Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning
Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning
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DOI：
10.3390/app11010371
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发表时间：
2021-01-01
影响因子：
2.7
通讯作者：
Hamamoto, Ryuji
中科院分区：
文献类型：
--
作者：
Komatsu, Masaaki;Sakai, Akira;Hamamoto, Ryuji
人工智能（AI）技术最近已应用于医学成像以提供诊断支持。胎儿先天性心脏病（CHD）的超声筛查，由于人工操作和检查者技术水平的差异，一直难以达到准确诊断。因此，我们提出了一种基于卷积神经网络（CNN）的仅使用正常数据的监督对象检测（SONO）架构，以检测胎儿超声视频中的心脏子结构和结构异常。我们使用类似条形码的时间轴来可视化检测概率，并计算每个视频的异常分数。检测心脏结构异常的性能评价使用了四腔视图（心脏）和三血管气管视图（血管）周围的连续横截面视频。冠心病组异常积分均值明显高于正常组（P < 0.001）。SONO算法生成的心脏和血管的受试者工作特征曲线下面积分别为0.787和0.891，高于其他传统算法。SONO实现了胎儿超声视频中每个心脏子结构的自动检测，并显示了检测心脏结构异常的适用性。类似条形码的时间轴为检查人员捕捉每个病例的临床特征提供了信息，也有望获得医疗AI领域的重要特征之一：“可解释AI”的发展。"
Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences among examiners. Hence, we proposed an architecture of Supervised Object detection with Normal data Only (SONO), based on a convolutional neural network (CNN), to detect cardiac substructures and structural abnormalities in fetal ultrasound videos. We used a barcode-like timeline to visualize the probability of detection and calculated an abnormality score of each video. Performance evaluations of detecting cardiac structural abnormalities utilized videos of sequential cross-sections around a four-chamber view (Heart) and three-vessel trachea view (Vessels). The mean value of abnormality scores in CHD cases was significantly higher than normal cases (p < 0.001). The areas under the receiver operating characteristic curve in Heart and Vessels produced by SONO were 0.787 and 0.891, respectively, higher than the other conventional algorithms. SONO achieves an automatic detection of each cardiac substructure in fetal ultrasound videos, and shows an applicability to detect cardiac structural abnormalities. The barcode-like timeline is informative for examiners to capture the clinical characteristic of each case, and it is also expected to acquire one of the important features in the field of medical AI: the development of "explainable AI."