Performance of vision language models for optic disc swelling identification on fundus photographs.

Abstract

Introduction: Vision language models (VLMs) combine image analysis capabilities with large language models (LLMs). Because of their multimodal capabilities, VLMs offer a clinical advantage over image classification models for the diagnosis of optic disc swelling by allowing a consideration of clinical context. In this study, we compare the performance of non-specialty-trained VLMs with different prompts in the classification of optic disc swelling on fundus photographs.

Methods: A diagnostic test accuracy study was conducted utilizing an open-sourced dataset. Five different prompts (increasing in context) were used with each of five different VLMs (Llama 3.2-vision, LLaVA-Med, LLaVA, GPT-4o, and DeepSeek-4V), resulting in 25 prompt-model pairs. The performance of VLMs in classifying photographs with and without optic disc swelling was measured using Youden's index (YI), F1 score, and accuracy rate.

Results: A total of 779 images of normal optic discs and 295 images of swollen discs were obtained from an open-source image database. Among the 25 prompt-model pairs, valid response rates ranged from 7.8% to 100% (median 93.6%). Diagnostic performance ranged from YI: 0.00 to 0.231 (median 0.042), F1 score: 0.00 to 0.716 (median 0.401), and accuracy rate: 27.5 to 70.5% (median 58.8%). The best-performing prompt-model pair was GPT-4o with role-playing with Chain-of-Thought and few-shot prompting. On average, Llama 3.2-vision performed the best (average YI across prompts 0.181). There was no consistent relationship between the amount of information given in the prompt and the model performance.

Conclusions: Non-specialty-trained VLMs could classify photographs of swollen and normal optic discs better than chance, with performance varying by model. Increasing prompt complexity did not consistently improve performance. Specialty-specific VLMs may be necessary to improve ophthalmic image analysis performance.