ArticleBMC ophthalmology2026
Research on deep learning-based lesion identification in optical coherence tomography.
Article in BMC ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo achieve automated identification of lesions in Optical Coherence Tomography images based on a lightweight convolutional neural network architecture.
methodsThis retrospective study developed a lightweight lesion recognition and classification model based on deep learning, utilizing OCT images from Sichuan Provincial People’s Hospital as well as datasets including OCT-C8, OCT-2017, and HD-OCT of MH. A total of 574,808 training images and 71,851 validation images were included. Model performance evaluation was conducted in two phases: First, the model’s generalization capability and stability were tested on four independent external validation sets (with image counts of 71,854, 2,763, 1,650, and 785, respectively). Second, the clinical experience level corresponding to the model’s diagnostic proficiency was assessed by comparing its classification performance with that of ophthalmologists at different stages of clinical expertise.
resultsThe model achieved an average accuracy of 87.40%, an F1-score of 75.78%, and a ROC_AUC value of 98.68% on the validation set. For the nine categories of OCT images—neovascularization, posterior vitreous detachment, epiretinal membrane, macular hole, macular schisis, subretinal fluid, normal images, vitreomacular traction, and obscured images—the model’s specificity was 99.42%, 98.20%, 95.43%, 99.01%, 96.97%, 99.66%, 99.15%, 99.54%, and 98.56%, respectively; while its recall rates were 94.29%, 74.16%, 97.94%, 76.17%, 89.16%, 84.03%, 73.08%, 73.23%, and 98.44%, respectively. The model achieved an average accuracy of 89.42% in the external validation sets. In comparison, junior physicians had an accuracy of 74% (95% CI: 61.84%–86.16%) in interpreting OCT images, while mid-career physicians had an accuracy of 88% (95% CI: 78.99%–97.01%). These results indicate that the model’s performance approaches that of mid-career physicians.
conclusionThis study achieved automated identification of nine categories of OCT images, with its clinical performance initially reaching the level of mid-career physicians. Moreover, the model can be deployed locally in medical environments to enhance diagnostic efficiency and accuracy.
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