Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning

Komatsu, Masaaki and Sakai, Akira and Komatsu, Reina and Matsuoka, Ryu and Yasutomi, Suguru and Shozu, Kanto and Dozen, Ai and Machino, Hidenori and Hidaka, Hirokazu and Arakaki, Tatsuya and Asada, Ken and Kaneko, Syuzo and Sekizawa, Akihiko and Hamamoto, Ryuji (2021) Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning. Applied Sciences, 11 (1). p. 371. ISSN 2076-3417

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Abstract

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.”

Item Type: Article
Subjects: EP Archives > Multidisciplinary
Depositing User: Managing Editor
Date Deposited: 16 Mar 2023 09:28
Last Modified: 31 May 2024 09:33
URI: http://research.send4journal.com/id/eprint/816

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