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Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to “learn” from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. We developed RIL-Contour to accelerate medical image annotation for and with deep-learning. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly annotate medical imaging. RIL-Contour supports using fully automated deep-learning methods, semi-automated methods, and manual methods to annotate medical imaging with voxel and/or text annotations. To reduce annotation error, RIL-Contour promotes the standardization of image annotations across a dataset. RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). The underlying concept of AID is to iteratively annotate, train, and utilize deep-learning models during the process of dataset annotation and model development. To enable this, RIL-Contour supports workflows in which multiple-image analysts annotate medical images, radiologists approve the annotations, and data scientists utilize these annotations to train deep-learning models. To automate the feedback loop between data scientists and image analysts, RIL-Contour provides mechanisms to enable data scientists to push deep newly trained deep-learning models to other users of the software. RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers.

Background Evaluation of corrected flow time (FTc) via ultrasonography is one of the suggested modalities for the assessment of intravascular volume status. This study aimed to compare the results of FTc of carotid artery measured via ultrasonography, as a measure of mechanical outcome of the cardiac cycle, with the results of FTc estimation from a new modified formula via electrocardiography (ECG), as a measure of electrical function of the cardiac cycle. Methods Healthy volunteers were evaluated before and after a passive leg raising (PLR) maneuver. FTc was measured concurrently before and after PLR via a modified method from ECG and via ultrasonography of the carotid artery. Results A total number of 98 healthy volunteers (51 women and 47 men) with a mean age of 30.69 ± 6.28 years were included. There was a significant correlation between FTc measured by ultrasonography and estimated by ECG both before PLR and after PLR (r = .878, p