Application of Machine Learning in CT Colonography and Radiological Age Assessment: Enhancing Traditional Diagnostics in Radiology
MCML Authors
Philipp Wesp
Dr.
Abstract
Philipp Wesp
Dr.
Abstract
Machine learning can address limitations in radiology where traditional methods fall short, as shown by this work’s focus on two clinical problems: differentiating premalignant from benign colorectal polyps and continuous age prediction through clavicle ossification in CT scans. For colorectal polyps, a random forest classifier and CNN models enabled non-invasive differentiation between benign and premalignant types in CT colonography, potentially supporting more precise cancer prevention. For age assessment, a deep learning model trained on automatically detected clavicle regions achieved superior accuracy compared to human estimates, demonstrating machine learning’s potential to enhance radiological diagnostics in complex cases. (Shortened).
BibTeXKey: Wes24