Key result
IoMT and machine learning integration enhances CPR quality through real-time monitoring and adaptive feedback.
Why the study?
Manual CPR and automated devices improve survival but have limitations like rescuer fatigue and lack of real-time feedback; integrating IoMT and ML may enhance CPR outcomes but faces challenges such as interoperability and data security.
Does the integration of Internet of Medical Things (IoMT) and Machine Learning (ML) improve cardiopulmonary resuscitation (CPR) performance and outcomes compared to conventional techniques?
Does the integration of Internet of Medical Things (IoMT) and Machine Learning (ML) improve cardiopulmonary resuscitation (CPR) performance and outcomes compared to conventional techniques?
Integrating IoMT and ML into CPR systems could revolutionize resuscitation by providing real-time, adaptive feedback, provided interoperability and data security barriers are overcome.
This review explores the pivotal role of cardiopulmonary resuscitation (CPR) in the chain of survival during cardiac events and delves into the challenges and advancements in CPR techniques and technologies. While manual interventions and automated devices have improved survival rates, they present limitations such as rescuer fatigue and lack of real-time feedback. The emergence of the Internet of Medical Things (IoMT) and machine learning (ML) algorithms offers transformative opportunities to enhance CPR rescue efforts by facilitating real-time data acquisition, remote monitoring, and adaptive feedback. However, challenges including interoperability and data security must be addressed for effective integration. The study discusses major findings from related literature, gaps in research, and future directions, highlighting the potential of integrating IoMT and ML to improve CPR outcomes and revolutionize healthcare delivery. Finally, it concludes with recommendations for optimizing CPR strategies and advancing technology for better patient outcomes.
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Chaitanya Vijaykumar Mahamuni (2024) conducted a review in Cardiac arrest. Integration of Internet of Medical Things (IoMT) and Machine Learning (ML) vs. Manual CPR or non-integrated automated CPR devices was evaluated. The integration of Internet of Medical Things and Machine Learning presents transformative opportunities to enhance CPR performance by facilitating real-time data acquisition and adaptive feedback.
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