Shaikh Mujeeb Ahmed, Al-Rawashdeh Hazim Saleh, Sait Abdul Rahaman Wahab
Department of Basic Medical Science, College of Medicine, AlMaarefa University, Diriyah 13713, Riyadh, Saudi Arabia.
Cyber Security Department, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Al Qassim, Saudi Arabia.
Life (Basel). 2025 Mar 1;15(3):390. doi: 10.3390/life15030390.
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.
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.
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.
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.
唐氏综合征（DS）是影响全球医疗保健的最常见染色体异常之一。人工智能（AI）和机器学习（ML）的最新进展提高了DS的诊断准确性。然而，缺乏对基于AI的DS诊断方法的整体影响和有效性进行全面评估。
本综述旨在确定AI驱动的DS诊断中使用的方法和技术。它根据标准评估指标评估AI模型的性能，突出其优势和局限性。
为确保透明度和严谨性，作者遵循系统评价和荟萃分析的首选报告项目（PRISMA）指南。他们从主要学术数据库中提取了1175篇文章。通过利用纳入和排除标准，最终选定了25篇文章。
研究结果揭示了AI驱动的DS诊断在各种数据模式方面取得的重大进展。这些模式包括面部图像、超声扫描和基因数据，显示出早期DS诊断的强大潜力。尽管有这些进展，本综述概述了AI方法的局限性。小而不均衡的数据集降低了AI模型的通用性。作者提出了可行的策略以促进这些模型在临床上的应用。
