Performance analysis of a deep-learning algorithm to detect the presence of inflammation in MRI of sacroiliac joints in patients with axial spondyloarthritis.

Objectives: To assess the ability of a previously trained deep-learning algorithm to identify the presence of inflammation on MRI of sacroiliac joints (SIJ) in a large external validation set of patients with axial spondyloarthritis (axSpA).

Methods: Baseline SIJ MRI scans were collected from two prospective randomised controlled trials in patients with non-radiographic (nr-) and radiographic (r-) axSpA (RAPID-axSpA: NCT01087762 and C-OPTIMISE: NCT02505542) and were centrally evaluated by two expert readers (and adjudicator in case of disagreement) for the presence of inflammation by the 2009 Assessment of SpondyloArthritis International Society (ASAS) definition. Scans were processed by the deep-learning algorithm, blinded to clinical information and central expert readings.

Opportunistically identifiable vertebral fractures on routine radiological imaging predict mortality: observational cohort study.

Unlabelled: In men and women with opportunistically identifiable vertebral fractures (VFs) on routine CT scans including the chest and/or abdomen, the risk of death is 51% higher than in those with no VF on the CT scan, and 325% higher than an age- and sex-matched general population cohort.

Purpose: There is little knowledge about the risk of death in patients with VFs present on routine radiological imaging. We evaluated the risk of death in men and women aged 50 years or older with opportunistically identifiable VFs on routine CT scans and not treated with osteoporosis medications.

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

Vertebral 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.

External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans.

Unlabelled: The Convolutional Neural Network algorithm achieved a sensitivity of 94% and specificity of 93% in identifying scans with vertebral fractures (VFs). The external validation results suggest that the algorithm provides an opportunity to aid radiologists with the early identification of VFs in routine CT scans of abdomen and chest.

Purpose: To evaluate the performance of a previously trained Convolutional Neural Network (CNN) model to automatically detect vertebral fractures (VFs) in CT scans in an external validation cohort.

Healthcare costs associated with opportunistically identifiable vertebral fractures.

Purpose: Vertebral fractures (VFs) are often available on radiological imaging undertaken during daily clinical work, yet the healthcare cost burden of these opportunistically identifiable fractures has not previously been reported. In this study, we examine the direct healthcare costs of subjects with vertebral fractures available for identification on routine CT scans.

Methods: Thoracolumbar vertebral fractures were identified from 2000 routine CT scans.

Fracture Risk in Men and Women With Vertebral Fractures Identified Opportunistically on Routine Computed Tomography Scans and Not Treated for Osteoporosis: An Observational Cohort Study.

Vertebral fractures (VFs) have been associated with future fractures, yet few studies have evaluated whether this pertains to VFs available for identification on routine radiological imaging. We sought to evaluate the risk of subsequent fractures in subjects with VF identified opportunistically on computed tomography (CT) scans performed as part of routine clinical practice. From the radiology database of Holbæk Hospital we identified the first CT scan including the thorax and/or abdomen of 2000 consecutive men and women aged 50 years or older, performed from January 1, 2010 onward.