Artificial intelligence (AI) is developing rapidly in the medical technology field, particularly in image analysis. ECG-diagnosis is an image analysis in the sense that cardiologists assess the waveforms presented in a 2-dimensional image. We hypothesized that an AI using a convolutional neural network (CNN) may also recognize ECG images and patterns accurately. We used the PTB ECG database consisting of 289 ECGs including 148 myocardial infarction (MI) cases to develop a CNN to recognize MI in ECG. Our CNN model, equipped with 6-layer architecture, was trained with training-set ECGs. After that, our CNN and 10 physicians are tested with test-set ECGs and compared their MI recognition capability in metrics F1 (harmonic mean of precision and recall) and accuracy. The F1 and accuracy by our CNN were significantly higher (83 ± 4%, 81 ± 4%) as compared to physicians (70 ± 7%, 67 ± 7%, P < 0.0001, respectively). Furthermore, elimination of Goldberger-leads or ECG image compression up to quarter resolution did not significantly decrease the recognition capability. Deep learning with a simple CNN for image analysis may achieve a comparable capability to physicians in recognizing MI on ECG. Further investigation is warranted for the use of AI in ECG image assessment.

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Aims The medical need for screening of aortic valve stenosis (AS), which leads to timely and appropriate medical intervention, is rapidly increasing because of the high prevalence of AS in elderly population. This study aimed to establish a screening method using understandable artificial intelligence (AI) to detect severe AS based on heart sounds and to package the built AI into a smartphone-application. Methods and Results In this diagnostic accuracy study, we developed multiple convolutional neural networks (CNNs) using a modified stratified 5-fold cross-validation to detect severe AS in electronic heart sound data recorded at three auscultation locations. Clinical validation was performed with the developed smartphone application in an independent cohort (model establishment: n = 556, clinical validation: n = 132). Our ensemble technique integrating the heart sounds from multiple auscultation locations increased the detection accuracy of CNN model by compensating detection errors. The established smartphone application achieved a sensitivity, specificity, accuracy, and F1 value of 97.6% (41/42), 94.4% (85/90), 95.7% (126/132), and 0.93, respectively, which were higher compared to the consensus of cardiologists (81.0%, 93.3%, 89.4%, and 0.829, respectively), implying a good utility for severe AS screening. The Grad-CAM++ demonstrated that the built AIs could focus on specific heart sounds to differentiate the severity of AS. Conclusions Our CNN model combining multiple auscultation locations and exported on smartphone application could efficiently identify severe AS based on heart sounds. The visual explanation of AI decisions for heart sounds was interpretable. These technologies may support medical training and remote consultations.

Single-lead ICD with atrial-sensing electrodes shows a lower incidence of inappropriate ICD therapy compared with the absence of atrial-sensing electrodes, without additional operative burden or increased complications.

SummaryPulmonary vein isolation (PVI) is a cornerstone therapy in patients with atrial fibrillation (AF). With increasing numbers of PVI procedures, demand arises to reduce the cumulative fluoroscopic radiation exposure for both the physician and the patient. New technologies are emerging to address this issue. Here, we report our first experiences with a new fluoroscopy integrating technology in addition to a current 3D-mapping system. The new fluoroscopy integrating system (FIS) with 3D-mapping was used prospectively in 15 patients with AF. Control PVI cases (n = 37) were collected retrospectively as a complete series. Total procedure time (skin to skin), fluoroscopic time, and dose-area-product (DAP) data were analyzed. All PVI procedures were performed by one experienced physician using a commercially available circular multipolar irrigated ablation catheter. All PVI procedures were successfully undertaken without major complications. Baseline characteristics of the two groups showed no significant differences. In the group using the FIS, the fluoroscopic time and DAP were significantly reduced from 571 ± 187 seconds versus 1011 ± 527 seconds (P = 0.0029) and 4342 ± 2073 cGycm 2 versus 6208 ± 3314 cGycm 2 (P = 0.049), respectively. Mean procedure time was not significantly affected and was 114 ± 31 minutes versus 104 ± 24 minutes (P = 0.23) by the FIS.The use of the new FIS with the current 3D-mapping system enables a significant reduction of the total fluoroscopy time and DAP compared to the previous combination of 3D-mapping system plus normal fluoroscopy during PVI utilizing a circular multipolar irrigated ablation catheter. However, the concomitant total procedure time is not affected. Thus, the new system reduces the radiation exposure for both the physicians and patients. (Int Heart J 2016; 57: 299-303)