Updated: Jun 28
Would that be the future of surgery?
Augmentation of surgical visualization, navigation, and decision-making assisted by augmented reality, mixed reality, and AI.
Autonomous navigation and autonomous manipulations, assisted by AI and surgical robotics.
Updated: Jun 28
To teach an AI model surgical intraoperative phases, a surgeon will watch a video and label each phase of the surgery.
Then an AI algorithm will be given both the labels and the video to learn what constitutes each phase.
The challenge is that surgical data is too complex and needs trained annotators.
Those expert annotators are rare.
One of the solutions for this problem is to pre-train models with unlabeled data.
Another solution is to let machine learning (ML) algorithms do “auto-annotation” from a few videos.
Updated: Jul 22
Applications of Big data in surgery are limitless.
Every surgery generates a bunch of data.
This data can be the patient's medical history, vital signs during surgery, type of procedure, and how well the patient recovers afterward.
Big data is about collecting, storing, and analyzing huge amounts of data to find useful patterns, trends, or insights.
Here are some applications of Big data in surgery:
Early detection of diseases.
Surgical risk assessment.
Personalize treatment recommendations.
Preoperative assistance helps to navigate complex anatomy and avoid critical structures.
Optimize surgical workflows and resource allocation.
Enhance surgical education and skill acquisition.
Advanced surgical practices.
Identification and standardization of best practices.
Drive innovation in surgical techniques and devices.
Big data helps gather, organize, and analyze lots of data to make surgeries safer, more effective, and personalized to each patient's needs.