RESUMEN
Lung ultrasound (LUS) is possibly the only medical imaging modality which could be used for continuous and periodic monitoring of the lung. This is extremely useful in tracking the lung manifestations either during the onset of lung infection or to track the effect of vaccination on lung as in pandemics such as COVID-19. There have been many attempts in automating the classification of the severity of lung involvement into various classes or automatic segmentation of various LUS landmarks and manifestations. However, all these approaches are based on training static machine learning models which require a significantly large clinically annotated dataset and are computationally heavy and are most of the time non-real time. In this work, a real-time light weight active learning-based approach is presented for faster triaging in COVID-19 subjects in resource constrained settings. The tool, based on the you look only once (YOLO) network, has the capability of providing the quality of images based on the identification of various LUS landmarks, artefacts and manifestations. This tool also predict the severity of lung infection and make use of the possibility of active learning based on the feedback from clinicians or on the image quality. The capability of this tool to summarize the significant frames which are having high severity of infection and high image quality will be helpful for clinicians to discern things more easily. The results show that the proposed object detection tool has a mean average precision (mAP) of 66% at an Intersection over Union (IoU) threshold of 0.5 for the prediction of LUS landmarks with initial training on less than 1000 images. The 14MB lightweight YOLOv5s network achieves 123 FPS while running on a Quadro P4000 GPU. The tool is available for usage and analysis upon request from the authors and details can be found online.