Jing Lu1, Jing Yang2
1Beidahuang Neuropsychiatric Hospital, Jiamusi, Heilongjiang, China
2Jiamusi Hospital of Traditional Chinese Medicine, Jiamusi, Heilongjiang, China
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.
Deep Learning, Giant Needling Method, Treatment of Stroke and Limb Dysfunction, Clinical Medicine
Jing Lu, Jing Yang. Clinical Study of Deep Learning-Based Giant Needling Combined with Conventional Acupuncture for Treatment of Limb Dysfunction Due to Stroke. International Journal of Frontiers in Medicine (2022), Vol. 4, Issue 6: 5-15. https://doi.org/10.25236/IJFM.2022.040602.
 Kang E J , Ryu H R , Kim Y K , et al. Review of Clinical Research about the Treatment of Hiccup After Stroke with Herbal Medicine - Based on Traditional Chinese Medicine Journals. The Journal of Internal Korean Medicine, 2018, 39(3):323-337.
 Gao M Y , Xie Y M , Zhang Y , et al. Research on clinical application of Fufang Kushen Injection in treating indication(therioma) in real world based on hospital information system database. Chinese Traditional and Herbal Drugs, 2018, 49(13):3143-3147.
 Yuan P , Rezvan A , Li X , et al. Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images. Journal of Clinical Medicine, 2019, 8(8):1159-1174.
 Wang N , Wang J , Wang Y . The Application of Intelligent Control in Clinical Medicine. Matec Web of Conferences, 2019, 267(1):02006-02014.
 Yuan L I , Zhi-Gang L , Nai-Yang G , et al. Applications of Deep Learning in Biological and Medical Data Analysis. Progress in Biochemistry and Biophysics, 2016, 43(5):472-483.
 Lai Y H , Chen W N , Hsu T C , et al. Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning. entific Reports, 2020, 10(1):4679-4688.
 Wu G F , Wang W M . Indication rules of Baliao points based on the clinical literature research. Chinese Acupuncture & Moxibustion, 2019, 39(1):96-102.
 Feng R , Badgeley M , Mocco J , et al. Deep learning guided stroke management: a review of clinical applications.. Journal of Neurointerventional Surgery, 2017,34(1):013355-013362.
 Belyalov F I . Application of prediction scores in clinical medicine. Russian Journal of Cardiology, 2016, 140(12):23-27.
 Naylor C D . On the Prospects for a (Deep) Learning Health Care System. JAMA: The Journal of the American Medical Association, 2018, 320(11):1099-1123.Perng J W , Kao I H , Kung C T , et al. Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning. Journal of Clinical Medicine, 2019, 8(11):1906-1924.
 Ariel Hernán Curiale, Flavio Darío Colavecchia, Mato G . Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI. Computer Methods and Programs in Biomedicine, 2019, 169(10):37.
 Biswas M , Kuppili V , Saba L , et al. Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk. Medical & Biological Engineering & Computing, 2018, 57(7):94-99.
 Rio J M N D , Sen P , Rasheed R , et al. Deep Learning-Based Segmentation and Quantification of Retinal Capillary Non-Perfusion on Ultra-Wide-Field Retinal Fluorescein Angiography. Journal of Clinical Medicine, 2020, 9(8):2537-2544.
 Lee E J , Kim Y H , Kim N , et al. Deep into the Brain: Artificial Intelligence in Stroke Imaging. Journal of Stroke, 2017, 19(3):277-285.
 Li L , Yu F , Shi D , et al. Application of virtual reality technology in clinical medicine. American Journal of Translational Research, 2017, 9(9):3867-3880.
 Wun-Jae K . Knowledge-based diagnosis and prediction using big data and deep learning in precision medicine. Investigative & Clinical Urology, 2018, 59(2):69-74.
 Zhou L Q , Wang J Y , Yu S Y , et al. Artificial intelligence in medical imaging of the liver. World Journal of Gastroenterology, 2019, 25(6):672-682.
 Yang Y J , Bang C S . Application of artificial intelligence in gastroenterology. World Journal of Gastroenterology, 2019, 25(14):31-48.
 Islam M M , Poly T N , Yang H C , et al. Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation. Journal of Clinical Medicine, 2020, 9(4):1-19.
 Ryu, Baek, Gho, et al. Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment. Journal of Clinical Medicine, 2020, 9(2):364-376.
 Campanella G , Hanna M G , Geneslaw L , et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature medicine, 2019, 25(8):1-6.
 Rajalingam B , Priya R . Multimodal Medical Image Fusion based on Deep Learning Neural Network for Clinical Treatment Analysis. International Journal of ChemTech Research, 2018, 11(6):160-176.
 Park S J , Choi K H , Park J , et al. A Study on Spatial Analysis Using R-Based Deep Learning. International Journal of Software Engineering and its Applications, 2016, 10(5):87-94.
 Klang E . Deep learning and medical imaging. Journal of Thoracic Disease, 2018, 10(3):1325-1328.
 Shi B , Grimm L J , Mazurowski M A , et al. Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. Journal of the American College of Radiology, 2018(6):527-534.