CNN-based evaluation of bone density improves diagnostic performance to detect osteopenia and osteoporosis in patients with non-contrast chest CT examinations. Issue 161 (April 2023)

• Record Type: Journal Article
• Authors: Breit, Hanns-Christian; Varga-Szemes, Akos; Schoepf, U. Joseph; Emrich, Tilman; Aldinger, Jonathan; Kressig, Reto W.; Beerli, Nadine; Andreas Buser, Tobias; Breil, Dieter; Derani, Ihsan; Bridenbaugh, Stephanie; Gill, Callum; Fischer, Andreas M.
• Is Part Of: European journal of radiology. Issue 161(2023)
• Publication Date: 2023-04
• Subjects: Artificial Intelligence -- Bone Density -- Tomography -- X-ray computed -- Bone diseases -- Metabolic -- Osteoporosis -- Deep learning; AI Artificial intelligence -- AUC Area under the curve -- BMD Bone mineral density -- CNN Convolutional Neuronal Network -- CT Computed tomography -- DEXA Dual Energy X-ray Absorptiometry -- HU Hounsfield Unit -- ROC Receiver operating characteristic -- VOI Region/Volume of interest; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2023.110728
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Osteopenia and osteoporosis are a major burden on the health care system due to the complications. Due to increasing workload, osteopenia is often not described by radiologists. AI-based evaluations as second reader allow an increase in the quality of reports. AI-based quantification of vertebral body attenuation is a good parameter for osteopenia. Abstract: Purpose: As osteoporosis is still underdiagnosed by clinicians and radiologists, the aim of the present study was to assess the performance of an Artificial intelligence (AI)-based Convolutional Neuronal Network (CNN)-Algorithm for the detection of low bone density on routine non-contrast chest CT in comparison to clinical reports using DEXA scans as reference. Method: This retrospective cross-sectional study included patients who underwent non-contrast chest CT and DEXA between April 2018 and June 2018 (n = 109, 19 men, mean age: 67.7 years). CT studies were evaluated for thoracic vertebral bone pathologies using a CNN-Algorithm, which calculates the attenuation profile of the spine. The content of the radiological reports was evaluated for the description of osteoporosis or osteopenia. DEXA was used as the reference standard. To estimate correlation the Spearman test was used and the comparison of the different groups was performed using the Wilcoxon rank sum test. Diagnostic was evaluated by performing a receiver operating characteristic curve analysis. Results: The DEXA examination revealed normal bone density in 42 patients, while 49 patients had osteopenia and 7 osteoporosis. There was a statistically significant correlation between the mean CNN-based attenuation of the thoracic spine and the bone density measured on the DEXA in the hip (r = 0.51, p < 0.001) and lumbar spine (r = 0.34, p = 0.01). The mean attenuation was significantly higher in patients with normal bone density (172 ± 44.5 HU) compared to those with osteopenia or osteoporosis (125.2 ± 33.8 HU), (p < 0.0001). Diagnostic performance in distinguishing normal from abnormal bone density was higher using the CNN-based vertebral attenuation (accuracy 0.75, sensitivity: 0.93, specificity: 0.61) compared to clinical reports (accuracy 0.51, sensitivity: 0.14, specificity: 0.53). Conclusion: CNN-based evaluation of bone density may provide additional value over standard clinical reports for the detection of osteopenia and osteoporosis in patients undergoing routine non-contrast chest CT scans. This additional value could improve identification of fracture risk and subsequent treatment.