{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:00:29Z","timestamp":1790834429363,"version":"4.1.0"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Nuchal Translucency (NT) is a measurement taken from ultrasound images described as the gathering of fluid behind the fetus's neck, during 11\u201313 weeks. Increased Nuchal Translucency can lead to high risk of chromosomal abnormalities like Down Syndrome. The existing AI model may struggle to detect the subtle difference due to low resolution fetal movement, which can lead to errors in measurements and diagnosis. In addition, traditional NT measurement methods rely heavily on manual caliper placement, making the process operator-dependent and reducing consistency and automation. To address these challenges, this paper proposes an Enhanced Multi-Scale Spatial Temporal Graph Attention Network (EMSSTGAN) to predict the risk of chromosomal abnormalities in NT. Initially, the input images of NT are pre-processed using feature refinement attention network (FRANet) for denoising and Multi-Scale Attention Retinex Network (MARN) for contrast enhancement. NT detection is then performed by localizing the nuchal translucency region using bounding box annotations, followed by U-Net-based segmentation for precise extraction of the NT area. The Active Contour Model (ACM) is subsequently applied to measure the NT thickness based on boundary geometry. The thickness when compared with threshold values is used to identify the risk of chromosomal abnormalities as low, medium and high. The proposed model achieves an accuracy of 98.15%, an error of 1.85%, and a FDR of 2.44%, respectively.<\/jats:p>","DOI":"10.3389\/frai.2026.1914775","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:41:30Z","timestamp":1790833290000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Nuchal Translucency analysis for early prenatal risk stratification"],"prefix":"10.3389","volume":"9","author":[{"given":"Smita S.","family":"Pawar","sequence":"first","affiliation":[{"name":"Electrical and Electronics Department, Sandip University","place":["Nashik, India"]},{"name":"DYPIU","place":["Akurdi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mangesh D.","family":"Nikose","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Department, Sandip University","place":["Nashik, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prakash G.","family":"Burade","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Department, Sandip University","place":["Nashik, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.2147\/RMHP.S511035","article-title":"Machine learning-based prediction of first trimester down syndrome risk in East Asian populations","volume":"18","author":"Chen","year":"2025","journal-title":"Risk Manag. 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