1
|
Karpathakis K, Pencheon E, Cushnan D. Learning From International Comparators of National Medical Imaging Initiatives for AI Development: Multiphase Qualitative Study. JMIR AI 2024; 3:e51168. [PMID: 38875584 DOI: 10.2196/51168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/01/2023] [Accepted: 11/03/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND The COVID-19 pandemic drove investment and research into medical imaging platforms to provide data to create artificial intelligence (AI) algorithms for the management of patients with COVID-19. Building on the success of England's National COVID-19 Chest Imaging Database, the national digital policy body (NHSX) sought to create a generalized national medical imaging platform for the development, validation, and deployment of algorithms. OBJECTIVE This study aims to understand international use cases of medical imaging platforms for the development and implementation of algorithms to inform the creation of England's national imaging platform. METHODS The National Health Service (NHS) AI Lab Policy and Strategy Team adopted a multiphased approach: (1) identification and prioritization of national AI imaging platforms; (2) Political, Economic, Social, Technological, Legal, and Environmental (PESTLE) factor analysis deep dive into national AI imaging platforms; (3) semistructured interviews with key stakeholders; (4) workshop on emerging themes and insights with the internal NHSX team; and (5) formulation of policy recommendations. RESULTS International use cases of national AI imaging platforms (n=7) were prioritized for PESTLE factor analysis. Stakeholders (n=13) from the international use cases were interviewed. Themes (n=8) from the semistructured interviews, including interview quotes, were analyzed with workshop participants (n=5). The outputs of the deep dives, interviews, and workshop were synthesized thematically into 8 categories with 17 subcategories. On the basis of the insights from the international use cases, policy recommendations (n=12) were developed to support the NHS AI Lab in the design and development of the English national medical imaging platform. CONCLUSIONS The creation of AI algorithms supporting technology and infrastructure such as platforms often occurs in isolation within countries, let alone between countries. This novel policy research project sought to bridge the gap by learning from the challenges, successes, and experience of England's international counterparts. Policy recommendations based on international learnings focused on the demonstrable benefits of the platform to secure sustainable funding, validation of algorithms and infrastructure to support in situ deployment, and creating wraparound tools for nontechnical participants such as clinicians to engage with algorithm creation. As health care organizations increasingly adopt technological solutions, policy makers have a responsibility to ensure that initiatives are informed by learnings from both national and international initiatives as well as disseminating the outcomes of their work.
Collapse
Affiliation(s)
| | - Emma Pencheon
- Foreign, Commonwealth and Development Office, UK Government, London, United Kingdom
| | | |
Collapse
|
2
|
Shadbahr T, Roberts M, Stanczuk J, Gilbey J, Teare P, Dittmer S, Thorpe M, Torné RV, Sala E, Lió P, Patel M, Preller J, Rudd JHF, Mirtti T, Rannikko AS, Aston JAD, Tang J, Schönlieb CB. The impact of imputation quality on machine learning classifiers for datasets with missing values. COMMUNICATIONS MEDICINE 2023; 3:139. [PMID: 37803172 PMCID: PMC10558448 DOI: 10.1038/s43856-023-00356-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/13/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance. METHODS We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. RESULTS The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. CONCLUSIONS It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.
Collapse
Affiliation(s)
- Tolou Shadbahr
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
- Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK.
| | - Jan Stanczuk
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Julian Gilbey
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Philip Teare
- Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK
| | - Sören Dittmer
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- ZeTeM, University of Bremen, Bremen, Germany
| | - Matthew Thorpe
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Ramon Viñas Torné
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Pietro Lió
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Mishal Patel
- Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK
- Clinical Pharmacology & Safety Sciences, AstraZeneca, Cambridge, UK
| | - Jacobus Preller
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - James H F Rudd
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Tuomas Mirtti
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Antti Sakari Rannikko
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Department of Urology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - John A D Aston
- Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| |
Collapse
|
3
|
Rangelov B, Young A, Lilaonitkul W, Aslani S, Taylor P, Guðmundsson E, Yang Q, Hu Y, Hurst JR, Hawkes DJ, Jacob J. Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes. Sci Rep 2023; 13:9986. [PMID: 37339958 PMCID: PMC10282086 DOI: 10.1038/s41598-023-32469-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 03/28/2023] [Indexed: 06/22/2023] Open
Abstract
The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model-SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease.
