Quinn Nieves (powersand8)

Purpose Given the recent COVID-19 pandemic and its stress on global medical resources, presented here is the development of a machine intelligent method for thoracic computed tomography (CT) to inform management of patients on steroid treatment. Approach Transfer learning has demonstrated strong performance when applied to medical imaging, particularly when only limited data are available. A cascaded transfer learning approach extracted quantitative features from thoracic CT sections using a fine-tuned VGG19 network. The extracted slice features were axially pooled to provide a CT-scan-level representation of thoracic characteristics and a support vector machine was trained to distinguish between patients who required steroid administration and those who did not, with performance evaluated through receiver operating characteristic (ROC) curve analysis. Least-squares fitting was used to assess temporal trends using the transfer learning approach, providing a preliminary method for monitoring disease progression. Results In the task of identifying patients who should receive steroid treatments, this approach yielded an area under the ROC curve of 0.85 ± 0.10 and demonstrated significant separation between patients who received steroids and those who did not. Furthermore, temporal trend analysis of the prediction score matched expected progression during hospitalization for both groups, with separation at early timepoints prior to convergence near the end of the duration of hospitalization. Conclusions The proposed cascade deep learning method has strong clinical potential for informing clinical decision-making and monitoring patient treatment.Purpose The segmentation of brain tumors is one of the most active areas of medical image analysis. While current methods perform superhuman on benchmark data sets, their applicability in daily clinical practice has not been evaluated. In this work, we investigate the generalization behavior of deep neural networks in this scenario. Approach We evaluate the performance of three state-of-the-art methods, a basic U-Net architecture, and a cascadic Mumford-Shah approach. We also propose two simple modifications (which do not change the topology) to improve generalization performance. BAY 43-9006 cell line Results In these experiments, we show that a well-trained U-network shows the best generalization behavior and is sufficient to solve this segmentation problem. We illustrate why extensions of this model in a realistic scenario can be not only pointless but even harmful. Conclusions We conclude from these experiments that the generalization performance of deep neural networks is severely limited in medical image analysis especially in the area of brain tumor segmentation. In our opinion, current topologies are optimized for the actual benchmark data set but are not directly applicable in daily clinical practice. Return-to-sport (RTS) testing after anterior cruciate ligament (ACL) reconstruction (ACLR) surgery has become popular. It has been recommended that such testing should incorporate several domains, or set of tests, but it is unclear which are most associated with a successful RTS. To determine (1) the proportion of patients who can pass a set of self-report and functional tests at 6 months after ACLR; (2) age, sex, and activity level differences between patients who pass and those who do not; and (3) whether specific types of tests are associated with a return to competitive sport at 12 months. Cohort study; Level of evidence, 2. This was a prospective longitudinal study of 450 patients who had primary ACLR. At 6 months postoperatively, patients completed 2 self-report measures, the International Knee Documentation Committee (IKDC) subjective knee form and ACL-Return to Sport after Injury (ACL-RSI) scale, and 3 functional measures single hop and triple crossover hop for distance and isokinetic quadrice met all of the thresholds of the commo