Research on the relationship between intelligent diagnosis of diabetes retinopathy and biomarkers based on multimodal data fusion.

Authors

Xiaojing Huang, Xiang He

Published in

Archives of physiology and biochemistry. Pages 1-11. Nov 12, 2026. Epub Nov 12, 2026.

Abstract

To explore the risk factors of diabetic retinopathy (DR) in type 2 diabetes (T2DM) patients, construct a multimodal deep neural network model based on clinical risk score and fundus image characteristics, and verify the expression changes of key proteins in DR rat models, so as to provide a new method for early diagnosis and grading evaluation of DR.
Pathological changes such as increased retinal tissue thickness and disordered cell arrangement were observed, and SPP1, TMEM173, and Circp protein expression were significantly upregulated (p < 0.05).
The multimodal deep neural network model that integrates clinical risk scores with fundus imaging features can significantly improve the accuracy and discrimination of DR grading diagnosis, and has good generalisation ability.
The DR rat model exhibits significant inflammatory response, oxidative stress, and abnormal angiogenesis, with upregulation of SPP1, TMEM173, and Circp protein expression, which may be involved in the pathogenesis of DR.

PMID:
43016326
Bibliographic data and abstract were imported from PubMed on 12 Nov 2026.

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