Work

Differentiating between structural and functional spinal deformities in children and adolescents using machine learning

Year: 2024

Type: conference-paper

Abstract: : Severe skeletal disorders of children are often mistaken for poor posture, leading to late admittance to treatment and thus limiting treatment outcome and patients’ quality of life. This work proposes a machine learning based methodology to differentiate between structural and functional spinal deformities. Four machine learning algorithms combined with feature engineering were used to train a dataset of 262 rasterstereographic measurements, containing the classes “scoliosis”, “other structural deformities” and “functional deformities”. The overall achieved F1-score was 0.876 with support vector machine. Based on these results, we propose rasterstereography as a novel method for determining the type of spinal deformities.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14277878

Authors: Jochim, T., Żurawski, A., Stecher, N., Heinke, A., Kiebzak, W., Malberg, H.

Institutions: Technische Universität Dresden, Jan Kochanowski University

Language: English

Topic: Scoliosis diagnosis and treatment

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6911882633

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