Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
Kupsch, E.
Differentiating childhood benign and malignant bone lesions in radiological examination using artificial intelligence
Humanmedizin; [ Diplomarbeit ] Medizinische Universität Graz; 2025. pp. 83
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- Autor*innen der Med Uni Graz:
- Betreuer*innen:
- Dutschke Anja
- Tschauner Sebastian
- Altmetrics:
- Abstract:
- Background: Primary bone tumors in children and adolescents are rare, therefore posing a significant diagnostic challenge. Malignant lesions are critical diagnoses that require intensive treatment and can be fatal. Early detection and reliable assessment of the lesion’s nature are of great clinical importance. The aim of this thesis was to create a dataset of radiographic images of pediatric benign and malignant bone lesions and to use it to evaluate the ability of EfficientNet models (B0 – B7) in classifying lesion dignity. Patients and Methods: The study included patients aged 0 to 19 years who underwent radiographical examination between 01.01.2004 and 30.05.2024. Diagnoses were either confirmed histopathologically or, if biopsy was not necessary on unavailable, made based on characteristic radiological appearance. To minimize confounding factors, only pre-interventional images were included, and those taken after biopsy, surgery, chemotherapy, or radiotherapy were excluded. The final dataset consisted of 800 X-ray images from 228 individual patients. These images were used to train and test EfficientNet variants B0 to B7. Evaluation metrics included accuracy, recall, precision, and F1-score. Additionally, Precision-Recall (PR) and Receiver Operating Characteristic (ROC) curves were created and the area under the curve (AUC) was calculated.