Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4): A novel tool to assess the severity of hidradenitis suppurativa using artificial intelligence.

Autor: Hernández Montilla I; Department of Medical Computer Vision and PROMs, LEGIT.HEALTH, Bilbao, Spain., Medela A; Department of Medical Computer Vision and PROMs, LEGIT.HEALTH, Bilbao, Spain., Mac Carthy T; Department of Clinical Endpoint Innovation, LEGIT.HEALTH, Bilbao, Spain., Aguilar A; Department of Clinical Endpoint Innovation, LEGIT.HEALTH, Bilbao, Spain., Gómez Tejerina P; Department of Medical Computer Vision and PROMs, LEGIT.HEALTH, Bilbao, Spain., Vilas Sueiro A; Dermatology Unit, Ferrol Teaching University Hospital Complex, Ferrol, Spain., González Pérez AM; Dermatology Unit, Salamanca Teaching University Hospital, Zamora, Spain., Vergara de la Campa L; Dermatology Unit, Toledo Teaching University Hospital Complex, Toledo, Spain., Luna Bastante L; Dermatology Unit, Rey Juan Carlos Teaching University Hospital, Madrid, Spain., García Castro R; Dermatology Unit, Fundacion Jiménez Díaz Teaching University Hospital, Madrid, Spain., Alfageme Roldán F; Dermatology Unit, Puerta de Hierro Hospital, Madrid, Spain.
Jazyk: angličtina
Zdroj: Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) [Skin Res Technol] 2023 Jun; Vol. 29 (6), pp. e13357.
DOI: 10.1111/srt.13357
Abstrakt: Background: Hidradenitis suppurativa (HS) is a painful chronic inflammatory skin disease that affects up to 4% of the European adult population. International Hidradenitis Suppurativa Severity Score System (IHS4) is a dynamic scoring tool that was developed to be incorporated into the doctor's daily clinical practice and clinical studies. This helps measure disease severity and guides the therapeutic strategy. However, IHS4 assessment is a time-consuming and manual process, with high inter-observer variability and high dependence on the observer's expertise.
Materials and Methods: We introduce the Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4), an automatic equivalent of IHS4 that deploys a deep learning model for lesion detection, called Legit.Health-IHS4net, based on the YOLOv5 architecture. AIHS4 was trained on Legit.Health-HS-IHS4, a collection of HS images manually annotated by six specialists and processed by a novel knowledge unification algorithm.
Results: Our results show that, with the current dataset size, our tool assesses the severity of HS cases with a performance comparable to that of the most expert physician. Furthermore, the model can be implemented into CADx systems to support doctors in their clinical practice and act as a new endpoint in clinical trials.
Conclusion: Our work proves the potential usefulness of artificial intelligence in the practice of evidence-based dermatology: models trained on the consensus of large clinical boards have the potential to empower dermatologists in their daily practice and replace current standard clinical endpoints.
(© 2023 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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