Medical staff inspect lumbar X-ray images to diagnose lumbar spine diseases, and the analysis process is currently automated using deep-learning techniques. The detection of landmarks is necessary in the automatic process of localizing the position and identifying the morphological features of the vertebrae. However, detection errors may occur owing to the noise and ambiguity of images, as well as individual variations in the shape of the lumbar vertebrae. This study proposes a method to improve the robustness of landmark detection results. This method assumes that landmarks are detected by a convolutional neural network-based two-step model consisting of Pose-Net and M-Net. The model generates a heatmap response to indicate the probable landmark positions. The proposed method then corrects the landmark positions using the heatmap response and active shape model, which employs statistical information on the landmark distribution. Experiments were conducted using 3600 lumbar X-ray images, and the results showed that the landmark detection error was reduced by the proposed method. The average value of maximum errors decreased by 5.58% after applying the proposed method, which combines the outstanding image analysis capabilities of deep learning with statistical shape constraints on landmark distribution. The proposed method could also be easily integrated with other techniques to increase the robustness of landmark detection results such as CoordConv layers and non-directional part affinity field. This resulted in a further enhancement in the landmark detection performance. These advantages can improve the reliability of automatic systems used to inspect lumbar X-ray images. This will benefit both patients and medical staff by reducing medical expenses and increasing diagnostic efficiency.

Background/Objectives: This study aimed to evaluate digital markers and establish quantitative diagnostic criteria for spinal malpositions in Chuna manual therapy using lumbar X-rays. Methods: A total of 2000 X-ray images were collected from adult patients at the International St. Mary’s Hospital of Catholic Kwandong University. Five Chuna manual medicine experts annotated anatomical landmarks using a digital marker labeling program and diagnosed three types of spinal malpositions: flexion/extension, lateral bending, and rotation. Diagnostic accuracy was evaluated using weighted F1 (F1_W) scores, and the optimal threshold values for each malposition type were determined based on maximum F1_W performance. Results: The results showed high diagnostic performance, with average maximum F1_W scores of 0.76 for flexion/extension, 0.85 for lateral bending, and 0.71 for rotation. Based on this analysis, threshold angles for each type of spinal malposition in Chuna manual diagnosis were determined. Conclusions: This study demonstrates the diagnostic validity of digital marker-based X-ray analysis in Chuna manual therapy and is the first to propose quantitative diagnostic thresholds for spinal malpositions. These findings may serve as a foundation for clinical application in spinal assessment and treatment planning, with further validation studies warranted.