Tong Liu

Yujie Xiao, Qinghao Zhao, Gongzheng Tang et al.

CAD remains a major global public health burden, yet scalable screening tools are limited. Although CCTA is a first-line non-invasive diagnostic modality, its use is constrained by resource requirements and radiation exposure. AI-ECG may offer a complementary approach for CAD risk stratification. In this multicenter study, we developed and validated an AI-ECG model using CCTA as the anatomical reference standard to predict vessel-specific coronary stenosis. In internal validation, the model achieved AUC values of 0.683-0.744 across vessels and showed consistent external performance. Discrimination was maintained in clinically normal ECGs and remained broadly stable across subgroups. Model-predicted probabilities increased monotonically with CCTA-defined stenosis severity. Model probabilities were converted into vessel-specific low-, intermediate-, and high-risk strata using predefined sensitivity- and specificity-based thresholds. Calibration analysis showed agreement between predicted and observed risk, while DCA indicated net clinical benefit over treat-all and treat-none strategies. Integrating AI-derived risk strata with guideline-based PTP categories improved rule-out performance, reduced the gray-zone proportion, and achieved positive NRI compared with PTP alone. In a longitudinal follow-up cohort, Kaplan-Meier analysis showed clear separation of major adverse cardiovascular event risk across model-defined risk groups. Waveform- and attribution-based analyses further identified structured ECG morphology differences and physiologically meaningful signal regions associated with high-risk predictions. These findings support AI-ECG as a feasible tool for complementary CAD screening, anatomical risk estimation, and clinical triage, while prospective studies are needed to confirm its clinical impact.

Deyun Zhang, Shijia Geng, Yang Zhou et al.

The artificial intelligence (AI) system has achieved expert-level performance in electrocardiogram (ECG) signal analysis. However, in underdeveloped countries or regions where the healthcare information system is imperfect, only paper ECGs can be provided. Analysis of real-world ECG images (photos or scans of paper ECGs) remains challenging due to complex environments or interference. In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images. The system was evaluated on a large dataset of 52,357 patients from multiple regions and populations across the world. On the detection task, the AI system obtained area under the receiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test 1), 0.984 (external test 2), and 0.979 (external test 3), respectively. Meanwhile, the detection results of AI system showed a strong correlation with the diagnosis of cardiologists (cardiologist 1 (R=0.794, p<1e-3), cardiologist 2 (R=0.812, p<1e-3)). On the screening task, the AI system achieved AUCs of 0.894 (hold-out test) and 0.850 (external test). The screening performance of the AI system was better than that of the cardiologists (AI system (0.846) vs. cardiologist 1 (0.520) vs. cardiologist 2 (0.480)). Our study demonstrates the feasibility of an accurate, objective, easy-to-use, fast, and low-cost AI system for CA detection and screening. The system has the potential to be used by healthcare professionals, caregivers, and general users to assess CAs based on real-world ECG images.

Nanyu Li, Yujuan Si, Di Wang et al.

Feature extraction plays an important role in Electrocardiogram (ECG) Beats classification system. Compared to other popular methods, VQ method performs well in feature extraction from ECG with advantages of dimensionality reduction. In VQ method, a set of dictionaries corresponding to segments of ECG beats is trained, and VQ codes are used to represent each heartbeat. However, in practice, VQ codes optimized by k-means or k-means++ exist large quantization errors, which results in VQ codes for two heartbeats of the same type being very different. So the essential differences between different types of heartbeats cannot be representative well. On the other hand, VQ uses too much data during codebook construction, which limits the speed of dictionary learning. In this paper, we propose a new method to improve the speed and accuracy of VQ method. To reduce the computation of codebook construction, a set of sparse dictionaries corresponding to wave segments of ECG beats is constructed. After initialized, sparse dictionaries are updated efficiently by Feature-sign and Lagrange dual algorithm. Based on those dictionaries, a set of codes can be computed to represent original ECG beats.Experimental results show that features extracted from ECG by our method are more efficient and separable. The accuracy of our method is higher than other methods with less time consumption of feature extraction

Xiaohang Fu, Tong Liu, Zhaohan Xiong et al.

Segmentation of histological images is one of the most crucial tasks for many biomedical analyses including quantification of certain tissue type. However, challenges are posed by high variability and complexity of structural features in such images, in addition to imaging artifacts. Further, the conventional approach of manual thresholding is labor-intensive, and highly sensitive to inter- and intra-image intensity variations. An accurate and robust automated segmentation method is of high interest. We propose and evaluate an elegant convolutional neural network (CNN) designed for segmentation of histological images, particularly those with Masson's trichrome stain. The network comprises of 11 successive convolutional - rectified linear unit - batch normalization layers, and outperformed state-of-the-art CNNs on a dataset of cardiac histological images (labeling fibrosis, myocytes, and background) with a Dice similarity coefficient of 0.947. With 100 times fewer (only 300 thousand) trainable parameters, our CNN is less susceptible to overfitting, and is efficient. Additionally, it retains image resolution from input to output, captures fine-grained details, and can be trained end-to-end smoothly. To the best of our knowledge, this is the first deep CNN tailored for the problem of concern, and may be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment.