Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders.

The development of artificial intelligence algorithms typically demands abundant high-quality data. In medicine, the datasets that are required to train the algorithms are often collected for a single task, such as image-level classification. Here, we report a workflow for the segmentation of anatomical structures and the annotation of pathological features in slit-lamp images, and the use of the workflow to improve the performance of a deep-learning algorithm for diagnosing ophthalmic disorders. We used the workflow to generate 1,772 general classification labels, 13,404 segmented anatomical structures and 8,329 pathological features from 1,772 slit-lamp images. The algorithm that was trained with the image-level classification labels and the anatomical and pathological labels showed better diagnostic performance than the algorithm that was trained with only the image-level classification labels, performed similar to three ophthalmologists across four clinically relevant retrospective scenarios and correctly diagnosed most of the consensus outcomes of 615 clinical reports in prospective datasets for the same four scenarios. The dense anatomical annotation of medical images may improve their use for automated classification and detection tasks. A workflow that segments anatomical structures in slit-lamp images and that annotates pathological features in each image improves the performance of a deep-learning algorithm for the diagnosis of ophthalmic disorders.