Automated machine learning model for fundus image classification by health-care professionals with no coding experience

• Published in: Scientific reports 2024-05, Vol.14 (1), p.10395-10395, Article 10395
• Main Authors: Zago Ribeiro, Lucas, Nakayama, Luis Filipe, Malerbi, Fernando Korn, Regatieri, Caio Vinicius Saito
• Format: Article
• Language: English
• Subjects: 692/699/3161; 692/699/3161/3175; Adult; Aged; Automation; Business metrics; Classification; Datasets; Deep Learning; Diabetes; Diabetes mellitus; Diabetic Retinopathy - diagnostic imaging; Edema; Feasibility studies; Female; Fundus Oculi; Health Personnel; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted - methods; Learning algorithms; Machine Learning; Macular Edema - diagnostic imaging; Male; Middle Aged; multidisciplinary; Ophthalmology; Retinopathy; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-60807-y

To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise. The performance metrics employed to evaluate the models were F1 score, area under curve (AUC), precision and recall. The performance metrics for referable diabetic retinopathy and macular edema were above 0.9 for both tasks and CFDL. The Google Vertex models demonstrated superior performance compared to the Amazon models, with the BRSET dataset achieving the highest accuracy (AUC of 0.994). Multi-classification tasks using only BRSET achieved similar overall performance between platforms, achieving AUC of 0.994 for laterality, 0.942 for age grouping, 0.779 for genetic sex identification, 0.857 for optic, and 0.837 for normality with Google Vertex. The study demonstrates the feasibility of using automated machine learning platforms for predicting binary outcomes from fundus images in ophthalmology. It highlights the high accuracy achieved by the models in some tasks and the potential of CFDL as an entry-friendly platform for ophthalmologists to familiarize themselves with machine learning concepts.