images from COVID patients and controls for research and innovation. |
Start Year | 2020 |
Description | HDR UK Multiomics - pan UK |
Organisation | Alan Turing Institute |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Bringing expertise in Safe Havens and clinical data management to the collaboration. |
Collaborator Contribution | Bringing expertise in management of other omic data. |
Impact | A new awarded grant to HDR UK for a pan uk project: Creating a national platform for molecular studies of multiple conditions: HDRUK multiomics consortium |
Start Year | 2019 |
Description | HDR UK Multiomics - pan UK |
Organisation | EMBL European Bioinformatics Institute (EMBL - EBI) |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Bringing expertise in Safe Havens and clinical data management to the collaboration. |
Collaborator Contribution | Bringing expertise in management of other omic data. |
Impact | A new awarded grant to HDR UK for a pan uk project: Creating a national platform for molecular studies of multiple conditions: HDRUK multiomics consortium |
Start Year | 2019 |
Description | HDR UK Multiomics - pan UK |
Organisation | Imperial College London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Bringing expertise in Safe Havens and clinical data management to the collaboration. |
Collaborator Contribution | Bringing expertise in management of other omic data. |
Impact | A new awarded grant to HDR UK for a pan uk project: Creating a national platform for molecular studies of multiple conditions: HDRUK multiomics consortium |
Start Year | 2019 |
Description | HDR UK Multiomics - pan UK |
Organisation | Swansea University |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Bringing expertise in Safe Havens and clinical data management to the collaboration. |
Collaborator Contribution | Bringing expertise in management of other omic data. |
Impact | A new awarded grant to HDR UK for a pan uk project: Creating a national platform for molecular studies of multiple conditions: HDRUK multiomics consortium |
Start Year | 2019 |
Description | HDR UK Multiomics - pan UK |
Organisation | University College London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Bringing expertise in Safe Havens and clinical data management to the collaboration. |
Collaborator Contribution | Bringing expertise in management of other omic data. |
Impact | A new awarded grant to HDR UK for a pan uk project: Creating a national platform for molecular studies of multiple conditions: HDRUK multiomics consortium |
Start Year | 2019 |
Description | HDR UK Multiomics - pan UK |
Organisation | University of Cambridge |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Bringing expertise in Safe Havens and clinical data management to the collaboration. |
Collaborator Contribution | Bringing expertise in management of other omic data. |
Impact | A new awarded grant to HDR UK for a pan uk project: Creating a national platform for molecular studies of multiple conditions: HDRUK multiomics consortium |
Start Year | 2019 |
Description | HDR UK Multiomics - pan UK |
Organisation | University of Edinburgh |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Bringing expertise in Safe Havens and clinical data management to the collaboration. |
Collaborator Contribution | Bringing expertise in management of other omic data. |
Impact | A new awarded grant to HDR UK for a pan uk project: Creating a national platform for molecular studies of multiple conditions: HDRUK multiomics consortium |
Start Year | 2019 |
Description | HDR UK Phenotype Portal - UK wide project |
Organisation | EMBL European Bioinformatics Institute (EMBL - EBI) |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | New collaboration between HDR UK groups across the UK to develop a Phenotype portal. Workstream lead for the web portal. |
Collaborator Contribution | The collaborators bring their expertise in phenotypes and data science to a pan UK project |
Impact | A collaborative grant to HDR UK which was funded |
Start Year | 2019 |
Description | HDR UK Phenotype Portal - UK wide project |
Organisation | King's College London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | New collaboration between HDR UK groups across the UK to develop a Phenotype portal. Workstream lead for the web portal. |
Collaborator Contribution | The collaborators bring their expertise in phenotypes and data science to a pan UK project |
Impact | A collaborative grant to HDR UK which was funded |
Start Year | 2019 |
Description | HDR UK Phenotype Portal - UK wide project |
Organisation | Swansea University |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | New collaboration between HDR UK groups across the UK to develop a Phenotype portal. Workstream lead for the web portal. |
Collaborator Contribution | The collaborators bring their expertise in phenotypes and data science to a pan UK project |
Impact | A collaborative grant to HDR UK which was funded |
Start Year | 2019 |
Description | HDR UK Phenotype Portal - UK wide project |
Organisation | University College London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | New collaboration between HDR UK groups across the UK to develop a Phenotype portal. Workstream lead for the web portal. |
Collaborator Contribution | The collaborators bring their expertise in phenotypes and data science to a pan UK project |
Impact | A collaborative grant to HDR UK which was funded |
Start Year | 2019 |
Description | HDR UK Phenotype Portal - UK wide project |
Organisation | University of Birmingham |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | New collaboration between HDR UK groups across the UK to develop a Phenotype portal. Workstream lead for the web portal. |
