Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care
Escalé-Besa, Anna (Institut Català de la Salut)
Yélamos, Oriol (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya))
Vidal-Alaball, Josep (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Fuster-Casanovas, Aïna (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Miró Catalina, Queralt (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Börve, Alexander (University of Gothenburg (Suècia))
Ander-Egg Aguilar, Ricardo (iDoc24 Inc (Estats Units d'Amèrica))
Fustà-Novell, Xavier (Fundació Althaia de Manresa)
Cubiró, Xavier (Hospital Universitari Mollet)
Esquius Rafat, Mireia (Fundació Althaia de Manresa)
López-Sanchez, Cristina (Institut d'Investigació Biomèdica Sant Pau)
Marin-Gomez, Francesc X. (Institut Català de la Salut)
Universitat Autònoma de Barcelona

Fecha:	2023
Resumen:	Dermatological conditions are a relevant health problem. Machine learning (ML) models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and disease classification. The objective of this study was to perform a prospective validation of an image analysis ML model, which is capable of screening 44 skin diseases, comparing its diagnostic accuracy with that of General Practitioners (GPs) and teledermatology (TD) dermatologists in a real-life setting. Prospective, diagnostic accuracy study including 100 consecutive patients with a skin problem who visited a participating GP in central Catalonia, Spain, between June 2021 and October 2021. The skin issue was first assessed by the GPs. Then an anonymised skin disease picture was taken and uploaded to the ML application, which returned a list with the Top-5 possible diagnosis in order of probability. The same image was then sent to a dermatologist via TD for diagnosis, as per clinical practice. The GPs Top-3, ML model's Top-5 and dermatologist's Top-3 assessments were compared to calculate the accuracy, sensitivity, specificity and diagnostic accuracy of the ML models. The overall Top-1 accuracy of the ML model (39%) was lower than that of GPs (64%) and dermatologists (72%). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained (n = 82), the balanced Top-1 accuracy of the ML model increased (48%) and in the Top-3 (75%) was comparable to the GPs Top-3 accuracy (76%). The Top-5 accuracy of the ML model (89%) was comparable to the dermatologist Top-3 accuracy (90%). For the different diseases, the sensitivity of the model (Top-3 87% and Top-5 96%) is higher than that of the clinicians (Top-3 GPs 76% and Top-3 dermatologists 84%) only in the benign tumour pathology group, being on the other hand the most prevalent category (n = 53). About the satisfaction of professionals, 92% of the GPs considered it as a useful diagnostic support tool (DST) for the differential diagnosis and in 60% of the cases as an aid in the final diagnosis of the skin lesion. The overall diagnostic accuracy of the model in this study, under real-life conditions, is lower than that of both GPs and dermatologists. This result aligns with the findings of few existing prospective studies conducted under real-life conditions. The outcomes emphasize the significance of involving clinicians in the training of the model and the capability of ML models to assist GPs, particularly in differential diagnosis. Nevertheless, external testing in real-life conditions is crucial for data validation and regulation of these AI diagnostic models before they can be used in primary care.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Health care ; Skin diseases
Publicado en:	Scientific reports, Vol. 13 (march 2023) , ISSN 2045-2322

DOI: 10.1038/s41598-023-31340-1
PMID: 36922556

14 p, 2.8 MB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias de la salud y biociencias > Institut de Recerca Sant Pau
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