Objective Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was developed to automatically measure bone mineral density (BMD) for opportunistic osteoporosis screening using lowdose chest computed tomography (LDCT) scans obtained for lung cancer screening. Methods First, a deep learning model was trained and tested with 200 annotated LDCT scans to segment and label all vertebral bodies (VBs). Then, the mean CT numbers of the trabecular area of target VBs were obtained based on the segmentation mask through geometric operations. Finally, a linear function was built to map the trabecular CT numbers of target VBs to their BMDs collected from approved software used for osteoporosis diagnosis. The diagnostic performance of the developed system was evaluated using an independent dataset of 374 LDCT scans with standard BMDs and osteoporosis diagnosis. Results Our deep learning model achieved a mean Dice coefficient of 86.6% for VB segmentation and 97.5% accuracy for VB labeling. Line regression and Bland-Altman analyses showed good agreement between the predicted BMD and the ground truth, with correlation coefficients of 0.964-0.968 and mean errors of 2.2-4.0 mg/cm 3. The area under the curve (AUC) was 0.927 for detecting osteoporosis and 0.942 for distinguishing low BMD. Conclusion The proposed deep learning-based system demonstrated the potential to automatically perform opportunistic osteoporosis screening using LDCT scans obtained for lung cancer screening. Key Points • Osteoporosis is a prevalent but underdiagnosed condition that can increase the risk of fracture. • A deep learning-based system was developed to fully automate bone mineral density measurement in low-dose chest computed tomography scans. • The developed system achieved high accuracy for automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening. Keywords Bone mineral density. Deep learning. Osteoporosis. Screening Abbreviations AUC Area under the curve BMD Bone mineral density CNN Convolutional neural network CT Computed tomography DL Deep learning DXA Dual-energy X-ray absorptiometry LDCT Low-dose chest computed tomography QA Quality assurance QCT Quantitative computed tomography VB Vertebral body VOI Volume of interest Yaling Pan and Dejun Shi contributed equally to this work.

Purpose To develop and evaluate the effectiveness of a deep learning framework (3D-ResNet) based on CT images to distinguish nontuberculous mycobacterium lung disease (NTM-LD) from Mycobacterium tuberculosis lung disease (MTB-LD). Method Chest CT images of 301 with NTM-LD and 804 with MTB-LD confirmed by pathogenic microbiological examination were retrospectively collected. The differences between the clinical manifestations of the two diseases were analysed. 3D-ResNet was developed to randomly extract data in an 8:1:1 ratio for training, validating, and testing. We also collected external test data (40 with NTM-LD and 40 with MTB-LD) for external validation of the model. The activated region of interest was evaluated using a class activation map. The model was compared with three radiologists in the test set. Result Patients with NTM-LD were older than those with MTB-LD, patients with MTB-LD had more cough, and those with NTM-LD had more dyspnoea, and the results were statistically significant ( p < 0.05). The AUCs of our model on training, validating, and testing datasets were 0.90, 0.88, and 0.86, respectively, while the AUC on the external test set was 0.78. Additionally, the performance of the model was higher than that of the radiologist, and without manual labelling, the model automatically identified lung areas with abnormalities on CT > 1000 times more effectively than the radiologists. Conclusion This study shows the efficacy of 3D-ResNet as a rapid auxiliary diagnostic tool for NTB-LD and MTB-LD. Its use can help provide timely and accurate treatment strategies to patients with these diseases. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05432-x.

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The present study aimed to investigate the visual preference for repetitive movements in children with autism spectrum disorder (ASD). Young children with ASD and typically-developing (TD) children were presented simultaneously with cartoons depicting repetitive and random movements respectively, while their eye-movements were recorded. We found that: (1) the children with ASD spent more time fixating on the repetitive movements than the random movements, whereas the TD children showed no preference for either type of movements; (2) the children's preference for the repetitive movements was correlated with the parent reports of their repetitive behaviors. Our findings show a promise in using the preferential looking as a potential indicator for the repetitive behaviors and aiding early screening of ASD in future investigations.

Objective: To investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs. Methods: We retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.8mm, 0.4mm, 0.18 mm) were obtained by reconstructing the single HRCT scan using three combinations of field of view and matrix size. For each pixel size setting, radiomic features were extracted for all GGNs and radiomic signatures for the invasiveness of GGNs were built through two modeling pipelines for comparison. Results: The study finally extracted 788 radiomic features. 87% radiomic features demonstrated inter pixel size variation. By either modeling pipeline, the radiomic signature under small pixel size performed significantly better than those under middle or large pixel sizes in predicting the invasiveness of GGNs (p’s value <0.05 by Delong test). With the independent modeling pipeline, the three pixel size bounded radiomic signatures shared almost no common features. Conclusions: Reducing pixel size could cause inconsistency in most radiomic features and improve the diagnostic performance of the downstream radiomic signatures. Particularly, super HRCTs with small pixel size resulted in more accurate radiomic signatures for the invasiveness of GGNs. Advances in knowledge: The dependence of radiomic features on pixel size will affect the performance of the downstream radiomic signatures. The future radiomic studies should consider this effect of pixel size.