Collaborator Contribution | The collaborators bring their expertise in phenotypes and data science to a pan UK project |
Impact | A collaborative grant to HDR UK which was funded |
Start Year | 2019 |
Description | HDR UK Phenotype Portal - UK wide project |
Organisation | University of Cambridge |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | New collaboration between HDR UK groups across the UK to develop a Phenotype portal. Workstream lead for the web portal. |
Collaborator Contribution | The collaborators bring their expertise in phenotypes and data science to a pan UK project |
Impact | A collaborative grant to HDR UK which was funded |
Start Year | 2019 |
Description | HDR UK Phenotype Portal - UK wide project |
Organisation | University of Edinburgh |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | New collaboration between HDR UK groups across the UK to develop a Phenotype portal. Workstream lead for the web portal. |
Collaborator Contribution | The collaborators bring their expertise in phenotypes and data science to a pan UK project |
Impact | A collaborative grant to HDR UK which was funded |
Start Year | 2019 |
Description | HDR UK Phenotype Portal - UK wide project |
Organisation | University of Oxford |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | New collaboration between HDR UK groups across the UK to develop a Phenotype portal. Workstream lead for the web portal. |
Collaborator Contribution | The collaborators bring their expertise in phenotypes and data science to a pan UK project |
Impact | A collaborative grant to HDR UK which was funded |
Start Year | 2019 |
Description | Imaging Collaboration with Cambridge |
Organisation | University of Cambridge |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Bringing expertise in software development |
Collaborator Contribution | Expertise in AI using clinical data |
Impact | EPSRC awarded grant for us to collaborate further |
Start Year | 2019 |
Description | Royal Surrey NHS Foundation Trust |
Organisation | Royal Surrey County Hospital NHS Foundation Trust |
Country | United Kingdom |
Sector | Public |
PI Contribution | Collaborated on building the National COVID-19 Chest Imaging Database (NCCID). This collaboration was highly successful and we then went on to collaborate on the National COVID-19 Chest Imaging Database (NCCID) Validation project |
Collaborator Contribution | Expertise in image data handling and infrastructures |
Impact | New funding for NCCID Validation project. A UK wide COVID imaging dataset for research and innovation. Talked about to the G7. |
Start Year | 2020 |
Title | Imaging RDMP |
Description | An architecture and platform to securely extract a copy of that data, link it to other clinical or social data sets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. |
Type Of Technology | Software |
Year Produced | 2020 |
Open Source License? | Yes |
Impact | New NHS service which can securely provision clinical imaging data for research and innovation |
Title | Research Data Management Platform (RDMP) |
Description | Software platform for managing longitudinal cohorts of research data and clinical record. Secure extraction of cohorts, audit and support for reproduciblity. |
Type Of Technology | Software |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | Other Safe Havens adopting the system |
URL | https://www.youtube.com/watch?v=Fgi9-Sdup-Y |
Company Name | Eye To The Future |
Description | Eye To The Future develops software that aims to improve the speed in which ophthalmologists and optometrists review patient retinal records. |
Year Established | 2021 |
Impact | None yet. |
Description | 2021 Statistical Data Confidentiality Expert Meeting |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Prof. Felix Ritchie presented a talk titled "Statistical disclosure controls for machine learning models" at the 2021 Statistical Data Confidentiality Expert Meeting |
Year(s) Of Engagement Activity | 2021 |
URL | https://uwe-repository.worktribe.com/output/8067227 |
Description | AI Summit London: An Intelligent Future for Medical Imaging Panel Discussion |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Expert Panel Discussion- An Intelligent Future for Medical Imaging |
Year(s) Of Engagement Activity | 2021 |
Description | Cambridge Spark Lecture Series |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Presentation: Overcoming the Challenges of Providing Access to Population Scale, Routinely Collected Health and Imaging Data for AI Development whilst Protecting Patient Confidentiality. |
Year(s) Of Engagement Activity | 2022 |
Description | Data Saves Lives: The Fight Against Covid-19 |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | . |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.youtube.com/watch?v=CKuuY1LNv-0 |
Description | Discovery Day Lecture: Data saves lives: the fight against COVID-19 |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | A lecture for the general public about our research. |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.youtube.com/watch?v=CKuuY1LNv-0 |
Description | Grand Rounds Seminar Series |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Professional Practitioners |
Results and Impact | Scaling Health Informatics: large scale recruitment, BIG data and analytics. |
Year(s) Of Engagement Activity | 2019 |
Description | HDR UK Event - Scotland's Data |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Scotland's Data |
Year(s) Of Engagement Activity | 2019 |
Description | HIC Twitter Feed |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Promotion of the research and services of the Health Informatics Centre. Promoting the secure anonymised access to clinical data for research. |
Year(s) Of Engagement Activity | 2019,2020 |
URL | https://twitter.com/dataonamission |
