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• W4366392421 abstract "<sec> <title>BACKGROUND</title> Recent advancements in computer vision and deep learning techniques, deep neural networks have helped achieve expert-level performance in clinical diagnoses. </sec> <sec> <title>OBJECTIVE</title> This diagnostic study aimed to determine the usefulness of a proposed artificial intelligence (AI)-based computer-aided diagnosis (AI-CAD) system in assisting ophthalmologists with the diagnosis of retinal diseases using optical coherence tomography (OCT) images. </sec> <sec> <title>METHODS</title> For the training and evaluation of the proposed deep learning model, 1,693 OCT images were collected and annotated. The dataset included 929 and 764 cases of acute and chronic central serous chorioretinopathy, respectively. Sixty-six ophthalmologists (two groups: 36 retina and 30 non-retina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. One hundred randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a central serous chorioretinopathy subtype for each of these images. Each image was provided under different conditions: (i) without AI support (No AI), (ii) with an AI probability score (AI prob), and (iii) with an AI probability score and visual evidence (AI Prob+Evid). The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. </sec> <sec> <title>RESULTS</title> The proposed system achieved a high detection performance (99.0% of the area under the curve), outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI Prob+Evid achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (No AI or AI Prob). Non-retina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. </sec> <sec> <title>CONCLUSIONS</title> Our proposed AI-CAD system improved the diagnosis of retinal disease by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists. </sec>" @default.
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• W4366392421 date "2023-04-14" @default.
• W4366392421 modified "2023-09-26" @default.
• W4366392421 title "Developing and evaluating an artificial intelligence-based computer-aided diagnosis system for retinal disease: A diagnostic study (Preprint)" @default.
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• W4366392421 doi "https://doi.org/10.2196/preprints.48142" @default.
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