A deep learning model detected and classified lumbar spinal stenosis at MRI comparable with radiologists for central canal and lateral recess stenosis, with slightly lower agreement for neural fora... Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve ­productivity and the consistency of reporting. Purpose To develop a DL mod...

Radiologists assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for a... Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) coul...

We propose an interpretable and lightweight 3D deep neural network model that diagnoses anterior cruciate ligament (ACL) tears from a knee MRI exam. Previous works focused primarily on achieving better diagnostic accuracy but paid less attention to practical aspects such as explainability and model size. They mainly relied on ImageNet pre-trained 2D deep neural network backbones, such as AlexNet or ResNet, which are computationally expensive. Some of them tried to interpret the models using post-inference visualization tools, such as CAM or Grad-CAM, which lack in generating accurate heatmaps. Our work addresses the two limitations by understanding the characteristics of ACL tear diagnosis. We argue that the semantic features required for classifying ACL tears are locally confined and highly homogeneous. We harness the unique characteristics of the task by incorporating: 1) attention modules and Gaussian positional encoding to reinforce the seeking of local features; 2) squeeze modules and fewer convolutional filters to reflect the homogeneity of the features. As a result, our model is interpretable: our attention modules can precisely highlight the ACL region without any location information given to them. Our model is extremely lightweight: consisting of only 43 K trainable parameters and 7.1 G of Floating-point operations per second (FLOPs), that is 225 times smaller and 91 times lesser than the previous state-of-the-art, respectively. Our model is accurate: our model outperforms the previous state-of-the-art with the average ROC-AUC of 0.983 and 0.980 on the Chiba and Stanford knee datasets, respectively.

A deep learning model trained for pneumothorax detection with a large dataset of chest radiographs generalized well to multiple external datasets that were independent of the training data source. Purpose To assess the generalizability of a deep learning pneumothorax detection model on datasets from multiple external institutions and examine patient and acquisition factors that might influen...

As coronavirus disease 2019 (COVID-19) infection spreads globally, the demand for chest imaging will inevitably rise with an accompanying increase in risk of disease transmission to frontline radiology staff. Radiology departments should implement strict infection control measures and robust operational plans to minimize disease transmission and mitigate potential impact of possible staff infection. In this article, the authors share several operational guidelines and strategies implemented in our practice to reduce spread of COVID-19 and maintain clinical and educational needs of a teaching hospital.

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.