Department of Ophthalmology, University of Washington, Seattle, WA, USA.
eScience Institute, University of Washington, Seattle, WA, USA.
Sci Rep. 2019 Apr 5;9(1):5694. doi: 10.1038/s41598-019-42042-y.
Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures retinal blood flow, to train an AI algorithm to generate flow maps from standard optical coherence tomography (OCT) images, exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer flow from single structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). Our model allows generating flow maps from large volumes of previously collected OCT data in existing clinical trials and clinical practice. This finding demonstrates a novel application of AI to medical imaging, whereby subtle regularities between different modalities are used to image the same body part and AI is used to generate detailed inferences of tissue function from structure imaging.
尽管人工智能 (AI) 取得了进步，但在医学成像中的应用却受到了专家生成标签的负担和限制。我们使用光学相干断层扫描血管造影 (OCTA) 的图像，这是一种相对较新的测量视网膜血流的成像方式，来训练人工智能算法，以便从标准的光学相干断层扫描 (OCT) 图像生成血流图，从而超越了专家标记的能力和绕过了对其的需求。深度学习能够从单个结构 OCT 图像推断出与 OCTA 相似的血流，并且明显优于专家临床医生（P<0.00001）。我们的模型允许从以前在现有临床试验和临床实践中收集的大量 OCT 数据中生成血流图。这一发现展示了人工智能在医学成像中的新应用，其中不同模式之间的细微规律被用于对同一身体部位成像，而人工智能则被用于从结构成像生成组织功能的详细推断。
