Comparison of concordance between chuna manual therapy diagnosis methods (palpation, X-ray, artificial intelligence program) in lumbar spine: An exploratory, cross-sectional, prospective observational study protocol.

Abstract

Introduction:Chuna manual therapy (CMT) is a type of manual medicine practiced by Korean medical doctors in South Korea. Spinal diagnosis in CMT uses a system that applies manual diagnostic and X-ray tests to detect specific vertebral malpositions, based on the relative alignment across vertebral bodies. Recently, artificial intelligence (AI) programs have been developed to assist in the radiological diagnosis of CMT using X-ray images. Nevertheless, a few clinical studies have reported on the concordance between diagnosticians, diagnostics methodologies, and the use of AI programs for diagnosing CMT. At present, the evidence to support CMT diagnosis is insufficient. This study thus aims to overcome such limitations by collecting and comparing CMT diagnostic data from experts and non-experts through manual diagnosis, X-ray test, and images obtained using an AI program. The study aims to search for CMT diagnosis methods with more outstanding rationality and consistency and to explore the potential use of AI-based CMT diagnosis programs.Methods/design:This study will be conducted as an exploratory, cross-sectional, prospective observational study that will recruit 100 non-specialist subjects. Each subject will submit a signed consent after the screening test and undergo L-spine standing AP & lateral X-ray imaging. Manual CMT diagnosis will be performed by 3 CMT experts according to the standard operation procedure (SOP). The X-ray images of the 100 subjects will subsequently be used to make the CMT radiological diagnoses according to the same SOP by the CMT expert group (n = 3) and CMT non-expert group (n = 3). Among the subjects, those in the non-expert group will receive another CMT radiological diagnosis with spinal data obtained using the AI program, approximately 1 month from after initial diagnosis.Based on the collected diagnostic data, within- and between-group concordance levels will be assessed for each diagnostic method. The verified level of concordance will be used to test the potential use of CMT diagnostic method and CMT AI programs with high levels of rationality and consistency.Ethics and dissemination:This trial has received complete ethical approval from the Wonkwang University Korean Medicine Hospital (IRB 2021–8). We intend to submit the results of the trial to a peer-reviewed journal and/or conferences.Trial registration:https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&search_page=M&pageSize=10&page=undefined&seq=20613&status=5&seq_group=20613, Identifier: KCT0006707.