Deep clustering for abdominal organ classification in ultrasound imaging.

Purpose: The purpose of this study is to examine the utilization of unlabeled data for abdominal organ classification in multi-label (non-mutually exclusive classes) ultrasound images, as an alternative to the conventional transfer learning approach.

Approach: We present a new method for classifying abdominal organs in ultrasound images. Unlike previous approaches that only relied on labeled data, we consider the use of both labeled and unlabeled data.

Deep Learning for the Detection, Localization, and Characterization of Focal Liver Lesions on Abdominal US Images.

Purpose: To train and assess the performance of a deep learning-based network designed to detect, localize, and characterize focal liver lesions (FLLs) in the liver parenchyma on abdominal US images.

Materials And Methods: In this retrospective, multicenter, institutional review board-approved study, two object detectors, Faster region-based convolutional neural network (Faster R-CNN) and Detection Transformer (DETR), were fine-tuned on a dataset of 1026 patients ( = 2551 B-mode abdominal US images obtained between 2014 and 2018). Performance of the networks was analyzed on a test set of 48 additional patients ( = 155 B-mode abdominal US images obtained in 2019) and compared with the performance of three caregivers (one nonexpert and two experts) blinded to the clinical history.

Screening for orthostatic hypotension using home blood pressure measurements.

Objective: Orthostatic hypotension is a common condition associated with adverse cardiovascular and cognitive prognosis. Screening for orthostatic hypotension consists of blood pressure measurements in supine (or sitting) and standing position during clinical consultations. As orthostatic hypotension is a poorly reproducible clinical condition, it is likely that the simple measurement carried out during consultations underestimates the true prevalence of the condition.