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Open AccessArticle

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

1
Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
2
Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
3
Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan
4
RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
5
Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
6
Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan
7
COLMINA Business Unit, Fujitsu Ltd., 1-1 Shinogura, Saiwai-ku, Kawasaki, Kanagawa 212-8510, Japan
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2021, 11(1), 371; https://doi.org/10.3390/app11010371
Received: 3 December 2020 / Revised: 27 December 2020 / Accepted: 29 December 2020 / Published: 2 January 2021
(This article belongs to the Special Issue Machine Learning in Medical Applications)
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.” View Full-Text
Keywords: fetal ultrasound video; deep learning; cardiac substructure detection; barcode-like timeline; cardiac structural abnormality fetal ultrasound video; deep learning; cardiac substructure detection; barcode-like timeline; cardiac structural abnormality
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MDPI and ACS Style

Komatsu, M.; Sakai, A.; Komatsu, R.; Matsuoka, R.; Yasutomi, S.; Shozu, K.; Dozen, A.; Machino, H.; Hidaka, H.; Arakaki, T.; Asada, K.; Kaneko, S.; Sekizawa, A.; Hamamoto, R. Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning. Appl. Sci. 2021, 11, 371. https://doi.org/10.3390/app11010371

AMA Style

Komatsu M, Sakai A, Komatsu R, Matsuoka R, Yasutomi S, Shozu K, Dozen A, Machino H, Hidaka H, Arakaki T, Asada K, Kaneko S, Sekizawa A, Hamamoto R. Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning. Applied Sciences. 2021; 11(1):371. https://doi.org/10.3390/app11010371

Chicago/Turabian Style

Komatsu, Masaaki; Sakai, Akira; Komatsu, Reina; Matsuoka, Ryu; Yasutomi, Suguru; Shozu, Kanto; Dozen, Ai; Machino, Hidenori; Hidaka, Hirokazu; Arakaki, Tatsuya; Asada, Ken; Kaneko, Syuzo; Sekizawa, Akihiko; Hamamoto, Ryuji. 2021. "Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning" Appl. Sci. 11, no. 1: 371. https://doi.org/10.3390/app11010371

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