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

ABSTRACT: 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 transforme

SUBMITTER: Peng Y 

PROVIDER: S-EPMC11866418 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature