Machine learning and facial recognition for down syndrome detection: A comprehensive review
This review article examines advancements in automated facial recognition methods for diagnosing Down syndrome in children, focusing on the integration of machine learning (ML) and deep learning (DL) strategies. Traditionally diagnosed through clinical assessments, Down syndrome, a genetic disorder...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Computers in Human Behavior Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451958825000156 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823861189684232192 |
|---|---|
| author | Khosro Rezaee |
| author_facet | Khosro Rezaee |
| author_sort | Khosro Rezaee |
| collection | DOAJ |
| description | This review article examines advancements in automated facial recognition methods for diagnosing Down syndrome in children, focusing on the integration of machine learning (ML) and deep learning (DL) strategies. Traditionally diagnosed through clinical assessments, Down syndrome, a genetic disorder characterized by distinctive facial features, has benefited from recent advancements in computer vision and artificial intelligence (AI). This paper explores various facial analysis techniques, including deep convolutional neural networks (DCNNs) and hybrid models combining traditional image processing with deep learning. The review highlights the strengths and limitations of these methods, the importance of large and diverse datasets, and the need for models capable of handling variations in lighting, facial angles, and genetic diversity. Additionally, ethical considerations related to privacy, bias, and data diversity are discussed to emphasize the challenges of implementing these technologies in clinical practice. The findings suggest that while AI-driven facial recognition systems hold promise in enhancing diagnostic accuracy, they must be complemented with traditional clinical methods and improved datasets to ensure reliable and equitable healthcare outcomes. |
| format | Article |
| id | doaj-art-7b45f2c7878b4fc7941d107610dfb00c |
| institution | Kabale University |
| issn | 2451-9588 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers in Human Behavior Reports |
| spelling | doaj-art-7b45f2c7878b4fc7941d107610dfb00c2025-02-10T04:34:38ZengElsevierComputers in Human Behavior Reports2451-95882025-03-0117100600Machine learning and facial recognition for down syndrome detection: A comprehensive reviewKhosro Rezaee0Department of Biomedical Engineering, Meybod University, Meybod, IranThis review article examines advancements in automated facial recognition methods for diagnosing Down syndrome in children, focusing on the integration of machine learning (ML) and deep learning (DL) strategies. Traditionally diagnosed through clinical assessments, Down syndrome, a genetic disorder characterized by distinctive facial features, has benefited from recent advancements in computer vision and artificial intelligence (AI). This paper explores various facial analysis techniques, including deep convolutional neural networks (DCNNs) and hybrid models combining traditional image processing with deep learning. The review highlights the strengths and limitations of these methods, the importance of large and diverse datasets, and the need for models capable of handling variations in lighting, facial angles, and genetic diversity. Additionally, ethical considerations related to privacy, bias, and data diversity are discussed to emphasize the challenges of implementing these technologies in clinical practice. The findings suggest that while AI-driven facial recognition systems hold promise in enhancing diagnostic accuracy, they must be complemented with traditional clinical methods and improved datasets to ensure reliable and equitable healthcare outcomes.http://www.sciencedirect.com/science/article/pii/S2451958825000156Down syndrome diagnosisFacial recognition technologyDeep learning modelsMachine learning in healthcareGenetic disorder detectionAutomated facial analysis |
| spellingShingle | Khosro Rezaee Machine learning and facial recognition for down syndrome detection: A comprehensive review Computers in Human Behavior Reports Down syndrome diagnosis Facial recognition technology Deep learning models Machine learning in healthcare Genetic disorder detection Automated facial analysis |
| title | Machine learning and facial recognition for down syndrome detection: A comprehensive review |
| title_full | Machine learning and facial recognition for down syndrome detection: A comprehensive review |
| title_fullStr | Machine learning and facial recognition for down syndrome detection: A comprehensive review |
| title_full_unstemmed | Machine learning and facial recognition for down syndrome detection: A comprehensive review |
| title_short | Machine learning and facial recognition for down syndrome detection: A comprehensive review |
| title_sort | machine learning and facial recognition for down syndrome detection a comprehensive review |
| topic | Down syndrome diagnosis Facial recognition technology Deep learning models Machine learning in healthcare Genetic disorder detection Automated facial analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2451958825000156 |
| work_keys_str_mv | AT khosrorezaee machinelearningandfacialrecognitionfordownsyndromedetectionacomprehensivereview |