Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

Abstract

Scoliosis is a disease caused by the spine curving. It is treatable but physiotherapists may do different measurements for curvature angles. That's a problem affecting the treatment planning. This study aims to develop a Deep Learning-based decision support system, which diagnoses scoliosis and plans treatments via Schroth method. The system has an interpretable and explainable CapsNet model processing x-ray image to detect 68-point vertebrae and make Cobb angle measurements. By using angle values and patient parameters, treatment is planned through an automated Schroth definition and the Fuzzy Logic. In the evaluations, the CapsNet had dominating findings (some of them are MSE: 0.0038, PCC: 0.93, Accuracy: 0.98). The Fuzzy Logic model was accurate at exercise plans for past cases. Also, physiotherapists and patients had positive feedback for the system usage, trustworthiness, diagnosis, treatment planning and tracking. As a conclusion, the system ensures advancements for automated diagnosis and treatment of scoliosis.

A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection

“…S. Goral and U. Kose [1] developed a Decision Support System based on DL, capable of diagnosing scoliosis and generating treatment plans following the Schroth method.…”

“…Scoliosis is defined as the deformation of the spine structure caused by 'S' or 'C' shaped curves in the spine [1]. It is a structural deviation of the spine involving lateral and rotational curvatures, frequently emerging in young children around puberty or slightly before, leading to functional impairment.…”