Related Experiment Video
Updated: Mar 31, 2026
Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography
Published on: February 18, 2022
Diabetic peripheral neuropathy identification using enface optical coherence tomography and multi-head attention deep
Ying Zou1,2, Ning Huo1, Li Chen1
1Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.
Objective:
Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, but current diagnostic methods are limited by invasiveness, poor sensitivity, or subjectivity. This study aims to develop a non-invasive, reliable diagnostic tool using multimodal optical coherence tomography (OCT) images and a deep learning (DL) algorithm with multi-head attention for early DPN detection.
Methods:
A multi-head attention-based DL model was constructed, with ResNet-18 as the feature extractor to fuse and classify enface OCT images from different retinal layers. A total of 3264 OCT images from 544 eyes of 435 diabetic patients were enrolled. The model was evaluated via fivefold cross-validation on the training dataset (Dataset A, n = 267) and further validated on a temporal validation dataset (Dataset B, n = 168). Single-layer contrast experiments were conducted to identify the most predictive retinal layer, and Gradient-weighted Class Activation Mapping (Grad-CAM) was used for model visualization.
Results:
The proposed model achieved an average area under the curve (AUC) of 0.719 in fivefold cross-validation and an AUC of 0.721 in the temporal validation dataset. Among all retinal layers, the avascular layer provided the highest predictive value for DPN (average AUC = 0.707), with significant differences in performance compared to other layers (p < 0.05). Grad-CAM visualization revealed that photoreceptor defects were the key regions contributing to the model's classification decisions, suggesting an association between photoreceptor abnormalities and DPN. Additionally, the model outperformed individual retinal indicators (retinal nerve fiber layer thickness, superficial capillary plexus/deep capillary plexus density) whose AUCs ranged only from 0.524 to 0.565.
Conclusion:
The multi-head attention-based DL model effectively identifies DPN using non-invasive OCT images, with the avascular layer providing critical information. This approach provides a promising clinically feasible early screening strategy, and photoreceptor defects may serve as a potential DPN biomarker, requiring further validation.
