Ophthalmology science · 2025
Explainable Artificial Intelligence-Assisted Exploration of Clinically Significant Diabetic Retinal Neurodegeneration on OCT Images.
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Yoshida Miyo, Murakami Tomoaki, Ishihara Kenji, Mori Yuki, Tsujikawa Akitaka
Аннотация
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: The AI model effectively discriminated eyes with poor vision (decimal VA ≤0.5) from those with good vision (VA ≥1.0) (area under the receiver operating characteristic curve of 0.947). Explainable artificial intelligence-based attention maps highlighted salient regions in the GCL/IPL (65.2% or 70.0%), ONL (52.2% or 28.3%), EZ (39.1% or 21.7%), and choroid (26.1% or 5.00%) in eyes with poor or good vision, respectively. Subjective evaluation by retinal specialists revealed the frequencies of these 4 findings as follows: ice-pick sign (32.4%), EZ disruption (25.0%), salt-and-pepper sign (16.0%), and choroidal hypertransmission (13.5%). Eyes with decimal VA ≤0.9 had these findings more frequently than those with VA ≥1.0 (P < 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.
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: The AI model effectively discriminated eyes with poor vision (decimal VA ≤0.5) from those with good vision (VA ≥1.0) (area under the receiver operating characteristic curve of 0.947). Explainable artificial intelligence-based attention maps highlighted salient regions in the GCL/IPL (65.2% or 70.0%), ONL (52.2% or 28.3%), EZ (39.1% or 21.7%), and choroid (26.1% or 5.00%) in eyes with poor or good vision, respectively. Subjective evaluation by retinal specialists revealed the frequencies of these 4 findings as follows: ice-pick sign (32.4%), EZ disruption (25.0%), salt-and-pepper sign (16.0%), and choroidal hypertransmission (13.5%). Eyes with decimal VA ≤0.9 had these findings more frequently than those with VA ≥1.0 (P < 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.