Epicardial fat volume and its association with cardiac arrhythmias in CT coronary angiography.

Purpose: This retrospective study aimed to investigate the epicardial fat volume in cardiac computed tomography (CT), its relationship with cardiac arrhythmias, and its correlation with the coronary artery disease reporting and data system (CAD-RADS) score. Material and methods: Ninety-six patients who underwent CT coronary angiography (CTCA) were included in this study. Patient data, including demographic information, clinical history, and imaging data were collected retrospectively. Epicardial fat volume was quantified using a standardised algorithm, the CAD-RADS scoring system was applied to assess the extent of coronary artery disease (CAD). Descriptive statistics, correlation analyses, and receiver operating characteristics methods were used. Results: The study found a significant correlation between epicardial fat volume and CAD-RADS score (r2 = 0.31; p < 0.001), indicating the known influence of epicardial fat on CAD risk. Moreover, patients with higher epicardial fat volumes were more likely to experience cardiac tachyarrhythmia (p < 0.001). Receiver operating characteristic analysis established a threshold value of 123 cm3 for epicardial fat volume to predict tachyarrhythmia with 80% sensitivity (AUC = 0.69). Conclusions: In this study a volume of at least 123 cm3 epicardial fat in native coronary calcium scans is associated with cardiac tachyarrhythmia. In these patients, careful selection of suitable imaging protocols is advised.

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

BACKGROUND AND OBJECTIVES: Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs. METHODS: To improve the efficiency of the segmentation process, we introduced a "human-in-the-loop" segmentation workflow with an active learning approach, looking at five major anatomical structures in the chest (heart, lungs, mediastinum, trachea, and clavicles). This allowed us to decrease the time needed for segmentation by 32% and select the most complex cases to utilize human expert annotators efficiently. After annotation of 2,000 CXRs from different Level 1 medical centers at Charité - University Hospital Berlin, there was no relevant improvement in model performance, and the annotation process was stopped. A 5-layer U-ResNet was trained for 150 epochs using a combined soft Dice similarity coefficient (DSC) and cross-entropy as a loss function. DSC, Jaccard index (JI), Hausdorff distance (HD) in mm, and average symmetric surface distance (ASSD) in mm were used to assess model performance. External validation was performed using an independent external test dataset from Aachen University Hospital (n = 20). RESULTS: The final training, validation, and testing dataset consisted of 1900/50/50 segmentation masks for each anatomical structure. Our model achieved a mean DSC/JI/HD/ASSD of 0.93/0.88/32.1/5.8 for the lung, 0.92/0.86/21.65/4.85 for the mediastinum, 0.91/0.84/11.83/1.35 for the clavicles, 0.9/0.85/9.6/2.19 for the trachea, and 0.88/0.8/31.74/8.73 for the heart. Validation using the external dataset showed an overall robust performance of our algorithm. CONCLUSIONS: Using an efficient computer-aided segmentation method with active learning, our anatomy-based model achieves comparable performance to state-of-the-art approaches. Instead of only segmenting the non-overlapping portions of the organs, as previous studies did, a closer approximation to actual anatomy is achieved by segmenting along the natural anatomical borders. This novel anatomy approach could be useful for developing pathology models for accurate and quantifiable diagnosis.

The Collaborative Research Center FONDA.

Today's scientific data analysis very often requires complex Data Analysis Workflows (DAWs) executed over distributed computational infrastructures, e.g., clusters. Much research effort is devoted to the tuning and performance optimization of specific workflows for specific clusters. However, an arguably even more important problem for accelerating research is the reduction of development, adaptation, and maintenance times of DAWs. We describe the design and setup of the Collaborative Research Center (CRC) 1404 "FONDA -- Foundations of Workflows for Large-Scale Scientific Data Analysis", in which roughly 50 researchers jointly investigate new technologies, algorithms, and models to increase the portability, adaptability, and dependability of DAWs executed over distributed infrastructures. We describe the motivation behind our project, explain its underlying core concepts, introduce FONDA's internal structure, and sketch our vision for the future of workflow-based scientific data analysis. We also describe some lessons learned during the "making of" a CRC in Computer Science with strong interdisciplinary components, with the aim to foster similar endeavors.