ArticleBioengineering (Basel, Switzerland)2024
Intelligent Evaluation Method for Scoliosis at Home Using Back Photos Captured by Mobile Phones.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Vertebra-Level Completeness Analysis in Thoracolumbar Ultrasound Using a YOLO-Based Detection Framework.Sensors (Basel, Switzerland) · 2026Article
- New Sight of Intelligent Algorithm Models and Medical Devices in Bioengineering: Updates and Directions.Bioengineering (Basel, Switzerland) · 2025Article
- Regional Variations in Mechanical Properties of Porcine Leptomeninges.Cyborg and bionic systems (Washington, D.C.) · 2025Article
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Authors and funding
13 authors.
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Abstract
The traditional scoliosis examination based on X-ray film is not suitable for large-scale screening, and it is also not suitable for dynamic evaluation during rehabilitation. Therefore, based on computer vision technology, this paper puts forward an evaluation method of scoliosis with different photos of the back taken by mobile phones, which involves three aspects: first, based on the key point detection model of YOLOv8, an algorithm for judging the type of spinal coronal curvature is proposed; second, an algorithm for evaluating the coronal plane of the spine based on the key points of the human back is proposed, aiming at quantifying the deviation degree of the spine in the coronal plane; third, the measurement algorithm of trunk rotation (ATR angle) based on multi-scale automatic peak detection (AMPD) is proposed, aiming at quantifying the deviation degree of the spine in sagittal plane. The public dataset and clinical paired data (mobile phone photo and X-ray) are used to test. The results show that this method has high accuracy and effectiveness in distinguishing the type of spinal curvature and evaluating the degree of deviation, which is higher than other deep learning algorithms.
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