Deep learning on nonenhanced cardiac MRI data can detect the presence and extent of chronic myocardial infarction. This approach may have potential to reduce use of gadolinium contrast administration. Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for chronic MI delineation via deep learning on non–contrast material–enhanced cardiac cine MRI. Materials and Methods In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating c...

Background: Computed tomography–derived fractional flow reserve (CT-FFR) using on-site machine learning enables identification of both the presence of coronary artery disease and vessel-specific ischemia. However, it is unclear whether on-site CT-FFR improves clinical or economic outcomes when compared with the standard of care in patients with stable coronary artery disease. Methods: In total, 1216 patients with stable coronary artery disease and an intermediate stenosis of 30% to 90% on coronary computed tomographic angiography were randomized to an on-site CT-FFR care pathway using machine learning or to standard care in 6 Chinese medical centers. The primary end point was the proportion of patients undergoing invasive coronary angiography without obstructive coronary artery disease or with obstructive disease who did not undergo intervention within 90 days. Secondary end points included major adverse cardiovascular events, quality of life, symptoms of angina, and medical expenditure at 1 year. Results: Baseline characteristics were similar in both groups, with 72.4% (881/1216) having either typical or atypical anginal symptoms. A total of 421 of 608 patients (69.2%) in the CT-FFR care group and 483 of 608 patients (79.4%) in the standard care group underwent invasive coronary angiography. Compared with standard care, the proportion of patients undergoing invasive coronary angiography without obstructive coronary artery disease or with obstructive disease not undergoing intervention was significantly reduced in the CT-FFR care group (28.3% [119/421] versus 46.2% [223/483]; P <0.001). Overall, more patients underwent revascularization in the CT-FFR care group than in the standard care group (49.7% [302/608] versus 42.8% [260/608]; P =0.02), but major adverse cardiovascular events at 1 year did not differ (hazard ratio, 0.88 [95% CI, 0.59–1.30]). Quality of life and symptoms improved similarly during follow-up in both groups, and there was a trend towards lower costs in the CT-FFR care group (difference, –¥4233 [95% CI, –¥8165 to ¥973]; P =0.07). Conclusions: On-site CT-FFR using machine learning reduced the proportion of patients with stable coronary artery disease undergoing invasive coronary angiography without obstructive disease or requiring intervention within 90 days, but increased revascularization overall without improving symptoms or quality of life, or reducing major adverse cardiovascular events. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03901326.

Coronavirus disease 2019 (COVID-19) induces myocardial injury, either direct myocarditis or indirect injury due to systemic inflammatory response. Myocardial involvement has been proved to be one of the primary manifestations of COVID-19 infection, according to laboratory test, autopsy, and cardiovascular magnetic resonance (CMR). However, the middle-term outcome of cardiac involvement after the patients were discharged from the hospital is yet unknown. The present study aimed to evaluate mid-term cardiac sequelae in recovered COVID-19 patients by CMR A total of 47 recovered COVID-19 patients were prospectively recruited and underwent CMR examination. The CMR protocol consisted of black blood fat-suppressed T2 weighted imaging, T2 star mapping, left ventricle (LV) cine imaging, pre- and post-contrast T1 mapping, and late gadolinium enhancement (LGE). LGE were assessed in mixed both recovered COVID-19 patients and healthy controls. The LV and right ventricle (RV) function and LV mass were assessed and compared with healthy controls. A total of 44 recovered COVID-19 patients and 31 healthy controls were studied. LGE was found in 13 (30%) of COVID-19 patients. All LGE lesions were located in the mid myocardium and/or sub-epicardium with a scattered distribution. Further analysis showed that LGE-positive patients had significantly decreased LV peak global circumferential strain (GCS), RV peak GCS, RV peak global longitudinal strain (GLS) as compared to non-LGE patients (p < 0.05), while no difference was found between the non-LGE patients and healthy controls. Myocardium injury existed in 30% of COVID-19 patients. These patients have depressed LV GCS and peak RV strains at the 3-month follow-up. CMR can monitor the COVID-19-induced myocarditis progression, and CMR strain analysis is a sensitive tool to evaluate the recovery of LV and RV dysfunction.

Current diagnostic assessment tools remain suboptimal in demonstrating complex morphology of congenital heart disease (CHD). This limitation has posed several challenges in preoperative planning, communication in medical practice, and medical education. This study aims to investigate the dimensional accuracy and the clinical value of 3D printed model of CHD in the above three areas. Using cardiac computed tomography angiography (CCTA) data, a patient-specific 3D model of a 20-month-old boy with double outlet right ventricle was printed in Tango Plus material. Pearson correlation coefficient was used to evaluate correlation of the quantitative measurements taken at analogous anatomical locations between the CCTA images pre- and post-3D printing. Qualitative analysis was conducted by distributing surveys to six health professionals (two radiologists, two cardiologists and two cardiac surgeons) and three medical academics to assess the clinical value of the 3D printed model in these three areas. Excellent correlation (r = 0.99) was noted in the measurements between CCTA and 3D printed model, with a mean difference of 0.23 mm. Four out of six health professionals found the model to be useful in facilitating preoperative planning, while all of them thought that the model would be invaluable in enhancing patient-doctor communication. All three medical academics found the model to be helpful in teaching, and thought that the students will be able to learn the pathology quicker with better understanding. The complex cardiac anatomy can be accurately replicated in flexible material using 3D printing technology. 3D printed heart models could serve as an excellent tool in facilitating preoperative planning, communication in medical practice, and medical education, although further studies with inclusion of more clinical cases are needed.