The image quality of echocardiography is an important factor to affect cardiovascular disease diagnosis. Currently, the deep learning (DL) used in cardiac echocardiogram image quality assess model focus more on evaluating the whole dynamic video, but the outputs revealed less local anatomical details in judging the image quality in heart chambers. This study was aimed to achieve the local part image quality assess, specifically for the five locals in the left ventricle of A4C section for myocardium. The object detection model, YOLOv8 (You Only Look Once), were used to crop five local parts in the left ventricle myocardium of A4C section. Then, the ResNet-18 model was used to evaluate the image quality of each cropped part, that output from score 0 to 3, four quality levels. The YOLOv8 model demonstrated exceptional performance metrics with Precision of 98.77%, Recall of 98.84%, mAP50 of 98.95%, and mAP50-90 of 81.33%. Additionally, the model exhibited an average Inference Time of 215ms per frame. Comparatively, the ResNet-18 model achieved Accuracy scores of 79.34%, 82.41%, 77.82%, 82.33%, and 78.13%, which correspond to the assessment of the left ventricular myocardium in all five local A4C views. The aggregate performance of the ResNet-18 model was characterized by average Macro Precision of 66.77%, Recall of 59.89%, and F1 Score of 59.49%. Furthermore, the model displayed average Micro Precision of 67.42%, Recall of 70.00%, and F1 Score of 69.98%. This study determined the effectiveness of YOLOv8 to find the bounding box of local myocardium and ResNet-18 for real-time automatic quality assessment, and had the potential to improve the efficiency of diagnosis for the doctor using echocardiogram.

As chip manufacturing approaches physical limits, the probability of defects due to specific chip layout structures has significantly increased. These defect-prone structures are known as lithographic hotspots. Pattern matching method is widely used in hotspot detection algorithms due to its efficiency and accuracy. However, traditional pattern matching algorithms face major challenges in both solution efficiency and flexibility for fuzzy matching. To overcome these limitations, an integer range-based deterministic finite automaton (r-DFA)-based layout pattern matching method supporting parallelization and fuzzy matching is proposed. Manhattan polygons in the layout are represented as multiple path strings, thereby transforming the pattern matching problem into a string regular expression search problem. To simplifies the construction of large integer range elements in fuzzy matching, the r-DFA is employed, enhancing construction efficiency and enabling the algorithm to achieve linear time complexity. Moreover, this approach focuses most of the matching process within each individual layout polygon, enabling parallelized matching across diverse layout polygons. Compared to the state-of-the-art, our approach supports fuzzy matching, and shows an average efficiency improvement of 1.23 times.