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- Mcgenity, Clare, et al.
(författare)
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Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
- 2024
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Ingår i: npj Digital Medicine. - : NATURE PORTFOLIO. - 2398-6352. ; 7:1
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Forskningsöversikt (refereegranskat)abstract
- Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.
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| 2. |
- McGenity, Clare, et al.
(författare)
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Guidelines for clinical trials using artificial intelligence - SPIRIT-AI and CONSORT-AI(dagger)
- 2021
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Ingår i: Journal of Pathology. - : WILEY. - 0022-3417 .- 1096-9896. ; 253:1, s. 14-16
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Tidskriftsartikel (refereegranskat)abstract
- The rapidly growing use of artificial intelligence in pathology presents a challenge in terms of study reporting and methodology. The existing guidelines for the design (SPIRIT) and reporting (CONSORT) of clinical trials have been extended with the aim of ensuring production of the highest quality evidence in this field. We explore these new guidelines and their relevance and application to pathology as a specialty. (c) 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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| 3. |
- McGenity, Clare, et al.
(författare)
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Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence
- 2023
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Ingår i: Journal of Clinical Pathology. - : BMJ PUBLISHING GROUP. - 0021-9746 .- 1472-4146.
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Tidskriftsartikel (refereegranskat)abstract
- AimsA survey of members of the UK Liver Pathology Group (UKLPG) was conducted, comprising consultant histopathologists from across the UK who report liver specimens and participate in the UK National Liver Pathology External Quality Assurance scheme. The aim of this study was to understand attitudes and priorities of liver pathologists towards digital pathology and artificial intelligence (AI). MethodsThe survey was distributed to all full consultant members of the UKLPG via email. This comprised 50 questions, with 48 multiple choice questions and 2 free-text questions at the end, covering a range of topics and concepts pertaining to the use of digital pathology and AI in liver disease. ResultsForty-two consultant histopathologists completed the survey, representing 36% of fully registered members of the UKLPG (42/116). Questions examining digital pathology showed respondents agreed with the utility of digital pathology for primary diagnosis 83% (34/41), second opinions 90% (37/41), research 85% (35/41) and training and education 95% (39/41). Fatty liver diseases were an area of demand for AI tools with 80% in agreement (33/41), followed by neoplastic liver diseases with 59% in agreement (24/41). Participants were concerned about AI development without pathologist involvement 73% (30/41), however, 63% (26/41) disagreed when asked whether AI would replace pathologists. ConclusionsThis study outlines current interest, priorities for research and concerns around digital pathology and AI for liver pathologists. The majority of UK liver pathologists are in favour of the application of digital pathology and AI in clinical practice, research and education.
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