Early diagnosis and initiation of treatment for fresh osteoporotic lumbar vertebral fractures (OLVF) are crucial. Magnetic resonance imaging (MRI) is generally performed to differentiate between fresh and old OLVF. However, MRIs can be intolerable for patients with severe back pain. Furthermore, it is difficult to perform in an emergency. MRI should therefore only be performed in appropriately selected patients with a high suspicion of fresh fractures. As radiography is the first-choice imaging examination for the diagnosis of OLVF, improving screening accuracy with radiographs will optimize the decision of whether an MRI is necessary. This study aimed to develop a method to automatically classify lumbar vertebrae (LV) conditions such as normal, old, or fresh OLVF using deep learning methods with radiography. A total of 3481 LV images for training, validation, and testing and 662 LV images for external validation were collected. Visual evaluation by two radiologists determined the ground truth of LV diagnoses. Three convolutional neural networks were ensembled. The accuracy, sensitivity, and specificity were 0.89, 0.83, and 0.92 in the test and 0.84, 0.76, and 0.89 in the external validation, respectively. The results suggest that the proposed method can contribute to the accurate automatic classification of LV conditions on radiography.

We have proposed a new user interface with workflow customization, implemented and evaluted in Endoscopy Department Mini-PACS that has been introduced and routinely used for two years at Toshiba General Hospital. We have set some task at endoscopy image acquisition units during examinations for two different types of user interfaces and compared performance. One is a command-button based operation using a remote control, and another is that with eight graphic buttons which are displayed on a CRT monitor and can be customized. Results of the two-year study show that mean number of input diagnosis codes per examination with graphic and customized interface is significantly greater than that with conventional interface. Also, mean time to complete one upper gastric endoscopy examination with new user interface is about 17 percent less than that with conventional interface. These result suggest that systems with the visualized and customized operation and feedback encourages physicians to use more functions and to compete tasks more efficiently than systems with conventional command-button based user interfaces.