The potential clinical utility of an artificial intelligence model for identification of vertebral compression fractures in chest radiographs.

Abstract

Purpose: To assess the ability of the Annalise Enterprise CXR Triage Trauma artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undiagnosed osteoporosis and its treatment.

Materials and methods: This retrospective study used a consecutive cohort of 596 chest radiographs from four U.S. hospitals between 2015 and 2021. Each radiograph included both frontal (anteroposterior or posteroanterior) and lateral projections. These radiographs were assessed for the presence of vertebral compression fracture in a consensus manner by up to three thoracic radiologists. The model then performed inference on the cases. A chart review was also performed for the presence of osteoporosis-related ICD-10 diagnostic codes and medication use for the study period and an additional year of follow up.

Results: The model successfully completed inference on 595 cases (99.8%); these cases included 272 positive cases and 323 negative cases. The model performed with area under the receiver operating characteristic curve of 0.955 (95% CI: 0.939 to 0.968), sensitivity 89.3% (95% CI: 85.7 to 92.7%) and specificity 89.2% (95% CI: 85.4 to 92.3%). Out of the 236 true-positive cases (i.e., correctly identified vertebral compression fractures by the model) with available chart information, only 86 (36.4%) had a diagnosis of vertebral compression fracture and 140 (59.3%) had a diagnosis of either osteoporosis or osteopenia; only 78 (33.1%) were receiving a disease modifying medication for osteoporosis.

Conclusion: The model identified vertebral compression fracture accurately with a sensitivity 89.3% (95% CI: 85.7 to 92.7%) and specificity of 89.2% (95% CI: 85.4 to 92.3%). Its automated use could help identify patients who have undiagnosed osteoporosis and who may benefit from taking disease modifying medications.