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 |
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Hauptverfasser: | , , , , , , , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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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 |
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ISSN: | 2666-3791 2666-3791 |
DOI: | 10.1016/j.xcrm.2024.101876 |