Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Biomedicines · 7 authors, 7 centres

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This narrative review examines Down syndrome (trisomy 21) across genetics, epidemiology, clinical manifestations, comorbidities, and diagnostic approaches, including emerging AI and machine learning applications. Key cited findings include high prevalence of congenital heart defects (72.2%) in Down syndrome infants, increased seizure prevalence (8.1–26%) compared to the general population (1.5–5%), and elevated dementia risk in older individuals with late-onset epilepsy. The review also discusses AI and ML applications for identifying genetic markers, predicting obstructive sleep apnea risk, and improving educational interventions. Limitations include that this is a narrative synthesis without original patient data, and comorbidity prevalence varies across geographical populations.

STUDY TYPE

narrative_review

SPECIALTY

neurology

SUMMARISED BY

AI pipeline

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ORIGINAL PAPER

PMC10741860

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Biomedicines

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ABSTRACT

Abstract 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. Keywords: Down syndrome, neurodevelopment, cognitive impairment, comorbidity, diagnosis, management, artificial intelligence, machine learning, neurological disorders, intellectual disability

CITATION EXPORT

Aabid Mustafa Koul, Faisel Ahmad, Abida Bhat et al.. Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis. Biomedicines. (2023). https://doi.org/10.3390/biomedicines11123284

AUTHORS

7 · 7 CENTRES

Aabid Mustafa Koul, Faisel Ahmad, Abida Bhat, Qurat-ul Aein, Ajaz Ahmad, Aijaz Ahmad Reshi, Rauf-ur-Rashid Kaul

1Department of Immunology and Molecular Medicine, Sher-i-Kashmir Institute of Medical Sciences, Srinagar 190006, India
2Department of Zoology, Central University of Kashmir, Ganderbal, Srinagar 190004, India
3Advanced Centre for Human Genetics, Sher-i-Kashmir Institute of Medical Sciences, Srinagar 190011, India
4Department of Human Genetics, Guru Nanak Dev University, Amritsar 143005, Punjab, India; [email protected]
5Departments of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
6Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi Arabia; [email protected]
7Department of Community Medicine, Sher-i-Kashmir Institute of Medical Sciences, Srinagar 190006, India