Fully automated system for the analysis of the efficacy of knee replacement surgery
Lead Research Organisation:
University of Manchester
Department Name: School of Health Sciences
Abstract
Musculoskeletal (MSK) diseases affect about 16% of adults and more than 30% in the age group 65+. As the incidence of most MSK diseases increases with age, the socio-economic impact of MSK diseases is increasing steadily in countries with an ageing population such as the UK. During 2014-2016, more than 260,000 primary knee replacement surgery (KRS) procedures were performed in England, Wales and Northern Ireland. KRS may, in the short or long run, lead to complications that will require a revision KRS and may result in life-threatening conditions (e.g., caused by a loose implant). Currently, the risk of KRS failure is 11% ten years post-operatively. When a KRS fails it is important to schedule a revision KRS to remove a loose or damaged implant before irreversible harm is done to the knee joint.
There are an increasing number of medical images being gathered in all UK NHS hospitals, with the growing need and opportunity to utilise this information to improve the health of the nation. Currently, medical images are underutilised in clinical practise and research into MSK diseases where 2D radiographs are the imaging technique of choice due to wide availability, speed of acquisition and low cost.
The aim of this project is to develop an automated software system to study the effectiveness of KRS. The system will automatically locate the knee joint in radiographs and analyse the radiographic shape and appearance of the knee bones or implant. It will then combine the obtained image-based data with clinical data aiming to predict and identify signs of early joint failure. The goal is to transform clinically collected image data into useful medical information to benefit healthcare at individual and societal levels.
Various clinical placements will be undertaken to identify clinical needs and analyse the clinical workflow in KRS decision-making. This involves finding answers to questions such as how can the current workflows be revised to fit a new software system. Working closely with the Connected Health Cities project will provide details on how to technically implement such a system into the existing digital healthcare infrastructure. The collected information will be used to inform a suitable design (e.g., graphical user interface) and implementation strategy for the system to be integrated in the clinical setting. The software system will be validated at various stages of the development cycle to ensure that it fits the purpose. The usability and acceptability of the system will be evaluated during a pilot trial towards the end of the project.
This research will result in a computer-aided system to inform the KRS decision-making process and the resulting medical management, improving the quality of care in clinical practice.
In the long-term, this project aims to improve the health of the general population through a greater understanding of early joint failure in KRS, and by providing a computer-aided system for identifying and monitoring the latter in clinical practice. This work will have a positive impact on: (i) patients, who will benefit from better treatment choice and management, leading to improved quality of care; (ii) clinicians, who will have access to additional data to make an informed decision and who will save time by using an automated system to generate such data; (iii) the healthcare system, which will benefit from cost reductions; and (iv) the wider research community, where newly developed imaging and data modelling methods will contribute to advancements in other areas of automated image analysis as well as large scale data analysis.
There are an increasing number of medical images being gathered in all UK NHS hospitals, with the growing need and opportunity to utilise this information to improve the health of the nation. Currently, medical images are underutilised in clinical practise and research into MSK diseases where 2D radiographs are the imaging technique of choice due to wide availability, speed of acquisition and low cost.
The aim of this project is to develop an automated software system to study the effectiveness of KRS. The system will automatically locate the knee joint in radiographs and analyse the radiographic shape and appearance of the knee bones or implant. It will then combine the obtained image-based data with clinical data aiming to predict and identify signs of early joint failure. The goal is to transform clinically collected image data into useful medical information to benefit healthcare at individual and societal levels.
Various clinical placements will be undertaken to identify clinical needs and analyse the clinical workflow in KRS decision-making. This involves finding answers to questions such as how can the current workflows be revised to fit a new software system. Working closely with the Connected Health Cities project will provide details on how to technically implement such a system into the existing digital healthcare infrastructure. The collected information will be used to inform a suitable design (e.g., graphical user interface) and implementation strategy for the system to be integrated in the clinical setting. The software system will be validated at various stages of the development cycle to ensure that it fits the purpose. The usability and acceptability of the system will be evaluated during a pilot trial towards the end of the project.
This research will result in a computer-aided system to inform the KRS decision-making process and the resulting medical management, improving the quality of care in clinical practice.
In the long-term, this project aims to improve the health of the general population through a greater understanding of early joint failure in KRS, and by providing a computer-aided system for identifying and monitoring the latter in clinical practice. This work will have a positive impact on: (i) patients, who will benefit from better treatment choice and management, leading to improved quality of care; (ii) clinicians, who will have access to additional data to make an informed decision and who will save time by using an automated system to generate such data; (iii) the healthcare system, which will benefit from cost reductions; and (iv) the wider research community, where newly developed imaging and data modelling methods will contribute to advancements in other areas of automated image analysis as well as large scale data analysis.
