Validation of an autonomous artificial intelligence-based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context
Font, Octavi (Optretina Image Reading Team)
Torrents-Barrena, Jordina (Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions)
Royo, Dídac (Optretina Image Reading Team)
Banderas García, Sandra (Universitat Autònoma de Barcelona. Departament de Cirurgia)
Zarranz-Ventura, Javier (Institut d'Investigacions Biomèdiques August Pi i Sunyer)
Bures, Anniken (Instituto de Microcirugía Ocular (IMO))
Salinas, Cecilia (Instituto de Microcirugía Ocular (IMO))
Zapata, Miguel Angel (Hospital Universitari Vall d'Hebron)

Fecha:	2022
Resumen:	Purpose: This study aims to evaluate the ability of an autonomous artificial intelligence (AI) system for detection of the most common central retinal pathologies in fundus photography. Methods: Retrospective diagnostic test evaluation on a raw dataset of 5918 images (2839 individuals) evaluated with non-mydriatic cameras during routine occupational health checkups. Three camera models were employed: Optomed Aurora (field of view - FOV 50º, 88% of the dataset), ZEISS VISUSCOUT 100 (FOV 40º, 9%), and Optomed SmartScope M5 (FOV 40º, 3%). Image acquisition took 2 min per patient. Ground truth for each image of the dataset was determined by 2 masked retina specialists, and disagreements were resolved by a 3rd retina specialist. The specific pathologies considered for evaluation were "diabetic retinopathy" (DR), "Age-related macular degeneration" (AMD), "glaucomatous optic neuropathy" (GON), and "Nevus. " Images with maculopathy signs that did not match the described taxonomy were classified as "Other. " Results: The combination of algorithms to detect any abnormalities had an area under the curve (AUC) of 0. 963 with a sensitivity of 92. 9% and a specificity of 86. 8%. The algorithms individually obtained are as follows: AMD AUC 0. 980 (sensitivity 93. 8%; specificity 95. 7%), DR AUC 0. 950 (sensitivity 81. 1%; specificity 94. 8%), GON AUC 0. 889 (sensitivity 53. 6% specificity 95. 7%), Nevus AUC 0. 931 (sensitivity 86. 7%; specificity 90. 7%). Conclusion: Our holistic AI approach reaches high diagnostic accuracy at simultaneous detection of DR, AMD, and Nevus. The integration of pathology-specific algorithms permits higher sensitivities with minimal impact on its specificity. It also reduces the risk of missing incidental findings. Deep learning may facilitate wider screenings of eye diseases.
Nota:	Altres ajuts: acords transformatius de la UAB
Derechos:	Tots els drets reservats.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Artifcial intelligence ; Screening ; Retinography ; Diabetic retinopathy ; Age-related macular degeneration
Publicado en:	Graefe's Archive for Clinical and Experimental Ophthalmology, May (2022) , ISSN 1435-702X

DOI: 10.1007/s00417-022-05653-2
PMID: 35567610

11 p, 843.5 KB

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