Chuna manual therapy (CMT) is a type of manual medicine practiced by Korean medical doctors (KMDs) 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. 

Cohort/Group Label

Normal Adult Group

Cohort/Group Description

From 100 clinical trial subjects, the following diagnostic data will be collected to be used to evaluate the diagnostic consistency.

1. Manual diagnoses by the Chuna manual therapy (CMT) expert group(n=3) 
2. Radiological diagnoses by the CMT expert group (n=3) 
3. Radiological diagnoses by the CMT non-expert group  (n=3) 
4. Artificial intelligence -based diagnosis by the CMT non-expert group(n=3) 

Not collect nor Archive

Adult male or female aged 20–60 years

This study is an exploratory clinical study targeting the normal condition group and does not go through a sampling method process except for the selection/exclusion criteria.

Gender

Age

Description

1. Adult male or female aged 20–60 years
2. Ability to understand the study protocol and agrees to participation, and who voluntarily signs the IRB-approved consent form
3. No communication problems during the physical examination and X-ray test 

1. Past trauma or surgery that may induce a structural problem in the lumbar spine (e.g., osteoporosis, fracture, and lumbar spine surgery)
2. A history of disease that may induce a structural change in the lumbar spine (e.g., congenital spinal deformity, ankylosing spondylitis, idiopathic scoliosis, and tumor)
3. An obesity factor of mid to high severity based on BMI (>30 kg/m2) that prevents manual spinal diagnosis
4. A history of psychotic disorder, alcohol addiction, or drug addiction
5. Pregnant mothers or women planning pregnancy
6. Other individuals deemed unsuitable for the study by the principal investigator

Within-group concordance between diagnosticians per diagnosis method

After collecting diagnostic data

Between-group concordance per diagnosis method

After collecting diagnostic data