Technical Summary
This project aims to develop a computer-aided system to analyse radiographic signs of early joint failure in knee replacement surgery (KRS).
The software system to be developed will be based on the BoneFinderTM-technology (www.bone-finder.com) which uses machine-learning methods to automatically locate skeletal structures in radiographs. Based on a set of training data consisting of radiographs and feature point annotations, BoneFinderTM uses Random Forest-based shape model matching methods to locate the corresponding feature points in unseen images. BoneFinderTM has been applied to automatically outline knee joints in radiographs but further methodological development is required to account for disease and to incorporate implant detection.
For a given image, the automatically identified feature points will be used to quantify the knee bones and implant fitting. This will include conventional geometric measurements of lengths and angles but these only give a sparse representation of the structures. Statistical Shape and Appearance Models will be used to capture and quantify their overall shape and texture. Using retrospective data, machine-learning methods will be applied to combine the derived radiographic shape and appearance information of the knee joint with collected clinical data to analyse signs of early joint failure. This will lead to a computer-aided system that will have learned to identify radiographic indications of early joint failure based on radiographs and clinical data for any new subject, informing the KRS decision-making process.
A feasibility study will be conducted to identify the clinical requirements for usability and acceptability, and for the integration of the system into the clinical workflow. This will also include the identification of a suitable implementation strategy of the system into the existing digital healthcare infrastructure. The system will be validated during a pilot trial.
The software system to be developed will be based on the BoneFinderTM-technology (www.bone-finder.com) which uses machine-learning methods to automatically locate skeletal structures in radiographs. Based on a set of training data consisting of radiographs and feature point annotations, BoneFinderTM uses Random Forest-based shape model matching methods to locate the corresponding feature points in unseen images. BoneFinderTM has been applied to automatically outline knee joints in radiographs but further methodological development is required to account for disease and to incorporate implant detection.
For a given image, the automatically identified feature points will be used to quantify the knee bones and implant fitting. This will include conventional geometric measurements of lengths and angles but these only give a sparse representation of the structures. Statistical Shape and Appearance Models will be used to capture and quantify their overall shape and texture. Using retrospective data, machine-learning methods will be applied to combine the derived radiographic shape and appearance information of the knee joint with collected clinical data to analyse signs of early joint failure. This will lead to a computer-aided system that will have learned to identify radiographic indications of early joint failure based on radiographs and clinical data for any new subject, informing the KRS decision-making process.
A feasibility study will be conducted to identify the clinical requirements for usability and acceptability, and for the integration of the system into the clinical workflow. This will also include the identification of a suitable implementation strategy of the system into the existing digital healthcare infrastructure. The system will be validated during a pilot trial.
Organisations
- University of Manchester (Fellow, Lead Research Organisation)
- STOCKPORT NHS FOUNDATION TRUST (Collaboration)
- SRM Dental College (Collaboration)
- Erasmus MC (Collaboration)
- ALDER HEY CHILDREN'S NHS FOUNDATION TRUST (Collaboration)
- University Medical Center Utrecht (UMC) (Collaboration)
- SALFORD ROYAL NHS FOUNDATION TRUST (Collaboration)
- University of Bristol (Collaboration)
Publications
Frysz M
(2022)
ACETABULAR DYSPLASIA AND CAM MORPHOLOGY ARE FEATURES OF SEVERE HIP OSTEOARTHRITIS: FINDINGS FROM A CROSS-SECTIONAL STUDY IN UK BIOBANK
in Osteoarthritis and Cartilage
Van Buuren MMA
(2022)
The association between statistical shape modeling-defined hip morphology and features of early hip osteoarthritis in young adult football players: Data from the femoroacetabular impingement and hip osteoarthritis cohort (FORCe) study.
in Osteoarthritis and cartilage open
Faber BG
(2022)
A novel semi-automated classifier of hip osteoarthritis on DXA images shows expected relationships with clinical outcomes in UK Biobank.
in Rheumatology (Oxford, England)
Faber B
(2021)
Deriving alpha angle from anterior-posterior dual-energy x-ray absorptiometry scans: an automated and validated approach
in Wellcome Open Research
Faber BG
(2021)
Deriving alpha angle from anterior-posterior dual-energy x-ray absorptiometry scans: an automated and validated approach.
