Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.

• Tác giả: Haoyu Chen, Xinjian Chen, Ching-Yu Cheng, Lixia Feng, Huazhu Fu, Ling Gao, Tao Li, Dan Liang, Zhen Liang, Aidi Lin, Tian Lin, Linna Liu, Jing Luo, Yuanyuan Peng, Tingkun Shi, Dawei Sun, Meng Wang, Jianhua Wu, Shanshan Yu, Ke Zou
• Ngôn ngữ: eng
• Ký hiệu phân loại: 133.594 Types or schools of astrology originating in or associated with a
• Thông tin xuất bản: United States : Cell reports. Medicine , 2025
• Bộ sưu tập: NCBI
• ID: 678031

Tóm tắt

 Inability to express the confidence level and detect unseen disease classes limits the clinical implementation of artificial intelligence in the real world. We develop a foundation model with uncertainty estimation (FMUE) to detect 16 retinal conditions on optical coherence tomography (OCT). In the internal test set, FMUE achieves a higher F1 score of 95.74% than other state-of-the-art algorithms (92.03%-93.66%) and improves to 97.44% with threshold strategy. The model achieves similar excellent performance on two external test sets from the same and different OCT machines. In human-model comparison, FMUE achieves a higher F1 score of 96.30% than retinal experts (86.95%, p = 0.004), senior doctors (82.71%, p <
  0.001), junior doctors (66.55%, p <
  0.001), and generative pretrained transformer 4 with vision (GPT-4V) (32.39%, p <
  0.001). Besides, FMUE predicts high uncertainty scores for >
 85% images of non-target-category diseases or with low quality to prompt manual checks and prevent misdiagnosis. Our FMUE provides a trustworthy method for automatic retinal anomaly detection in a clinical open-set environment.