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AI / informaticsSovremennye tekhnologii v meditsine · 2026

Integration of Transformer-Based Architecture and Large Language Models for Optical Coherence Tomography Data Analysis to Improve the Accuracy of Differential Diagnosis of Retinal Diseases.

O V Konshina, A D Pershin, M K Kulyabin, V I Borisov

OphthalmologyAI / informatics

Combining OCT biomarkers with clinical history improved retinal disease diagnosis accuracy

Integration of Transformer-Based Architecture and Large Language Models for Optical Coherence Tomography Data Analysis to Improve the Accuracy of Differential Diagnosis of Retinal Diseases.

O V Konshina … V I Borisov

Sovremennye tekhnologii v meditsine · 2026

Background

UNLABELLED: The aim of the present study was to improve the differential diagnosis accuracy of retinal diseases combining a transformer model to classify the biomarkers on OCT images and the large language model DeepSeek-V3.

Methods

For biomarker classification, we compared ResNet, DenseNet, EfficientNet, and Vision Transformer (ViT-Tiny-Patch16-224) architectures.

78%

Results

78%

combined tool picked the correct diagnosis as its top guess

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Conclusion

The suggested approach enabled to automate the detection of biomarkers on OCT images and enhance the differential diagnosis accuracy of eye diseases, reducing the image interpretation time and supporting clinical decision-making.

Post

Integration of Transformer-Based Architecture and Large Language Models for Optical Coherence Tomography Data Analysis to Improve the Accuracy of Differential Diagnosis of Retinal Diseases.

Combining OCT biomarkers with clinical history improved retinal disease diagnosis accuracy

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