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Diabetic peripheral neuropathy identification using enface optical coherence tomography and multi-head attention deep

Ying Zou^1,2, Ning Huo¹, Li Chen¹

¹Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.

|March 30, 2026

Summary

This summary is machine-generated.

A new deep learning model using optical coherence tomography (OCT) images can detect diabetic peripheral neuropathy (DPN) non-invasively. The avascular layer in OCT scans shows promise for early DPN screening.

Keywords:

Diabetic peripheral neuropathy algorithm deep learning optical coherence tomography photoreceptor defects

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Area of Science:

Ophthalmology
Medical Imaging
Computational Biology

Background:

Diabetic peripheral neuropathy (DPN) is a common diabetes complication with limited diagnostic tools.
Current methods for DPN diagnosis are invasive, insensitive, or subjective.
There is a need for non-invasive and reliable early detection methods for DPN.

Purpose of the Study:

To develop a non-invasive diagnostic tool for early DPN detection.
To utilize multimodal optical coherence tomography (OCT) images and a deep learning (DL) algorithm.
To investigate the efficacy of a multi-head attention DL model for DPN diagnosis.

Main Methods:

A multi-head attention DL model with ResNet-18 was developed to classify enface OCT images.
The model was trained and validated on 3264 OCT images from 435 diabetic patients.
Gradient-weighted Class Activation Mapping (Grad-CAM) was used for model interpretability.

Main Results:

The DL model achieved an AUC of 0.719 (cross-validation) and 0.721 (temporal validation).
The avascular layer demonstrated the highest predictive value for DPN (AUC=0.707).
Photoreceptor defects were identified as key indicators for DPN classification by the model.

Conclusions:

A multi-head attention DL model effectively detects DPN using non-invasive OCT images.
The avascular layer and photoreceptor defects show potential as biomarkers for DPN.
This approach offers a promising strategy for early DPN screening and diagnosis.