# Artificial Intelligence Algorithms for Healthcare
###### Abstract
It highlights integrating these models into hardware, specifically FPGA, to achieve rapid diagnostic results essential in rescue missions. The research demonstrates significant advancements in emergency medical response, offering a practical approach to addressing the challenges of diagnosing neurological complications in critical, time-sensitive situations. The paper \"ENightTrack: Restraint-Free Depth-Camera-Based Surveillance and Alarm System for Fall Prevention Using Deep Learning Tracking\" introduces \"eNightTrack,\" a non-contact, depth-camera-based surveillance and alarm system designed for fall prevention in healthcare settings. Leveraging deep learning for tracking patient movements, the system aims to mitigate fall risks without physical or chemical restraints, which can have negative side effects. The methodology encompasses data collection through role-play scenarios, instance segmentation using a customized YOLOv8 model, multi-object tracking techniques, and an innovative alarm algorithm. The system was tested across 20 scenarios with 307 video fragments, demonstrating high sensitivity and robustness against potential disturbances such as medical staff presence and varying bed heights. The results underscore the system's effectiveness in fall risk detection, with a sensitivity of 96.8% and only five missed warnings out of 154 cases. This indicates a significant advancement in using deep learning for patient monitoring, with potential applications extending beyond fall prevention. The study represents a crucial step towards developing more ethical and practical patient monitoring systems in healthcare environments. The study \"A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis\" presents a novel deep learning framework for diagnosing leukemia, highlighting its critical importance due to the disease's severity and the necessity for early detection. Utilizing advanced segmentation and classification techniques within a deep learning architecture, the study introduces a U-Net model for effective leukemia cell detection and categorization. The methodology involves extensive testing across multiple datasets, achieving notable accuracy in segmentation and classification. The research underscores the framework's superior performance over existing methods, offering a significant tool for healthcare professionals in diagnosing leukemia. The paper contributes substantially to the field by combining deep learning innovations with practical clinical needs, marking a step forward in automated leukemia detection. The paper \"Assessing the Impact of Patient Characteristics on Genetic Clinical Pathways: A Regression Approach\" addresses the variability in treatment responses to high-risk pregnancies in genetic services, focusing on the influence of patient characteristics on clinical pathways. The study employs a retrospective data analysis from three clinical genetics and laboratory units in Italy. It uses statistical modeling to understand how patient demographics affect the clinical pathway and its duration. This approach identifies significant variability in treatments, attributing it to clinical needs and individual patient characteristics. The results highlight the critical role of patient characteristics in determining the clinical pathway length, suggesting that these factors should be considered alongside clinical information to optimize care pathways and resource use. The paper concludes that understanding the impact of patient characteristics on clinical pathways is essential for improving the efficiency and equity of genetic services. The research contributes to the field by providing a framework for analyzing and enhancing the design of clinical pathways in genetic services, with implications for personalized patient care and resource allocation. Conceptualization, D.C. and S.Y. ; methodology, D.C. and S.Y. ; validation, D.C. and S.Y. ; formal analysis, D.C. and S.Y. ; resources, D.C. and S.Y. ; writing--original draft preparation, D.C. and S.Y.