Citations

Introduction: Recipients of lung transplants are at heightened risk of developing low bone mineral density (BMD). They also have increased risk of developing osteoporosis-related fractures. This rate of BMD decline is not well characterized. The aim of this manuscript is to characterize the rate of BMD decrease after lung transplant. Methodology: This is a preliminary retrospective cohort study of 200 patients who received lung transplants. Each patient had pre-transplant dual energy X-ray absorptiometry (DXA) and post-transplant DXA scans on the same DXA scanner. The BMD at each lumbar vertebra, total hip, and femoral neck were recorded. Generalized linear mixed effects models with random intercepts and random slopes were used to model the rate of change of BMD after transplant, adjusting for time since transplant, sex, and sex*time interaction. Results: We found that men had higher baseline BMD than women at all sites (P<0.05 for all). The rate of BMD decrease was fastest at the femoral neck (P<0.05). Men lost BMD at a faster rate (-5.23 x10-5 g/cm2/day) than women at the femoral neck (-2.22 x10-5 g/cm2 /day). Conclusion: BMD decreases after lung transplant and occurs faster in men than women.

Abstract Artificial intelligence (AI) has the potential to be a disruptive technology in healthcare. Recently, there is increased speculation that AI may be used to replace healthcare providers in the future. To answer this question, we reviewed over 21 000 articles published in medical specialty journals between 2019 and 2021 to evaluate whether these AI models were intended to assist or replace healthcare providers. We also evaluated whether all Food and Drug Administration (FDA)-approved AI models were used to assist or replace healthcare providers. We find that most AI models published in this time period were intended to assist rather than replace healthcare providers, and that most of the published AI models performed tasks that could not be done by healthcare providers.

Knee CT scans are used for planning for total knee arthroplasties in patients who are often simultaneously at risk for frailty fractures due to low bone mineral density. We retrospectively identified 200 patients (85.5% female) with concurrent CT scans of the knee and Dual energy x-ray absorptiometry (DXA). The mean CT attenuation of the distal femur, proximal tibia and fibula, and patella, were calculated using volumetric 3-dimensional segmentation using 3D Slicer. Data were split randomly into training 80% and test 20% datasets. The optimal CT attenuation threshold for the proximal fibula was obtained in the training dataset and evaluated in the test dataset. A support vector machine (SVM) with radial basis function (RBF) using C-classification was trained and tuned using 5-fold cross-validation in the training dataset and then evaluated in the test dataset. The SVM had a higher area-under-the curve (AUC) of 0.937 and better performance to detect osteoporosis/osteopenia than the CT attenuation of the fibula (AUC of 0.717) (P=0.015). Opportunistic screening for osteoporosis/osteopenia could be accomplished using CT scans of the knee.

Purpose To predict whether a patient has osteoporosis/osteopenia using the attenuation of trabecular bone obtained from knee computed tomography (CT) scans. Methods Retrospective analysis of 273 patients who underwent contemporaneous knee CT scans and dual-energy X-ray absorptiometry (DXA) within 1 year. Volumetric segmentation of the trabecular bone of the distal femur, proximal tibia, patella, and proximal fibula was performed to obtain the bone CT attenuation. The data was randomly split into training/validation (78%) and test (22%) datasets and the performance in the test dataset were evaluated. The predictive properties of the CT attenuation of each bone to predict osteoporosis/osteopenia were assessed. Multivariable support vector machines (SVM) and random forest classifiers (RF) were used to predict osteoporosis/osteopenia. Results Patients with a mean age (range) of 67.9 (50–87) years, 85% female were evaluated. Seventy-seven (28.2%) of patients had normal bone mineral density (BMD), 140 (51.3%) had osteopenia, and 56 (20.5%) had osteoporosis. The proximal tibia had the best predictive ability of all bones and a CT attenuation threshold of 96.0 Hounsfield Units (HU) had a sensitivity of .791, specificity of .706, and area under the curve (AUC) of .748. The AUC for the SVM with cubic kernel classifier (AUC = .912) was better than the RF classifier (AUC = .683, P < .001) and better than using the CT attenuation threshold of 96.0 HU at the proximal tibia (AUC = .748, P = .025). Conclusions Opportunistic screening for osteoporosis/osteopenia can be performed using knee CT scans. Multivariable machine learning models are more predictive than the CT attenuation of a single bone.