Proceedings on Automation in Medical Engineering
Vol. 3 No. 1 (2026): Proc AUTOMED
https://doi.org/10.18416/AUTOMED.2026.2486

18th Interdisciplinary AUTOMED Symposium in Collaboration with the TC Medical Robotics, 2486

Anatomical plane multi-view learning for detecting spinal deformities with surface topography

Main Article Content

Thurid Jochim (TUD Dresden University of Technology), Andreas Heinke (TUD Dresden University of Technology), Arkadiusz Żurawski (Jan Kochanowski University – Collegium Medicum), Hagen Malberg (TUD Dresden University of Technology)

Copyright (c) 2026 Proceedings on Automation in Medical Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Detecting functional and structural spinal deformities in children, such as rounded back and scoliosis, is essential to enable timely treatment and reduce the risk of reduced quality of life in adulthood. This study proposes a multi-view binary classifier based on anatomical planes for identifying postural deviations. Tested on 1,477 surface topographical measurements from school children, the model achieved 75% accuracy, 84% specificity, and 64% sensitivity. With further optimization and validation on broader cohorts, this approach shows promosing as a scalable tool for school-based screening programs.

Article Details