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초록정보 내용
Objectives
This study aims to establish a deep learning-based diagnostic protocol for identifying lumbar malposition on lumbar spine X-ray images in Chuna manual therapy. The goal is to replace subjective palpation-dependent diagnosis with objective, reproducible radiographic assessment.
Methods
Radiographic biomarkers-including vertebral corner points, pedicle margins, and lumbosacral alignment indicators-will be manually annotated by experts and used to train convolutional neural network models. The dataset will be divided by patient into training, validation, and test sets. Landmark detection accuracy and malposition classification will be evaluated using mean absolute error, intraclass correlation coefficients, accuracy, F1-score, Area Under the Curve(AUC), and Cohen's kappa.
Results
The proposed model is expected to detect anatomical landmarks with high agreement to expert annotations and classify malposition types with clinically acceptable performance. This protocol may provide a standardized, quantitative framework for lumbar alignment assessment in Chuna manual therapy.
Conclusions
This study established a deep learning-based diagnostic protocol for the automated detection of radiographic biomarkers and the quantification of lumbar displacements in Chuna manual therapy. By providing an objective and reproducible evaluation system, this protocol is expected to enhance diagnostic reliability and support evidence-based clinical practice in Korean medicine.
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