Decision Tree Analysis for Point-of-Care Ultrasound Imaging: Precision in Constrained Healthcare Settings

The study develops an intelligent decision support system using point-of-care ultrasound imaging. The system’s primary emphasis is on tackling the issues that might arise in healthcare settings with limited available resources. The gadget can make educated judgments at important junctures during image capture and processing thanks to the use of a decision tree algorithm that is incorporated into a portable ultrasound device that is powered by a Raspberry Pi. The decision tree model was trained using a dataset including various medical situations. It guarantees appropriate settings and image quality management, which contributes to an increase in diagnosis accuracy. The user’s interactions and inputs are intelligently directed to provide a better experience for the user as a whole. Adaptive learning and continuous optimization are made possible by continual data gathering and user input, as well as retraining of the decision tree model. The dependability of the device and its conformance to medical standards are both ensured by the integration of safety features and concerns for regulatory compliance. This system has the potential to make economical, high-quality ultrasound imaging more accessible and impactful in underprivileged healthcare situations, eventually leading to improvements in the quality of healthcare received by patients.