An Overview on Particle Swarm Optimization Algorithms for Clinical Detection Techniques

Down syndrome (DS) is one of the most prevalent chromosomal abnormalities and represents a significant global healthcare concern, highlighting the need for accurate, efficient, and reliable approaches for early detection and diagnosis. Recent advances in artificial intelligence (AI) and machine learning (ML) have created new opportunities for improving the accuracy and efficiency of DS detection. Among computational optimization approaches, Particle Swarm Optimization (PSO) is a population-based optimization technique in which individual particles improve their positions based on their own experience and information obtained from other particles within the swarm. This cooperative interaction enables particles to explore the search space efficiently and increases the probability of identifying optimal or near-optimal solutions. Such optimization capabilities have potential applications in improving AI- and ML-based diagnostic systems. Despite considerable progress in computational approaches for DS diagnosis, comprehensive evaluations of the overall effectiveness and impact of AI-based detection techniques remain limited. Consequently, more robust analytical and optimization techniques are required to address existing diagnostic limitations and improve the reliability of automated detection systems. This research presents an overview of existing techniques for the detection of Down syndrome using AI, with particular consideration of the potential role of computational optimization approaches such as PSO. It further analyzes existing clinical techniques used for detecting Down syndrome in babies and examines their relationship with emerging AI-assisted diagnostic approaches. The study provides a basis for understanding current developments, identifying limitations in existing detection methods, and highlighting areas where robust AI and optimization techniques could contribute to more accurate and efficient Down syndrome diagnosis.