Detection and estimation of down syndrome genes by machine learning techniques

Çelik, Enes; İlhan, Hamza Osman; Elbir, Ahmet

doi:10.1109/siu.2017.7960496

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…By making timely and accurate recommendations based on patient data, AI and ML can serve as decision support tools for healthcare professionals [82]. ML models can predict the risk of Down syndrome through incorporating clinical and genomic data analysis, enabling healthcare practitioners to make well-informed decisions about additional diagnostic procedures or specialist referrals [83]. It is significant to remember that a medical practitioner should always validate the final diagnosis [84].…”

Section: Decision Support Systems For Healthcarementioning

confidence: 99%

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

Koul,

Ahmad,

Bhat

et al. 2023

Biomedicines

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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.

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Section: Decision Support Systems For Healthcarementioning

confidence: 99%