In severe cases, the risk of secondary complications, such as scoliosis, lower back pain, and disk problems, increases significantly. Furthermore, leg length discrepancy may introduce twisted posture while standing or walking and bend the spine, which can cause pain during exercise or prolonged walking. When severe malalignment occurs, excessive weight placed on the medial or lateral side of the knee can cause strain on the knee joint, resulting in osteoarthritis. However, minor malalignments in the lower limbs can limit body movements and cause muscle tension. Appropriate alignment of lower limbs is crucial in maintaining correct posture and preventing pain owing to poor posture. When a deformation persists in one part, considerable orthopedic disabilities, such as differences in the lengths of the two legs or bending and twisting of the lower limbs, may occur 1. Lower limbs, which constitute half of the human body, maintain a stable state of movement by absorbing external forces and shocks while supporting body weight during walking or running. In conclusion, the analysis of anteroposterior standing X-ray medical imaging data by the DL-based lower limb alignment diagnostic support system produces measurement results similar to those obtained by radiologists. Furthermore, the average measurement speed was approximately 12 s.
Based on the results and 250 frontal lower limb radiographs obtained from 250 patients, the system measurements for 16 indicators revealed superior reliability (CCC, PCC, and ICC ≤ 0.9 mean absolute error, mean square error, and root mean square error ≥ 0.9) for clinical observations. A radiologist compared the final calculated measurements with the observations in terms of the concordance correlation coefficient (CCC), Pearson correlation coefficient (PCC), and intraclass correlation coefficient (ICC). To precisely detect necessary landmarks, a DL model was implemented stepwise. For this study, leg radiographs of non-overlapping 770 patients were collected from January 2016 to August 2020.
To this end, a deep-learning (DL)-based system was developed to automatically, rapidly, and accurately detect lower limb alignment by using anteroposterior standing X-ray medical imaging data of lower limbs. However, this process is manual, time consuming, and prone to human error. Currently, physicians use physical tools and radiographs to directly observe limb alignment. During clinical evaluation of patients and planning orthopedic treatments, the periodic assessment of lower limb alignment is critical.
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