{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T05:40:27Z","timestamp":1787982027181,"version":"build-2784847793"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Salman Center for Disability Research","award":["KSRG-2024-11-D"],"award-info":[{"award-number":["KSRG-2024-11-D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Down syndrome (DS) is one of the prevalent chromosomal disorders, representing distinctive craniofacial features and a range of developmental and medical challenges. Due to the lack of clinical expertise and high infrastructure costs, access to genetic testing is restricted to resource-constrained clinical settings. There is a demand for developing a non-invasive and equitable DS screening tool, facilitating DS diagnosis for a wide range of populations. In this study, we develop and validate a robust, interpretable deep learning model for the early detection of DS using facial images of infants. A hybrid feature extraction architecture combining RegNet X\u2013MobileNet V3 and vision transformer (ViT)-Linformer is developed for effective feature representation. We use an adaptive attention-based feature fusion to enhance the proposed model\u2019s focus on diagnostically relevant facial regions. Bayesian optimization with hyperband (BOHB) fine-tuned extremely randomized trees (ExtraTrees) is employed to classify the features. To ensure the model\u2019s generalizability, stratified five-fold cross-validation is performed. Compared to the recent DS classification approaches, the proposed model demonstrates outstanding performance, achieving an accuracy of 99.10%, precision of 98.80%, recall of 98.87%, F1-score of 98.83%, and specificity of 98.81%, on the unseen data. The findings underscore the strengths of the proposed model as a reliable screening tool to identify DS in the early stages using the facial images. This study paves the foundation to build equitable, scalable, and trustworthy digital solution for effective pediatric care across the globe.<\/jats:p>","DOI":"10.3390\/life15091361","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T15:49:34Z","timestamp":1756309774000},"page":"1361","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning-Powered Down Syndrome Detection Using Facial Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7843-2784","authenticated-orcid":false,"given":"Mujeeb Ahmed","family":"Shaikh","sequence":"first","affiliation":[{"name":"Department of Basic Medical Science, College of Medicine, AlMaarefa University, Diriyah 13713, Riyadh, Saudi Arabia"},{"name":"King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hazim Saleh","family":"Al-Rawashdeh","sequence":"additional","affiliation":[{"name":"King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia"},{"name":"Cyber Security Department, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Qassim, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5445-7899","authenticated-orcid":false,"given":"Abdul Rahaman Wahab","family":"Sait","sequence":"additional","affiliation":[{"name":"King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia"},{"name":"Department of Archives and Communication, Center of Documentation and Administrative Communication, King Faisal University, P.O. Box 400, Hofuf 31982, Al-Ahsa, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shaikh, M.A., Al-Rawashdeh, H.S., and Sait, A.R.W. (2025). A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques. Life, 15.","DOI":"10.3390\/life15030390"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fuc\u00e0, E., Costanzo, F., Celestini, L., Mandarino, A., and Vicari, S. (2021). Characterization of sleep disturbances in children and adolescents with Down syndrome and their relation with cognitive and behavioral features. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18095001"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ijezie, O.A., Healy, J., Davies, P., Balaguer-Ballester, E., and Heaslip, V. (2023). 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