Echocardiography (echo) is a translationally relevant ultrasound imaging modality widely used to assess cardiac structure and function in preclinical models of heart failure (HF) during research and drug development. Though echo is a very valuable tool, the image analysis is a time consuming, resource demanding process, and is susceptible to inter-reader variability. Recent advancements in deep learning have enabled researchers to automate image processing and reduce analysis time and inter-reader variability in the field of medical imaging. In the present study, we developed a fully automated tool - Mouse Echo Neural Net (MENN) - for the analysis of both long axis brightness (B)-mode and short axis motion (M)-mode images of the left ventricle. MENN is a series of fully convolutional neural networks that were trained and validated using manually segmented B-mode and M-mode echo images of the left ventricle. The segmented images were then used to compute cardiac structural and functional metrics. The performance of MENN was further validated in two preclinical models of HF. MENN achieved excellent correlations (Pearson's r = 0.85 to 0.99) and good to excellent agreement between automated and manual analyses. Further inter-reader variability analysis showed that MENN has better agreements with an expert analyst than both a trained analyst and a novice. Notably, the use of MENN reduced manual analysis time by >92%. In conclusion, we developed an automated echocardiography analysis tool that allows for fast and accurate analysis of B-mode and M-mode mouse echo data and mitigates the issue of inter-reader variability in manual analysis.

Research reports on pulmonary function measurements often mention caffeine abstinence as a condition for testing subjects. Yet, the effects of caffeine on respiration are not well documented. This study was intended to investigate the physiological effects of caffeine on respiratory resistance measurements and the necessity of caffeine avoidance in such testing. Thirty-one subjects were administered caffeine in pill form for dosages in the range of 3 -4.5 mg/kg body weight. Respiratory resistance was measured with the Airflow Perturbation Device every 15 minutes after caffeine ingestion until a full hour of elapsed time. No changes were noted in inhalation, exhalation, and average respiratory resistances during the times of measurements. The conclusion is that for low dosages of caffeine, respiratory resistance measurements are not affected by caffeine ingestion prior to testing.