An intelligent prenatal screening system for the prediction of Trisomy-21

A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery

The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

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, 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.

A machine learning technology to improve the risk of non-invasive prenatal tests

BACKGROUND

Timely and accurate diagnosis of genetic diseases can lead to proper action and prevention of irreparable events.

OBJECTIVE

In this work we propose an integrated genetic-neural network (GNN) to improve the prediction risk of trisomy diseases including Down's syndrome (T21), Edwards' syndrome (T18) and Patau's Syndrome (T13).

METHODS

A dataset including 561 pregnant were created. In this integrated model, the structure and input parameters of the proposed multilayer feedforward network (MFN) were optimized.

RESULTS

The results of execution of the GNN on the testing dataset showed that the developed model can be accurately classify the anomalies from healthy fetus with 97.58% accuracy rate, and 99.44% and 85.65% sensitivity, and specificity, respectively. In the proposed GNN model, the Levenberg Merquident (LM) algorithm, the Radial Basis (Radbas) function from various types of functions were selected by the proposed GA. Moreover, maternal age, Nuchal Translucency (NT), Crown-rump length (CRL), Pregnancy-associated plasma protein A (PAPP-A) were selected by the proposed GA as the most effective factors for classifying the healthfetuses from the cases with fetal disorders.

CONCLUSION

The proposed computerized model increases the diagnostic performance of the physicians especially in the accurate detection of healthy fetus with non - invasive and low - cost treatments.