Developing synergies between transient astronomy and early medical intervention
Lead Research Organisation:
University of Portsmouth
Department Name: Institute of Cosmology and Gravitation
Abstract
The application of artificial intelligence (AI) to healthcare will revolutionise medicine and patient care over the coming decades. The wealth of data being collected by medical professionals keeps growing and they need significant help to quickly understand this information for their key diagnostic tests. In particular, can this wealth of digital data be used to automatically detect possible problems early, and thus alert doctors for closer inspection?
Astronomy is also experiencing a data revolution driven by STFC experiments like LIGO and LSST. These experiments are opening up the time domain in astronomy and in the near-future astronomers will be deluged by millions of transient events a day; LSST will produce on average hundreds of transients per second! The prioritisation of so many events will be key to our success and astrophysicists are already developing and applying AI technology to help find the most interesting events within the ocean of "ordinary" transients.
This project brings these two problems together by applying the knowledge and expertise developed in transient astronomy to the early automated diagnosis of medical problems. This project is at the forefront of interdisciplinary research bring world-leading cardiologists from King's College Hospital together with the best transient astrophysicists in the UK. Together, we hope to find early diagnostic signatures in detailed time-series patient data e.g. ECGs. This pilot activity will be used to prime-pump future investigations and grow the number of scientists engaged in such impactful research.
Astronomy is also experiencing a data revolution driven by STFC experiments like LIGO and LSST. These experiments are opening up the time domain in astronomy and in the near-future astronomers will be deluged by millions of transient events a day; LSST will produce on average hundreds of transients per second! The prioritisation of so many events will be key to our success and astrophysicists are already developing and applying AI technology to help find the most interesting events within the ocean of "ordinary" transients.
This project brings these two problems together by applying the knowledge and expertise developed in transient astronomy to the early automated diagnosis of medical problems. This project is at the forefront of interdisciplinary research bring world-leading cardiologists from King's College Hospital together with the best transient astrophysicists in the UK. Together, we hope to find early diagnostic signatures in detailed time-series patient data e.g. ECGs. This pilot activity will be used to prime-pump future investigations and grow the number of scientists engaged in such impactful research.
Planned Impact
This proposal will provide a range of impacts:
1. Development of interdisciplinary research on two cardiology data science projects to create early AI diagnostic tests for cardiac arrest. While still at an early stage, we would hope the outcomes of these projects would inform and influence long-term healthcare providing quicker alerts for doctors to make time critical decisions. Such societal impact is at the heart of the UK Industrial Strategy which seeks to put the UK at the forefront of AI in healthcare.
2. We will engage with a larger range of scientists, doctors and industrialists across the southeast to grow research and innovation capacity for precision healthcare and early diagnosis. These innovators will then be encouraged to explore their ideas through two dedicated hackathons provide rapid prototyping of possible solutions. These solutions will form the basis for future proposals and investment, gain both societal and commercial impact.
1. Development of interdisciplinary research on two cardiology data science projects to create early AI diagnostic tests for cardiac arrest. While still at an early stage, we would hope the outcomes of these projects would inform and influence long-term healthcare providing quicker alerts for doctors to make time critical decisions. Such societal impact is at the heart of the UK Industrial Strategy which seeks to put the UK at the forefront of AI in healthcare.
2. We will engage with a larger range of scientists, doctors and industrialists across the southeast to grow research and innovation capacity for precision healthcare and early diagnosis. These innovators will then be encouraged to explore their ideas through two dedicated hackathons provide rapid prototyping of possible solutions. These solutions will form the basis for future proposals and investment, gain both societal and commercial impact.
Publications
ANITA Collaboration ANITA
(2020)
Unusual Near-horizon Cosmic-ray-like Events Observed by ANITA-IV
in arXiv e-prints
Bay Daya
(2020)
Improved Constraints on Sterile Neutrino Mixing from Disappearance Searches in the MINOS, MINOS+, Daya Bay, and Bugey-3 Experiments
in arXiv e-prints
Brout D.
