Prediction of Fetal Brain and Heart Abnormalities using Artificial Intelligence Algorithms: A Review

This review examines the use of AI, including ML and DL algorithms, in detecting and predicting congenital fetal anomalies, particularly in brain and heart, through ultrasonography and MRI. It emphasizes the efficiency and quality of these algorithms, the importance of integrating multiple data sources, and the need for human clinical expertise and real-world validation.

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Analysis of Multi-modal Data Through Deep Learning Techniques to Diagnose CVDs: A Review

This review explores the advancements in AI, ML, and DL techniques in cardiology, focusing on data fusion of non-imaging and imaging data for personalized cardiac care. It highlights the unique clinical insights provided by multimodal DL methods, despite challenges in scalability and data integration.

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Applications of ML and DL Algorithms in the Prediction, Diagnosis, and Prognosis of Alzheimer’s Disease

This review covers recent studies on using ML and DL algorithms, such as SVMs, Logistic Regression, Random Forest, CNN, and RNN, to diagnose and assess Alzheimer's progression. It discusses the algorithms' effectiveness, benefits, and drawbacks, emphasizing the need for more data and advanced neuroimaging technologies for future research

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