Интеграция систем искусственного интеллекта в портативные УЗИ-сканеры: возможности для дистанционного контроля восстановительного лечения

Abstract

This paper provides a comprehensive analysis of the technological synergy between portable ultrasound systems (POCUS) and artificial intelligence (AI) algorithms as one of the key drivers of transformation in modern rehabilitation medicine. Particular attention is given to the transition from traditional diagnostic methods to intelligent digital solutions that ensure higher accuracy, speed, and reproducibility of clinical data.

The article elaborates on the principles of automated ultrasound navigation, advanced medical image segmentation techniques, and real-time quantitative analysis of biometric parameters. It describes algorithms capable of automatically recognizing anatomical structures, detecting pathological changes, and generating preliminary diagnostic conclusions, thereby minimizing the influence of the human factor.

The architecture of “smart scanning” systems is examined, including the integration of portable ultrasound devices, cloud computing technologies, and neural network models. This integration enables highly accurate and continuous monitoring of tissue regeneration processes in home settings. It is emphasized that such solutions not only facilitate patient monitoring but also allow for the prompt adjustment of rehabilitation programs based on objective data.

Special attention is paid to the clinical effectiveness of AI-assisted ultrasound in various fields of medicine, including cardiological, neurological, and musculoskeletal rehabilitation. Evidence is presented demonstrating improved diagnostic accuracy, reduced variability in interpretation, and accelerated clinical decision-making through the use of intelligent support systems.

The authors substantiate the need to transition from discrete, episodic in-hospital monitoring to continuous digital tracking of the recovery process, which enhances treatment personalization, reduces the risk of complications, and significantly improves functional outcomes in patients.