{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:45:28Z","timestamp":1779201928673,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. The proposed method transforms one-dimensional (1D) patient data\u2014including features such as nuchal translucency (NT), human chorionic gonadotropin (hCG), and pregnancy-associated plasma protein A (PAPP-A)\u2014into two-dimensional (2D) Aztec barcode images, enabling advanced feature extraction using transformer-based deep learning models. Methods: The dataset consists of 958 anonymous patient records. Each record includes four first trimester screening markers, hCG, PAPP-A, and NT, expressed as multiples of the median. The DS risk outcome was categorized into three classes: high, medium, and low. Three transformer architectures\u2014DeiT3, MaxViT, and Swin\u2014are employed to extract high-level features from the generated barcodes. The extracted features are combined into a unified set, and dimensionality reduction is performed using two feature selection techniques: minimum Redundancy Maximum Relevance (mRMR) and RelieF. Intersecting features from both selectors are retained to form a compact and informative feature subset. The final features are classified using machine learning algorithms, including Bagged Trees and Naive Bayes. Results: The proposed approach achieved up to 100% classification accuracy using the Naive Bayes classifier with 1250 features selected by RelieF and 527 intersecting features from mRMR. By selecting a smaller but more informative subset of features, the system significantly reduced hardware and processing demands while maintaining strong predictive performance. Conclusions: The results suggest that the proposed hybrid AI method offers a promising and resource-efficient solution for DS risk assessment in first trimester screening. However, further comparative studies are recommended to validate its performance in broader clinical contexts.<\/jats:p>","DOI":"10.3390\/diagnostics15121444","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T03:52:28Z","timestamp":1749181948000},"page":"1444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening"],"prefix":"10.3390","volume":"15","author":[{"given":"Emre","family":"Yal\u00e7\u0131n","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, Division of Perinatology, Cukurova University School of Medicine, 01330 Adana, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8009-063X","authenticated-orcid":false,"given":"Serpil","family":"Aslan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, 44210 Malatya, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8264-3899","authenticated-orcid":false,"given":"Mesut","family":"To\u011fa\u00e7ar","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, Faculty of Economics and Administrative Sciences, F\u0131rat University, 23119 Elazig, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00fcleyman Cansun","family":"Demir","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Division of Perinatology, Cukurova University School of Medicine, 01330 Adana, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"839","DOI":"10.4236\/ojog.2015.515119","article-title":"Prenatal Diagnosis of Congenital Malformations in Douala General Hospital","volume":"5","author":"Charlotte","year":"2015","journal-title":"Open J. 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