Clinical Study of Deep Learning-Based Giant Needling Combined with Conventional Acupuncture for Treatment of Limb Dysfunction Due to Stroke

Abstract

With the development of computer technology and the deepening of machine learning research, the application prospects of artificial intelligence technology in the medical field are becoming more and more broad. Deep learning algorithm is a method tool that realizes machine learning so as to be as close as possible to artificial intelligence. Its application in clinical medicine is the current hot direction of artificial intelligence in the medical field. Therefore, clinical medical research combining deep learning algorithms with traditional therapies is of great significance to the application and development of deep learning in the medical field. This article uses deep learning algorithms to study a clinical treatment plan that combines the giant needling method in traditional medicine with conventional acupuncture therapy for stroke patients with limb dysfunction. This article mainly uses the convolutional neural network in the deep learning algorithm to establish a tracking prediction model. Through the time-series signal of the neural response of stroke patients, acupoint detection and medical image processing, the study of the giant needling method combined with conventional acupuncture therapy on the limb dysfunction of stroke the clinical treatment effect. This article analyzes the limb function and therapeutic effect of the giant needling method combined with conventional acupuncture therapy before and after the clinical treatment of stroke-related limb dysfunction. This paper studies the advantages and disadvantages of deep learning algorithms and other machine learning algorithms in the clinical data processing of stroke-related limb dysfunction through comparative analysis. Studies have shown that during the six months of stroke recovery, which is mainly based on the treatment of limb dysfunction, deep learning has a prediction accuracy of 91.44% for stroke patients limb dysfunction. Data collection, medical imaging and processing for clinical studies on stroke limb dysfunction the prediction accuracy is far superior to other machine learning algorithms.