in Wellcome open research
Description | An Automated System for Measuring Hip Dysplasia in Children with Cerebral Palsy |
Amount | £148,797 (GBP) |
Funding ID | AI_AWARD02268 |
Organisation | National Institute for Health Research |
Sector | Public |
Country | United Kingdom |
Start | 11/2021 |
End | 10/2022 |
Description | Sir Henry Dale Fellowship |
Amount | £965,000 (GBP) |
Funding ID | 223267/Z/21/Z |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 09/2021 |
End | 12/2027 |
Description | iTPA Access to Expertise Award (A2E) on Establishing a data sharing platform between clinicians and researchers for studying children's hip diseases |
Amount | £25,000 (GBP) |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 09/2020 |
End | 05/2021 |
Description | iTPA Flexible Fund Award on Development of an interactive translational roadmap web-tool to guide health technology research and innovation |
Amount | £12,000 (GBP) |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 12/2022 |
End | 06/2023 |
Description | iTPA Projects for Translation Award (P4T) on An Automated Software System for Monitoring Developmental Dysplasia of the Hip in Children |
Amount | £36,000 (GBP) |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 09/2020 |
End | 10/2021 |
Description | AUGMENT study (Universities of Bristol, Southampton, Aberdeen, Cardiff and Queensland) |
Organisation | University of Bristol |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | We are developing methods and software to automatically analyse the hips and knees from DXA images. |
Collaborator Contribution | Partners provide data and clinical expertise/skills, and study the role of bone and joint size and shape in explaining common musculoskeletal diseases. |
Impact | This collaboration has led to a Wellcome Trust Collaborative Award. Multiple peer-reviewed journal papers (e.g. in Rheumatology, Osteoarthritis and Cartilage, Bone) and peer-reviewed abstract presentations (e.g. at British Society of Rheumatology Annual Conference: 2022; Osteoarthritis Research Society International World Congress on Osteoarthritis: OARSI-2021, OARSI-2022; American College of Rheumatology Annual Meeting: ACR-Convergence-2021; American Society of Bone and Mineral Research Annual Meeting: ASBMR-2021; Bone Research Society Annual Meeting: BRS-2020) have been published so far. |
Start Year | 2017 |
Description | Substudy-collaboration with Alder Hey Children's NHS Foundation Trust (AHFT) |
Organisation | Alder Hey Children's NHS Foundation Trust |
Country | United Kingdom |
Sector | Public |
PI Contribution | We are developing methods and software to automatically assess the hips of children from radiographic images and related clinical data. We are publishing results at conferences and in journals. |
Collaborator Contribution | AHFT provides radiographic images and related clinical data as well as clinical expertise. AHFT sets up the technical infrastructure for facilitated data sharing between the University and the Trust. AHFT contributes to all publications. |
Impact | This is a multi-disciplinary collaboration (computer science, radiology, pediatric orthopaedic surgery) that has so far led to a number of grants as well as publications. Our presentation at the Bone Research Society Annual Meeting 2020 was awarded the Best Clinical Poster Prize, and our presentation at the Health Data Research UK 2024 conference was awarded the Best Lightning Talk. |
Start Year | 2016 |
Description | Substudy-collaboration with SRM Dental College & Hospital Chennai |
Organisation | SRM Dental College |
Country | India |
Sector | Academic/University |
PI Contribution | We have developed a software system to evaluate the variation in morphology of the TMJ condyles in orthopantomogram (OPG) images. |
Collaborator Contribution | SRM provided the OPG images and the manual annotations of the outline of the TMJ condyles. The images and annotations were used to develop the system. SRM was leading on writing the journal paper. |
Impact | This is a multi-disciplinary collaboration (computer science, dentistry) that has resulted in one journal paper. |
Start Year | 2015 |
Description | Substudy-collaboration with Salford Royal NHS Foundation Trust (SRFT) |
Organisation | Salford Royal NHS Foundation Trust |
Country | United Kingdom |
Sector | Public |
PI Contribution | We are developing a software system for the fully automatic assessment of radiographic changes associated with diabetic foot collapse. |
Collaborator Contribution | SRFT provided radiographs of the foot as well as manual measurements of Meary's angle, calcaneal tilt and cuboid height. They also provided manual annotations of the outline of the bones of the foot in each of the radiographs. We use the images and annotations to develop the system, and the measurements to evaluate the system. SRFT also contributed to writing the initial journal paper. |
Impact | This is a multi-disciplinary collaboration (computer science, radiology, podiatry). We have been awarded an MRC DTP studentship for this project to be continued. We have published a journal paper on the initial results as well as a number of abstracts as part of the DTP studentship. |
Start Year | 2018 |
Description | Substudy-collaboration with Stockport NHS Foundation Trust (SFT) |
Organisation | Stockport NHS Foundation Trust |
Department | Stepping Hill Hospital |
