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ISSN : 2092-8475(Print)
ISSN : (Online)
Journal of International Academy of Physical Therapy Research Vol.12 No.1 pp.2261-2266
DOI : https://doi.org/10.20540/JIAPTR.2021.12.1.2261

Deep Learning Frameworks for Cervical Mobilization Based on Website Images

Wansuk Choia, Seoyoon Heob
aDepartment of Physical Therapy, International University of Korea, Jinju, Republic of Korea;
bDepartment of Occupational Therapy, Kyungbok University, Namyangju, Republic of Korea
Seoyoon Heo, OT, ATO, Prof., PhD
Department of Occupational Therapy, School of Medical and Health Care, Kyungbok University, Gyeongbokdae-ro, Jinjeop-eup, Namyangju-si, Gyeonggi-do, Republic of Korea
Tel: 82-31-539-5351
E-mail: prof.heo@gmail.com

Abstract

Background: Deep learning related research works on website medical images have been actively conducted in the field of health care, however, articles related to the musculoskeletal system have been introduced insufficiently, deep learning-based studies on classifying orthopedic manual therapy images would also just be entered. Objectives: To create a deep learning model that categorizes cervical mobilization images and establish a web application to find out its clinical utility. Design: Research and development. Methods: Three types of cervical mobilization images (central posteroanterior (CPA) mobilization, unilateral posteroanterior (UPA) mobilization, and anteroposterior (AP) mobilization) were obtained using functions of ‘Download All Images’ and a web crawler. Unnecessary images were filtered from 'Auslogics Duplicate File Finder' to obtain the final 144 data (CPA=62, UPA=46, AP=36). Training classified into 3 classes was conducted in Teachable Machine. The next procedures, the trained model source was uploaded to the web application cloud integrated development environment (https://ide.goorm.io/) and the frame was built. The trained model was tested in three environments: Teachable Machine File Upload (TMFU), Teachable Machine Webcam (TMW), and Web Service webcam (WSW). Results: In three environments (TMFU, TMW, WSW), the accuracy of CPA mobilization images was 81-96%. The accuracy of the UPA mobilization image was 43~94%, and the accuracy deviation was greater than that of CPA. The accuracy of the AP mobilization image was 65-75%, and the deviation was not large compared to the other groups. In the three environments, the average accuracy of CPA was 92%, and the accuracy of UPA and AP was similar up to 70%. Conclusion: This study suggests that training of images of orthopedic manual therapy using machine learning open software is possible, and that web applications made using this training model can be used clinically.

초록

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