Collapse
Affiliation(s)
- Bojidar Rangelov
- Satsuma Lab, Centre for Medical Image Computing (CMIC), University College London, London, UK.
| | - Alexandra Young
- Satsuma Lab, Centre for Medical Image Computing (CMIC), University College London, London, UK
- Department of Neuroimaging, King's College London, London, UK
| | | | - Shahab Aslani
- Satsuma Lab, Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Paul Taylor
- Institute of Health Informatics, University College London, London, UK
| | - Eyjólfur Guðmundsson
- Satsuma Lab, Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Qianye Yang
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Yipeng Hu
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - John R Hurst
- UCL Respiratory, University College London, London, UK
| | - David J Hawkes
- Centre for Medical Image Computing, University College London, London, UK
| | - Joseph Jacob
- Satsuma Lab, Centre for Medical Image Computing (CMIC), University College London, London, UK
| |
Collapse
|
4
|
Giansanti D, Di Basilio F. The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus. Healthcare (Basel) 2022; 10:509. [PMID: 35326987 PMCID: PMC8949694 DOI: 10.3390/healthcare10030509] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/03/2022] [Accepted: 03/08/2022] [Indexed: 12/27/2022] Open
Abstract
Artificial intelligence is having important developments in the world of digital radiology also thanks to the boost given to the research sector by the COVID-19 pandemic. In the last two years, there was an important development of studies focused on both challenges and acceptance and consensus in the field of Artificial Intelligence. The challenges and acceptance and consensus are two strategic aspects in the development and integration of technologies in the health domain. The study conducted two narrative reviews by means of two parallel points of view to take stock both on the ongoing challenges and on initiatives conducted to face the acceptance and consensus in this area. The methodology of the review was based on: (I) search of PubMed and Scopus and (II) an eligibility assessment, using parameters with 5 levels of score. The results have: (a) highlighted and categorized the important challenges in place. (b) Illustrated the different types of studies conducted through original questionnaires. The study suggests for future research based on questionnaires a better calibration and inclusion of the challenges in place together with validation and administration paths at an international level.
Collapse
|
5
|
Bird A, Oakden-Rayner L, McMaster C, Smith LA, Zeng M, Wechalekar MD, Ray S, Proudman S, Palmer LJ. Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint. Arthritis Res Ther 2022; 24:268. [PMID: 36510330 PMCID: PMC9743640 DOI: 10.1186/s13075-022-02972-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/03/2022] [Indexed: 12/14/2022] Open
Abstract
Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has been the assessment of plain radiographs with scoring techniques that quantify joint damage. However, with significant improvements in therapy, current radiographic scoring systems may no longer be fit for purpose for the milder spectrum of disease seen today. We argue that artificial intelligence is an apt solution to further improve upon radiographic scoring, as it can readily learn to recognize subtle patterns in imaging data to not only improve efficiency, but can also increase the sensitivity to variation in mild disease. Current work in the area demonstrates the feasibility of automating scoring but is yet to take full advantage of the strengths of artificial intelligence. By fully leveraging the power of artificial intelligence, faster and more sensitive scoring could enable the ongoing development of effective treatments for patients with rheumatoid arthritis.
Collapse
Affiliation(s)
- Alix Bird
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
| | - Lauren Oakden-Rayner
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
| | - Christopher McMaster
- grid.410678.c0000 0000 9374 3516Department of Rheumatology, Austin Health, Heidelberg, VIC 3084 Australia
| | - Luke A. Smith
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
| | - Minyan Zeng
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
| | - Mihir D. Wechalekar
- grid.1014.40000 0004 0367 2697Department of Rheumatology, Flinders Medical Centre, and College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042 Australia
| | - Shonket Ray
- grid.418019.50000 0004 0393 4335Artificial Intelligence and Machine Learning, GlaxoSmithKline, South San Francisco, CA USA
| | - Susanna Proudman
- grid.416075.10000 0004 0367 1221Department of Rheumatology, Royal Adelaide Hospital, Adelaide, SA 5000 Australia
| | - Lyle J. Palmer
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
| |
Collapse
|