Clinically applicable deep learning for diagnosis and referral in retinal disease
Clinically applicable deep learning for diagnosis and referral in retinal disease
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DOI：
10.1038/s41591-018-0107-6
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发表时间：
2018-09-01
期刊：
影响因子：
82.9
通讯作者：
Ronneberger, Olaf
中科院分区：
文献类型：
--
作者：
De Fauw, Jeffrey;Ledsam, Joseph R.;Ronneberger, Olaf
诊断成像的数量和复杂性正在以比人类专业知识更快的速度增长。人工智能在对一些常见疾病的二维照片进行分类方面表现出了巨大的潜力，并且通常依赖于数百万注释图像的数据库。到目前为止，在现实世界的临床路径中使用三维诊断扫描达到专家临床医生的表现的挑战仍然没有解决。在这里，我们将一种新型的深度学习架构应用于一组临床异构的三维光学相干断层扫描，这些扫描来自一家大型眼科医院的患者。我们证明了在仅接受14，884次扫描培训后，就一系列威胁视力的视网膜疾病提出转诊建议的性能达到或超过了专家的建议。此外，我们证明了我们的架构产生的组织分割作为一个独立的设备表示;引用的准确性时，保持使用不同类型的设备的组织分割。我们的工作消除了以前的障碍，以更广泛的临床使用，而没有禁止在现实世界中的多种病理的训练数据要求。
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.