Clinical Significance of Pose Estimation Methods Compared with Radiographic Parameters in Adolescent Patients with Idiopathic Scoliosis

Abstract

Introduction: Human pose estimation, a computer vision technique that identifies body parts and constructs human body representations from images and videos, has recently demonstrated high performance through deep learning. However, its potential application in clinical photography remains underexplored. This study aimed to establish photographic parameters for patients with adolescent idiopathic scoliosis (AIS) using pose estimation and to determine correlations between these photographic parameters and corresponding radiographic measures. Methods: We conducted a study involving 42 patients with AIS who had undergone spinal correction surgery and conservative treatment. Preoperative photographs were captured using an iPhone 13 Pro mounted on a tripod positioned at the head of an X-ray tube. From the outputs of pose estimation, we derived five photographic parameters and subsequently conducted a statistical analysis to assess their correlations with relevant conventional radiographic parameters. Results: In the sagittal plane, we identified significant correlations between photographic and radiographic parameters measuring trunk tilt angles. In the coronal plane, significant correlations were found between photographic parameters measuring shoulder height and trunk tilt and corresponding radiographic measurements. Conclusions: The results suggest that pose estimation, achievable with common mobile devices, offers potential for AIS screening, early detection, and continuous posture monitoring, effectively mitigating the need for X-ray radiation exposure. Level of Evidence: 3.

Keywords

• adolescent idiopathic scoliosis
• pose estimation
• posture parameters
• deep learning
• clinical photography
• mobile applications
• movenet
• convolutional neural network