Description | HIC Website |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Health Informatics Centre Website. Showing projects supported and safe use of clinical data for research. |
Year(s) Of Engagement Activity | 2015,2016,2017,2018,2019,2020 |
Description | Input into HDR UK Data Standards Paper |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Input into HDR UK Data Standards Paper |
Year(s) Of Engagement Activity | 2020 |
Description | Input into HDR UK Trusted Research Environments Green Paper |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Input into HDR UK Trusted Research Environments Green Paper. |
Year(s) Of Engagement Activity | 2020 |
URL | https://ukhealthdata.org/wp-content/uploads/2020/07/200723-Alliance-Board_Paper-E_TRE-Green-Paper.pd... |
Description | Invited External Advisory Board Member |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | European Consortium for Research on Patient-derived xenografts, EurOPDX (www.europdx.eu). July 2019 - Present |
Year(s) Of Engagement Activity | 2017,2019,2020 |
Description | Invited Member of MRC Population Health Sciences Group (PHSG) |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Policymakers/politicians |
Results and Impact | Oversee population health sciences investment across MRC Boards and panels. Advise MRC Strategy Board, boards and panels on development and implementation of strategies and policies. Advise on strategic funding initiatives and partnership activities. Carry out gap analyses and horizon scanning. |
Year(s) Of Engagement Activity | 2020 |
Description | Invited Speaker HDR UK Data Federation Initiatives, |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | UK Health Data Research Alliance Symposium |
Year(s) Of Engagement Activity | 2020 |
Description | Invited Speaker: BHF Data Science Centre - Imaging Data Workshop |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Invited Speaker: Curating NHS imaging cohorts within a trusted research environment |
Year(s) Of Engagement Activity | 2021 |
Description | Invited Speaker: British College of Radiologists Conference: Artificial intelligence in radiology. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Invited talk: What can we do to enable scalable, routine access to large volumes of imaging data to support the training of AI for use in clinical care? Promote the PICTURES work and Imaging research to the international community. |
Year(s) Of Engagement Activity | 2020 |
URL | https://www.mybir.org.uk/CPBase__event_detail?id=a173Y00000CwkXhQAJ&site=a0N2000000COvFsEAL |
Description | Invited Speaker: Data on a Mission - From Descriptive to Predictive Analytics using Health Data. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Other audiences |
Results and Impact | Edinburgh University |
Year(s) Of Engagement Activity | 2019 |
Description | Invited Speaker: Developing an AI Imaging Ecosystem. Refreshing Radiology in the North |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Other audiences |
Results and Impact | Conference Talk |
Year(s) Of Engagement Activity | 2020 |
Description | Invited Speaker: Enabling international research access to highly heterogeneous, routinely collected, linked clinical images at scale. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | University of Aberdeen |
Year(s) Of Engagement Activity | 2019 |
Description | Invited Speaker: Exciting developments of HIC's Next Generation Safe Haven and new COVID research initiatives |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | Public Health Scotland Research Talks |
Year(s) Of Engagement Activity | 2020 |
Description | Invited Speaker: Health Data Science at Scale. Moving from Descriptive to Predictive Analytics |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Cambridge University Seminar Series |
Year(s) Of Engagement Activity | 2019 |
Description | Invited Speaker: How can we make the UK leading in AI R&D using real world clinical data? |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | HDR UK Research Day |
Year(s) Of Engagement Activity | 2019 |
Description | Invited Speaker: NIHR Imaging Group |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | NIHR Imaging group raising awareness |
Year(s) Of Engagement Activity | 2019 |
Description | Invited Speaker: SINPASE ASM. Enabling scalable research access to heterogeneous, routinely collected, linked clinical images for the Scottish Population. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | SINPASE ASM |
Year(s) Of Engagement Activity | 2019,2020 |
URL | http://www.sinapse.ac.uk/ |
Description | Invited Speaker: What can we do to enable scalable, routine access to large volumes of imaging data to support the training of AI for use in clinical care? |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | British College of Radiologists Conference: Artificial intelligence in radiology |
Year(s) Of Engagement Activity | 2020 |
Description | Invited Workshop lead: HDR UK - What does a world leading research infrastructure look like? |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Workshop on "What does a world leading research infrastructure look like? " |
Year(s) Of Engagement Activity | 2019 |
Description | Invited seminar at the University of Edinburgh |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | Talk on "Health Data Science at Scale: Moving from Descriptive to Predictive Analytics" |
Year(s) Of Engagement Activity | 2019 |
Description | Invited speaker - Developing an AI Imaging Ecosystem. Refreshing Radiology in the North |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | Talk on Developing an AI Imaging Ecosystem. Refreshing Radiology in the North (Scotland) |
Year(s) Of Engagement Activity | 2020 |
