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

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...

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Veröffentlicht in:Cell reports. Medicine 2024-12, p.101876
Hauptverfasser: Peng, Yuanyuan, Lin, Aidi, Wang, Meng, Lin, Tian, Liu, Linna, Wu, Jianhua, Zou, Ke, Shi, Tingkun, Feng, Lixia, Liang, Zhen, Li, Tao, Liang, Dan, Yu, Shanshan, Sun, Dawei, Luo, Jing, Gao, Ling, Chen, Xinjian, Cheng, Ching-Yu, Fu, Huazhu, Chen, Haoyu
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Sprache:eng
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Zusammenfassung: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 
ISSN:2666-3791
2666-3791
DOI:10.1016/j.xcrm.2024.101876