The COVID-19 pandemic has accelerated the need for automatic triaging and summarization of ultrasound videos for fast access to pathologically relevant information in the Emergency Department and lowering resource requirements for telemedicine. In this work, a PyTorch based unsupervised reinforcement learning methodology which incorporates multi feature fusion to output classification labels, segmentation maps and summary videos for lung ultrasound is presented. The use of unsupervised training eliminates tedious manual labeling of key-frames by clinicians opening new frontiers in scalability in training using unlabeled or weakly labeled data. Our approach was benchmarked against expert clinicians from different geographies displaying superior Precision and F1 scores (over 80% and 44%).

This letter presents a double-tuned dual input transmitter coil operating at 13.56 MHz and 40.68 MHz industrial, scientific, and medical (ISM) bands for multisite biomedical applications. The proposed system removes the need for two separate coils, which reduces system size and unwanted couplings. The design and analysis of the double-tuned transmitter coil using a lumped element frequency trap are discussed in this letter. The transmitter achieves measured matching of -26.2 dB and -21.5 dB and isolation of -17.7 dB and -11.7dB at 13.56 MHz and 40.68 MHz, respectively. A 3 mm × 15 mm flexible coil is used as an implantable receiver. This letter shows synchronized multisite stimulation of two flexible implants at a distance of 2 cm while covered with 1 cm chicken breast.