Country | United Kingdom |
Sector | Hospitals |
PI Contribution | We are developing methods and software to automatically assess pre-/post-operative knee radiographs and related clinical data (including patient reported outcomes) of patients that underwent knee replacement surgery. |
Collaborator Contribution | SFT is providing the data as well as clinical expertise. |
Impact | This is a multi-disciplinary collaboration (computer science, orthopaedic surgery). We have so far collected over 18000 radiographic images of patients that underwent knee replacement surgery. |
Start Year | 2018 |
Description | The World COACH Consortium (a Worldwide Collaboration in OsteoArthritis prediCtion of the Hip) |
Organisation | Erasmus MC |
Department | Department of Orthopaedics |
Country | Netherlands |
Sector | Academic/University |
PI Contribution | We are developing methods and software to automatically analyse hip shape from radiographic images. |
Collaborator Contribution | Partners provide data (bringing together all prospective cohort studies worldwide that have 4+ years longitudinal hip imaging data available) and clinical expertise/skills, and study hip osteoarthritis prediction. |
Impact | Multiple peer-reviewed abstracts and journal papers have been published. |
Start Year | 2019 |
Description | UMCU - Bonefinder |
Organisation | University Medical Center Utrecht (UMC) |
Country | Netherlands |
Sector | Academic/University |
PI Contribution | We are building models to automatically locate the outlines of the bones in the hip and the knee in radiographs, and provide the models and software tools. We contribute to all publications. |
Collaborator Contribution | UMCU provides radiographs of the knee and the hip as well as manual bone outlines. UMCU uses the outlines for association studies and publishes the results at conferences and in journals. |
Impact | Three abstracts and two journal papers have been published so far. |
Start Year | 2014 |
Title | benfaber20/Automatic-alpha-angle: Alpha Angle from DXA v1.1 |
Description | Includes README and example points and list files |
Type Of Technology | Software |
Year Produced | 2021 |
Open Source License? | Yes |
Impact | We have developed and validated an automated measure of alpha angle from DXA scans, showing high agreement with manually measuring alpha angle. This software will allow alpha angle measurements to be derived in a standardised way across studies and large population cohorts. |
URL | https://zenodo.org/record/4462770 |
Description | NIHR Musculoskeletal Research User Group |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Patients, carers and/or patient groups |
Results and Impact | With regards to preparing a follow-on funding application, I presented my research to the NIHR-funded Musculoskeletal Research User Group (MSK RUG) at The University of Manchester. The meeting was attended by 13 members with a range of MSK conditions, of varying ages and from both genders. The personal perspectives gained from discussions with the group members have broadened and enhanced my thinking around how to best involve patients in future studies. The meeting also informed the design of the research to be undertaken and led to the identification of MSK RUG group members for future involvement. |
Year(s) Of Engagement Activity | 2020 |
Description | Online STEM activities |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Schools |
Results and Impact | Various online activities during COVID-19 to inform about STEM career options and to highlight how important a simple understanding of maths is (via STEM Ambassador Programme, https://www.stem.org.uk/stem-ambassadors). |
Year(s) Of Engagement Activity | 2020,2021 |
Description | Presentation at STEM for BRITAIN |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | I gave a poster presentation on "BoneFinder: An AI software tool to analyse X-ray images" at STEM for BRITAIN, Houses of Parliament, London, UK. STEM for Britain is a poster competition and exhibition at the Houses of Parliament that aims (i) to encourage, support and promote Britain's early-career STEM researchers, and (ii) to foster dialogue and engagement between early-stage researchers and Members both in Westminster and in their Constituencies. |
Year(s) Of Engagement Activity | 2020 |
Description | Presentation at Springer Nature Falling Walls Lab London |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | I gave an oral presentation on "Breaking the walls of radiologist shortages" at the Springer Nature Falling Walls Lab London. The Falling Walls Lab is a platform that enables the next generation of problem solvers to showcase their ground-breaking research and initiatives on the national and global stage. |
Year(s) Of Engagement Activity | 2019 |
Description | STEM Exhibition |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | In my role as STEM Ambassador, I have had a BoneFinder stall at the STEM Exhibition of Levenshulme High School (Manchester) to raise awareness of STEM subjects and to promote STEM careers to the students of this all girls school. |
Year(s) Of Engagement Activity | 2018,2019 |
Description | STEM talks |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | I am regularly giving STEM talks at local schools to raise awareness around STEM and to generate knowledge of STEM careers. |
Year(s) Of Engagement Activity | 2019,2020 |