Towards Improved Identification of Vertebral Fractures in Routine Computed Tomography (CT) Scans: Development and External Validation of a Machine Learning Algorithm

Vertebral fractures are the landmark of osteoporosis and are associated with increased risk of further fractures. Yet they commonly go undiagnosed, and the underlying osteoporosis untreated. We developed a machine learning algorithm for automated vertebral fracture detection and demonstrated strong performance on an external validation set of 2000 CT scans. This method can improve the identification and reporting of vertebral fractures by opportunistically screening for them in routine CT scans. ABSTRACTVertebral fractures (VFs) are the hallmark of osteoporosis, being one of the most frequent types of fragility fracture and an early sign of the disease. They are associated with significant morbidity and mortality. VFs are incidentally found in one out of five imaging studies, however, more than half of the VFs are not identified nor reported in patient computed tomography (CT) scans. Our study aimed to develop a machine learning algorithm to identify VFs in abdominal/chest CT scans and evaluate its performance. We acquired two independent data sets of routine abdominal/chest CT scans of patients aged 50  years or older: a training set of 1011 scans from a non-interventional, prospective proof-of-concept study at the Universitair Ziekenhuis (UZ) Brussel and a validation set of 2000 subjects from an observational cohort study at the Hospital of Holbæk. Both data sets were externally reevaluated to identify reference standard VF readings using the Genant semiquantitative (SQ)...
Source: Journal of Bone and Mineral Research - Category: Orthopaedics Authors: Tags: Research Article Source Type: research