Computer Vision News 32 AI for Surgical Video Analysis We spoke with Asher Patinkin, one of the knowledgeable experts in this field at RSIP Vision. He provided a full review of the most advanced AI and Computer Vision algorithms that can be used for surgical video analysis. Depending on the specific requirements, Deep Learning algorithms, such as convolutional neural networks (CNNs), RNNs can be trained on large datasets of surgical video footage to perform tasks such as object detection, tracking, segmentation, and activity recognition, as follows: Object Detection – Asher points out that Algorithms like YOLO, Faster RCNN, SSD (Single Shot Detector) and RetinaNet help identify specific objects or instruments within surgical video footage, such as surgical tools, implants, or anatomical structures. Tracking – Here we distinguish between traditional algorithms (like Kalman Filter, Mean Shift, Particle Filter) and more recent Deep Learning algorithms, which follow the movement of objects or instruments within the surgical video footage over time, allowing for analysis of the trajectory and motion of these objects. Surgical video analysis involves using artificial intelligence and machine learning algorithms to analyze surgical video footage. This practice, which includes both intraoperative and postoperative video analysis, has numerous benefits for patients, for surgeons and for other medical professionals as well. This is what we use at RSIP Vision.
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