Description | Invited talks, panels and meetings around Tokyo/Yokohama for a week: Medical Imaging and AI programme, Japan. |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Week long tour in Japan organised by Scottish Development International |
Year(s) Of Engagement Activity | 2019 |
Description | Invited to give seminar at Cambridge University |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Talk on "Health Data Science at Scale. Moving from Descriptive to Predictive Analytics" |
Year(s) Of Engagement Activity | 2019 |
Description | Invited to present to researchers at Queens University Belfast |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Other audiences |
Results and Impact | Promote the outputs of the PICTURES programme and HDR UK infrastructure to support research using routinely collected data. Talk was "Data on a Mission". |
Year(s) Of Engagement Activity | 2020 |
Description | Leading HDR UK Workshop sessions |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | HDR UK - What does a world leading research infrastructure look like? June 2019. |
Year(s) Of Engagement Activity | 2019 |
Description | Leading HDR UK short life working group for imaging data interoperability |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Chairing a series of workshops with leading centres across the UK on behalf of HDR UK around developing an AI imaging ecosystem. |
Year(s) Of Engagement Activity | 2020 |
Description | MRC Policy Input |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | Translational Research Group Workshop: Enabling pathways for AI. UKRI. July 2019. |
Year(s) Of Engagement Activity | 2019 |
Description | NIHR Imaging Science Delivery Group |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | . |
Year(s) Of Engagement Activity | 2021 |
Description | Overcoming the Challenges of Providing Access to Population Scale, Routinely Collected Health and Imaging Data for AI Development whilst Protecting Patient Confidentiality |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Presentation to the AI summit in London covering the work of PICTURES, CO-CONNECT and GRAIMATTER |
Year(s) Of Engagement Activity | 2022 |
Description | PICTURES Twitter Feed |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Set-up to promote the work of the PICTURES Programme |
Year(s) Of Engagement Activity | 2019,2020 |
URL | https://twitter.com/imageonamission |
Description | PICTURES Website |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | A website about the PICTURES project, including a video aimed at the general public. Communicates what the programme is all about and how such a programme makes a difference to clinical care. |
Year(s) Of Engagement Activity | 2020 |
URL | https://www.imageonamission.ac.uk/ |
Description | PICTURES study to create new platform to help tackle major health issues |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | A £4.4 million project led by the University of Dundee aims to turn a database of millions of clinical images into a powerful research tool to help tackle health conditions including lung cancer and dementia. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.dundee.ac.uk/stories/pictures-study-create-new-platform-help-tackle-major-health-issues |
Description | Pfizer Lunch and Learn |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Keynote Speaker: Whistle-stop tour of the Data for Research and Landscape in the UK |
Year(s) Of Engagement Activity | 2021 |
Description | Public video explaining PICTURES |
Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | A video explaining PICTURES designed for the general public |
Year(s) Of Engagement Activity | 2020 |
URL | https://www.imageonamission.ac.uk/ |
Description | Research Data Scotland Transition Board |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | . |
Year(s) Of Engagement Activity | 2021 |
Description | SINAPSE Annual Scientific Meeting 2021 |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Lead researcher on the PICTURES Exemplar 2 project presented a lighting talk and poster at the 2011 SINAPSE meeting, sharing preliminary results of analysis on the image data provided through this programme. |
Year(s) Of Engagement Activity | 2018,2019,2020,2021 |
URL | http://www.sinapse.ac.uk/member-area/2021-asm-summary-with-links |
Description | Second TV interview covering PICTURES Programme launch |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Interviewed by a regional TV channel covering the PICTURES project. This was then broadcast regionally. |
Year(s) Of Engagement Activity | 2019 |
Description | TV interview covering the launch of the PICTURES Programme |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Media (as a channel to the public) |
Results and Impact | Interview by a Scottish TV company covering the PICTURES launch. |
Year(s) Of Engagement Activity | 2019 |
Description | Talk at Scotland HDR UK Conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Talk on "How can we make the UK leading in AI R&D using real world clinical data?" Edinburgh. HDR UK Scotland Research Day |
Year(s) Of Engagement Activity | 2019 |
Description | Talk to Edinburgh University |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | Talk on Enabling research access to heterogeneous, routinely collected, linked clinical images at scale |
Year(s) Of Engagement Activity | 2019 |
Description | The AI Summit London (panel) |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Expert panel speaker: The Landscape of AI Adoption in Medical Imaging - Challenges & Opportunities. |
Year(s) Of Engagement Activity | 2022 |
URL | https://london.theaisummit.com/ |
Description | UK Cross Sector Reference Data Frameworks group |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | . |
Year(s) Of Engagement Activity | 2021 |