(2020)
VizieR Online Data Catalog: The first 3yrs of DES-SN (DES-SN3YR) (Brout+, 2019)
in VizieR Online Data Catalog
Collaboration D
(2020)
Supernova Neutrino Burst Detection with the Deep Underground Neutrino Experiment
in arXiv e-prints
Collaboration E
(2022)
Euclid preparation. XVIII. The NISP photometric system
in arXiv e-prints
Collaboration N
(2020)
Search for Slow Magnetic Monopoles with the NOvA Detector on the Surface
in arXiv e-prints
Collaboration N
(2020)
Search for multi-messenger signals in NOvA coincident with LIGO/Virgo detections
in arXiv e-prints
Collaboration N
(2020)
Adjusting Neutrino Interaction Models and Evaluating Uncertainties using NOvA Near Detector Data
in arXiv e-prints
Deaconu C.
(2020)
A search for ultrahigh-energy neutrinos associated with astrophysical sources using the third flight of ANITA
in arXiv e-prints
DeĀ Jaeger T
(2020)
Studying Type II supernovae as cosmological standard candles using the Dark Energy Survey
in Monthly Notices of the Royal Astronomical Society
Description | Our use of astronomy algorithms and techniques can help advance understanding and interpretation of medical data through collaboration with doctors and clinical staff. Their expertise is vital in the design of any advanced techniques while astronomers provide the know-how to make it work. |
Exploitation Route | We are preparing publications in medical jorunals on our collaboration. |
Sectors | Healthcare |
URL | https://www.ukri.org/news/star-spotting-science-used-to-spot-cancerous-cells/ |
Description | https://www.ukri.org/news/star-spotting-science-used-to-spot-cancerous-cells/ |
First Year Of Impact | 2020 |
Sector | Healthcare |
Impact Types | Societal |
Title | KOCAR Dataset |
Description | We have a high quality and cleaned dataset of around 400 patients who have suffered Out of Hospital Cardiac Arrest. This database has formed the foundation of the research we have conducted as part of this award. |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | No |
Impact | This dataset has resulted in the KOCAR web tool (described in another researchfish section), and also a publication which has been submitted for review. |
Description | King's College Hospital Collaboration |
Organisation | King's College Hospital |
Country | United Kingdom |
Sector | Hospitals |
PI Contribution | We have used statistical analysis methods commonly used in astronomy to analysis the patient data provided by King's College Hospital. We have worked closely with the Department of Cardiology to advise on data analysis and collection techniques to facilitate future collaboration. |
Collaborator Contribution | Our collaborators have provided data to our group for research and also co-supervise placement students. |
Impact | We have submitted a paper to a cardiology journal that explores a decision-tree based method for the classification of a culprit coronary lesion in cardiac arrest patients. |
Start Year | 2017 |
Description | Kings College Hospital Trust |
Organisation | King's College Hospital NHS Foundation Trust (NCH) |
Department | Cardiology |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Expertise and skills in data science based on astronomical data analysis techniques |
Collaborator Contribution | Access to unique cardiology data including clinical expertise in interpreting any data analysis results |
Impact | results still being analysed but looking profitable to find better treatments especially for emergency medicine |
Start Year | 2018 |
Description | Molegazer |
Organisation | Oxford University Hospitals NHS Foundation Trust |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Arising from a Cancer ResearchUK sandpit event the Molegazer collaboration aims to develop early detection of melanoma from time-series analyses of naevi evolution using techniques developed in super nova astronomy |
Collaborator Contribution | The School of Astronomy and Physics at Southampton are collaborators with us on Super Novae research. Oxford University Hospital Trust are the problem owners and have provided images for initial analysis as well as clinical knowledge. |
Impact | Proposals to Welcome Trust and CRUK both unsuccessful |
Start Year | 2019 |
Description | Molegazer |
Organisation | University of Southampton |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Arising from a Cancer ResearchUK sandpit event the Molegazer collaboration aims to develop early detection of melanoma from time-series analyses of naevi evolution using techniques developed in super nova astronomy |
Collaborator Contribution | The School of Astronomy and Physics at Southampton are collaborators with us on Super Novae research. Oxford University Hospital Trust are the problem owners and have provided images for initial analysis as well as clinical knowledge. |
Impact | Proposals to Welcome Trust and CRUK both unsuccessful |
Start Year | 2019 |
Title | KOCAR Web App |
Description | The KOCAR web tool provides an easy method of inputting data into the decision tree-based model we developed as part of this award. The web app asks the user a series of Yes/No questions to determine the probability of a patient having a culprit coronary lesion. |
Type Of Technology | Webtool/Application |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | The web app is currently used retrospectively until the paper is published and the tool approved. It has the potential to guide patient treatment. |
URL | https://kocar-250409.appspot.com/ |