Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis
Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, a...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Biomedicines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9059/11/12/3284 |
| _version_ | 1797381845738848256 |
|---|---|
| author | Aabid Mustafa Koul Faisel Ahmad Abida Bhat Qurat-ul Aein Ajaz Ahmad Aijaz Ahmad Reshi Rauf-ur-Rashid Kaul |
| author_facet | Aabid Mustafa Koul Faisel Ahmad Abida Bhat Qurat-ul Aein Ajaz Ahmad Aijaz Ahmad Reshi Rauf-ur-Rashid Kaul |
| author_sort | Aabid Mustafa Koul |
| collection | DOAJ |
| description | Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life. |
| first_indexed | 2024-03-08T20:57:22Z |
| format | Article |
| id | doaj.art-07bebca95e8d4b11bf1167e7f8cefb48 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-08T20:57:22Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-07bebca95e8d4b11bf1167e7f8cefb482023-12-22T13:55:12ZengMDPI AGBiomedicines2227-90592023-12-011112328410.3390/biomedicines11123284Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced DiagnosisAabid Mustafa Koul0Faisel Ahmad1Abida Bhat2Qurat-ul Aein3Ajaz Ahmad4Aijaz Ahmad Reshi5Rauf-ur-Rashid Kaul6Department of Immunology and Molecular Medicine, Sher-i-Kashmir Institute of Medical Sciences, Srinagar 190006, IndiaDepartment of Zoology, Central University of Kashmir, Ganderbal, Srinagar 190004, IndiaAdvanced Centre for Human Genetics, Sher-i-Kashmir Institute of Medical Sciences, Srinagar 190011, IndiaDepartment of Human Genetics, Guru Nanak Dev University, Amritsar 143005, Punjab, IndiaDepartments of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi ArabiaDepartment of Community Medicine, Sher-i-Kashmir Institute of Medical Sciences, Srinagar 190006, IndiaDown syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life.https://www.mdpi.com/2227-9059/11/12/3284Down syndromeneurodevelopmentcognitive impairmentcomorbiditydiagnosismanagement |
| spellingShingle | Aabid Mustafa Koul Faisel Ahmad Abida Bhat Qurat-ul Aein Ajaz Ahmad Aijaz Ahmad Reshi Rauf-ur-Rashid Kaul Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis Biomedicines Down syndrome neurodevelopment cognitive impairment comorbidity diagnosis management |
| title | Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis |
| title_full | Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis |
| title_fullStr | Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis |
| title_full_unstemmed | Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis |
| title_short | Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis |
| title_sort | unraveling down syndrome from genetic anomaly to artificial intelligence enhanced diagnosis |
| topic | Down syndrome neurodevelopment cognitive impairment comorbidity diagnosis management |
| url | https://www.mdpi.com/2227-9059/11/12/3284 |
| work_keys_str_mv | AT aabidmustafakoul unravelingdownsyndromefromgeneticanomalytoartificialintelligenceenhanceddiagnosis AT faiselahmad unravelingdownsyndromefromgeneticanomalytoartificialintelligenceenhanceddiagnosis AT abidabhat unravelingdownsyndromefromgeneticanomalytoartificialintelligenceenhanceddiagnosis AT quratulaein unravelingdownsyndromefromgeneticanomalytoartificialintelligenceenhanceddiagnosis AT ajazahmad unravelingdownsyndromefromgeneticanomalytoartificialintelligenceenhanceddiagnosis AT aijazahmadreshi unravelingdownsyndromefromgeneticanomalytoartificialintelligenceenhanceddiagnosis AT raufurrashidkaul unravelingdownsyndromefromgeneticanomalytoartificialintelligenceenhanceddiagnosis |