{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T15:47:17Z","timestamp":1786549637229,"version":"build-2736575974"},"reference-count":58,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019345","name":"King Salman center For Disability Research","doi-asserted-by":"crossref","award":["KSRG-2024-11-D"],"award-info":[{"award-number":["KSRG-2024-11-D"]}],"id":[{"id":"10.13039\/501100019345","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Life"],"abstract":"<jats:p>Background: Down syndrome (DS) is one of the most prevalent chromosomal abnormalities affecting global healthcare. Recent advances in artificial intelligence (AI) and machine learning (ML) have enhanced DS diagnostic accuracy. However, there is a lack of thorough evaluations analyzing the overall impact and effectiveness of AI-based DS diagnostic approaches. Objectives: This review intends to identify methodologies and technologies used in AI-driven DS diagnostics. It evaluates the performance of AI models in terms of standard evaluation metrics, highlighting their strengths and limitations. Methodology: In order to ensure transparency and rigor, the authors followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. They extracted 1175 articles from major academic databases. By leveraging inclusion and exclusion criteria, a final set of 25 articles was selected. Outcomes: The findings revealed significant advancements in AI-powered DS diagnostics across diverse data modalities. The modalities, including facial images, ultrasound scans, and genetic data, demonstrated strong potential for early DS diagnosis. Despite these advancements, this review outlined the limitations of AI approaches. Small and imbalanced datasets reduce the generalizability of the AI models. The authors present actionable strategies to enhance the clinical adoptions of these models.<\/jats:p>","DOI":"10.3390\/life15030390","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T09:04:49Z","timestamp":1740992689000},"page":"390","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7843-2784","authenticated-orcid":false,"given":"Mujeeb","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","family":"Al-Rawashdeh","sequence":"additional","affiliation":[{"name":"Cyber Security Department, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Al Qassim, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5445-7899","authenticated-orcid":false,"given":"Abdul","family":"Sait","sequence":"additional","affiliation":[{"name":"Department of Archives and Communication, Center of Documentation and Administrative Communication, King Faisal University, P.O. 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