Key result
A two-stage deep learning approach using YOLOv8 and ResNet-18 achieved 98.77% precision for bounding box detection and up to 82.41% accuracy for automated quality assessment of local left ventricular myocardium regions.
Why the study?
Existing deep learning echocardiogram quality assessment models evaluate whole dynamic videos, revealing fewer local anatomical details for judging image quality in heart chambers.
Does a deep learning pipeline using YOLOv8 and ResNet-18 accurately assess the local image quality of the left ventricular myocardium in A4C echocardiogram views?
Does a deep learning pipeline using YOLOv8 and ResNet-18 accurately assess the local image quality of the left ventricular myocardium in A4C echocardiogram views?
A deep learning pipeline combining YOLOv8 and ResNet-18 can effectively and automatically assess the local image quality of the left ventricular myocardium in A4C echocardiogram views in real-time.
Local LV myocardial quality scoring should not yet change practice; leaves open incremental value over whole-video models.
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.
No takes yet. Share an insight, caveat, or question.
Liu et al. (2025) studied Echocardiogram image quality assessment (n=17,437). YOLOv8 and ResNet-18 deep learning models vs. Expert human labelers (gold standard) was evaluated on Object detection precision and image quality assessment accuracy. A two-stage deep learning approach using YOLOv8 and ResNet-18 achieved 98.77% precision for bounding box detection and up to 82.41% accuracy for automated quality assessment of local left ventricular myocardium regions.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: