{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:49:36Z","timestamp":1792003776650,"version":"4.4.0"},"reference-count":24,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Clinical Ultrasound"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The fetal ultrasound examination is the significant task of mid\u2010term pregnancy inspection and the accurate localization as well as the segmentation of the cerebellum is crucial for clinical diagnosis. This research focuses on developing deep learning techniques for prenatal prediction of neurodevelopmental disorders using 5th\u2010month ultrasound brain images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study introduces two specialized convolutional neural network (CNN) architectures: the differential CNN for plane localization and the dual CNN for cerebellum segmentation which are critical for accurate diagnostics during prenatal care. The differential CNN incorporates six different convolutional operators to capture diverse features for precise localization of specific planes within images. The dual CNN architecture integrates both the original image and complementary information such as feature maps, to enhance segmentation accuracy for the cerebellum. The models are trained on annotated datasets of ultrasound images, validated, and tested on separate datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The effectiveness of the proposed CNN architectures is determined by performing the specific tasks of plane localization and cerebellum segmentation, respectively. The proposed models achieved high performances of 98.6% and 0.956% from accuracy and dice coefficient (DSC) compared to existing approaches in medical image analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The findings of this study have significant implications for the prenatal prediction of neurodevelopmental disorders, offering a promising advancement in prenatal care and early diagnostics. The custom CNN architectures tailored to the specific tasks of plane localization and cerebellum segmentation highlight the importance of task\u2010specific model design in medical imaging. While the study acknowledges certain limitations and challenges, the power of deep learning is analyzed for marking the benefit of healthcare and neurodevelopmental disorder prediction during pregnancy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcu.23924","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T01:40:01Z","timestamp":1738633201000},"page":"721-735","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Prenatal Diagnostics Using Deep Learning: A Dual Approach to Plane Localization and Cerebellum Segmentation in Ultrasound Images"],"prefix":"10.1002","volume":"53","author":[{"given":"D.","family":"Vetriselvi","sequence":"first","affiliation":[{"name":"Department of Computing Technologies School of Computing, SRM Institute of Science and Technology  Kattankulathur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9477-2111","authenticated-orcid":false,"given":"R.","family":"Thenmozhi","sequence":"additional","affiliation":[{"name":"Department of Computing Technologies School of Computing, SRM Institute of Science and Technology  Kattankulathur India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-022-13359-7"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12060792"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/acer.15009"},{"key":"e_1_2_12_5_1","first-page":"146","article-title":"The Impact of Exercise in Improving Executive Function Impairments Among Children and Adolescents With ADHD, Autism Spectrum Disorder, and Fetal Alcohol Spectrum Disorder: A Systematic Review and Meta\u2010Analysis","volume":"47","author":"Varigonda A. 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