Explainable Artificial Intelligence–Assisted Exploration of Clinically Significant Diabetic Retinal Neurodegeneration on OCT Images
Miyo Yoshida, Tomoaki Murakami, Kenji Ishihara, Yuki Mori, Akitaka Tsujikawa
Abstract
Purpose: To explore clinically significant diabetic retinal neurodegeneration in OCT images using explainable artificial intelligence (XAI) and subsequent evaluation by retinal specialists. Design: A single-center, retrospective, consecutive case series. Participants: Three hundred ninety-seven eyes from 397 diabetic retinopathy patients for XAI-based screening and 244 fellow eyes for subjective human evaluation. Methods: We acquired 30° horizontal OCT images centered on the fovea. An artificial intelligence (AI) model was developed to infer visual acuity (VA) reduction using fine-tuned RETFound-OCT. Attention maps highlighting regions contributing to VA inference were generated using layer-wise relevance propagation. Retinal specialists assessed OCT findings based on salient regions indicated by XAI. Two newly described findings, a needle-like appearance of the ganglion cell layer (GCL)/inner plexiform layer (IPL) ("ice-pick sign") and dot-like alterations in the outer nuclear layer (ONL) ("salt-and-pepper sign"), were evaluated alongside 2 established findings: EZ disruption and choroidal hypertransmission. Main Outcome Measures: Identification of clinically significant OCT findings associated with diabetic retinal neurodegeneration. Results: < 0.001 for all comparisons). Salt-and-pepper sign and choroidal hypertransmission exhibited high specificity for identifying eyes with poor vision. Statistical analyses demonstrated more significant associations between EZ disruption, salt-and-pepper sign, and hypertransmission compared with their relationships with the ice-pick sign. Conclusions: Artificial intelligence-assisted exploration of OCT findings identified 2 established lesions and 2 novel OCT biomarkers indicative of clinically significant diabetic retinal neurodegeneration. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.