期刊
NATURE MEDICINE
卷 24, 期 9, 页码 1342-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41591-018-0107-6
关键词
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资金
- NIHR Clinician Scientist Award [NIHR-CS-2014-14-023]
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
- UCL Institute of Ophthalmology
- NIHR Moorfields Clinical Research Facility
- College of Optometrists, United Kingdom
- Wellcome Trust [100227/Z/12/Z] Funding Source: researchfish
The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.
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