Regulating A Thermal Suit Inside the Operating Room -Based Machine Learning Algorithms Using Temperature Sensors

Abstract

The new thermal suit represents an advancement in healthcare, playing a role in managing patient temperature and improving comfort during illness or post-surgery recovery. This artificial intelligence adapts to individual needs, ensuring safety and positive surgical outcomes. This study aims to enhance temperature control in operating rooms using artificial intelligence. Maintaining temperature during surgery is often difficult for teams, affecting outcomes and patient well-being. The goal is to balance between regulating body temperature and reducing heat-related risks Ups and downs. The approach involves designing a suit equipped with sensors MLX 90614 to monitor the body Temperature. Interpret this data using AI, like a Random Forest (RF) algorithm, and adjust the temperature distribution accordingly. A critical step is to use AI algorithms to design and manufacture suits Decisions based on data collected. Testing and research were conducted to show how well the garment retains heat. As shown in the results ratio, with absolute errors ranging from 1.90 to 3.27, squared mean errors Show a level between 18.51 and $\mathbf{18.60}$ for performance improvement. Low error values indicate precise and effective temperature control, reducing the risks of complications associated with improper temperature fluctuations during surgery. The aim is to enhance the accuracy of thermal management, thereby increasing comfort during surgeries and reducing the risks associated with temperature changes. This is a testament to modern healthcare’s unwavering commitment to leveraging technology to achieve tangible results. Improving overall medical services, embracing technology, and ensuring it is used to its full potential to benefit patients and the healthcare industry. The potential positive impact of this technology on patient outcomes can be emphasized, such as reducing recovery time, improving thermal stability during surgeries, and decreasing the likelihood of temperature-related complications. This would highlight the added value of the proposed system in enhancing healthcare quality and achieving better patient outcomes.

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