{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T15:59:19Z","timestamp":1787932759671,"version":"build-2784847793"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cabinet Office BRIDGE"},{"name":"National Cancer Center Research and Development Fund","award":["2023-A-04"],"award-info":[{"award-number":["2023-A-04"]}]},{"name":"JSPS Grant-in-Aid for Scientific Research","award":["JP21K09456"],"award-info":[{"award-number":["JP21K09456"]}]},{"name":"JSPS Grant-in-Aid for Scientific Research","award":["JP24K12634"],"award-info":[{"award-number":["JP24K12634"]}]},{"name":"JSPS Grant-in-Aid for Scientific Research","award":["JP25K15973"],"award-info":[{"award-number":["JP25K15973"]}]},{"name":"JST SPRING","award":["JPMJSP2180"],"award-info":[{"award-number":["JPMJSP2180"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Total anomalous pulmonary venous connection (TAPVC) is one of the most severe congenital heart defects; however, prenatal diagnosis remains suboptimal. A normal fetal heart has a junction between the pulmonary venous (PV) and left atrium (LA). In contrast, no junctions are observed in patients with TAPVC. In the present study, we attempted to visualize and detect fetal PV-LA connections using artificial intelligence (AI) trained on the fetal cardiac ultrasound videos of 100 normal cases and six TAPVC cases. The PV-LA aggregate area was segmented using the following three-dimensional (3D) segmentation models: SegResNet, Swin UNETR, MedNeXt, and SegFormer3D. The Dice coefficient and 95% Hausdorff distance were used to evaluate segmentation performance. The mean values of the shortest PV-LA distance (PLD) and major axis angle (PLA) in each video were calculated. These methods demonstrated sufficient performance in visualizing and detecting the PV-LA connection. In terms of TAPVC screening performance, MedNeXt-PLD and SegResNet-PLA achieved mean area under the receiver operating characteristic curve values of 0.844 and 0.840, respectively. Overall, this study shows that our approach can support unskilled examiners in capturing the PV-LA connection and has the potential to improve the prenatal detection rate of TAPVC.<\/jats:p>","DOI":"10.3390\/bioengineering13010100","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T08:26:36Z","timestamp":1768465596000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Three-Dimensional Visualization and Detection of the Pulmonary Venous\u2013Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7681-9051","authenticated-orcid":false,"given":"Reina","family":"Komatsu","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, Showa Medical University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0421-8085","authenticated-orcid":false,"given":"Masaaki","family":"Komatsu","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Showa Medical University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan"},{"name":"AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan"},{"name":"Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8053-8098","authenticated-orcid":false,"given":"Katsuji","family":"Takeda","sequence":"additional","affiliation":[{"name":"AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, 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Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1296-8152","authenticated-orcid":false,"given":"Naoki","family":"Teraya","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Showa Medical University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shohei","family":"Wakisaka","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Showa Medical University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan"},{"name":"Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8240-0264","authenticated-orcid":false,"given":"Takashi","family":"Natsume","sequence":"additional","affiliation":[{"name":"Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan"},{"name":"Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomonori","family":"Taniguchi","sequence":"additional","affiliation":[{"name":"Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan"},{"name":"Department of Obstetrics and Gynecology, Tohoku University Graduate School of Medicine, 2-1 Seiryomachi, Aoba-ku, Sendai 980-8574, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rina","family":"Aoyama","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Showa Medical University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0613-446X","authenticated-orcid":false,"given":"Mayumi","family":"Kaneko","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Showa Medical University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuki","family":"Iwamoto","sequence":"additional","affiliation":[{"name":"Digital Health Platform Development Office, Healthcare Business Unit, Fujitsu Japan Ltd., 1-5 Omiya-cho, Saiwai-ku, Kawasaki 212-0014, 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