Advancements in artificial intelligence have brought about significant transformations in the medical field, particularly in surgical procedures. Surgeons seldom receive feedback on how well they perform surgery, despite evidence that it accelerates their acquisition of skills (e.g., suturing). Such feedback can be automated, in theory, by artificial intelligence systems.
Surgical video annotation is a specialized application of data annotation in the medical field, where surgical procedures are meticulously analyzed and labeled in real time. It involves precise labeling of various elements and actions captured in surgical videos. By accurately annotating surgical videos, medical experts can gain valuable insights, improve surgical techniques, standardize best practices, and even detect potential complications early on.
This blog delves into various types of surgical video annotation and outlines its benefits and role in the healthcare industry.
Types of Annotation for Surgical AI
Surgical Phase Recognition
One of the fundamental types of surgical video annotation involves recognizing the surgical phase. By accurately time-stamping each step in a surgical procedure, AI algorithms create a detailed breakdown of the surgical workflow.
For instance, during a knee replacement surgery, the AI system can accurately identify and annotate the individual steps of the procedure, such as incision, bone preparation, implantation, and wound closure, and time stamp them accordingly. By analyzing the steps, AI can assess surgeon performance and establish best practices that can be shared across the medical community, leading to improved consistency and outcomes.
Surgical Instrument Tracking
In this scenario, the surgical instruments are annotated by categorizing them according to their type and then segmented using techniques like brush, polygon, or key points across multiple frames. The primary goal of this annotation is to precisely monitor the position and movements of these instruments, often for the training of AI systems utilized in robotic surgery.
For instance, in laparoscopic surgeries, the AI system can track the movement of surgical instruments like forceps, scissors, and graspers throughout the operation. Surgical Tool Tracking enhances the assessment of surgical techniques and workflow. By analyzing the tool movements, surgeons and medical professionals can identify opportunities for efficiency and optimization.
Labeling Anatomical Structures
Labeling anatomical structures for surgical AI facilitates precise identification and localization of critical tissues, enhancing the AI’s ability to assist surgeons in real-time during procedures.
For instance, in neurosurgery, the AI system can accurately label brain structures, such as the cerebral cortex, hippocampus, and ventricles, ensuring precise navigation during the procedure. It can be done on 2D, 3D, and video data using bounding boxes, polygons, key points, or semantic segmentation.
Labeling Polyp and Abnormalities
This annotation type focuses on identifying and labeling polyps and abnormalities like cancer cells or other formations within the patient’s body using a precise segmentation mask or a polygon. Training the AI system to accurately identify abnormalities will aid in early detection and diagnosis, leading to timely interventions and improved patient outcomes. For instance, the AI system can detect and annotate polyps in colonoscopy, making it easier for physicians to detect and diagnose potential gastrointestinal issues.
Dental Surgery
Surgical data annotation finds applications in dental surgeries, allowing for detailed analysis and assessment of dental procedures. In dental implant surgeries, the AI system can track the placement of dental implants and analyze the occlusion for optimal outcomes. Similar to robotic surgical approaches, this technology involves monitoring surgical processes, instruments, and anatomical structures to facilitate automated or semi-automated surgical interventions.
Depth Labeling
Depth labeling involves annotating the three-dimensional depth perception in surgical videos, providing a comprehensive understanding of spatial relationships during surgery. It can be achieved by labeling the foreground vs. the background with a full semantic segmentation mask.
In ophthalmic surgeries like cataract removal, the AI system can help surgeons assess the position and manipulation of intraocular lenses. Understanding the depth enhances surgical accuracy and reduces the risk of errors related to spatial perception, ensuring successful surgical outcomes.
Why Expert Human-in-the-Loop is Necessary for Surgical Video Annotation
While surgical video annotation offers invaluable benefits, it comes with its challenges, including data sourcing, variability in anatomy across patients, and the complexity of lengthy surgical videos. It is where the “human-in-the-loop” approach becomes crucial.
Incorporating human expertise ensures meticulous annotation accuracy and adaptability to unique surgical scenarios. Human annotators can handle complex surgical cases, comprehend subtle anatomical differences, and adapt to evolving surgical techniques, making the annotation process more reliable and trustworthy.
Conclusion
Surgical AI is a transformative technology that empowers the medical community to enhance surgical procedures, optimize patient care, and promote medical research. With various annotation types and the integration of human expertise, AI-assisted surgeries can improve patient outcomes and shape the future of modern healthcare.
iMerit is a leading provider of high-quality data annotation services, specializing in Surgical Video Annotation for the healthcare industry. Our skilled workforce and cutting-edge AI technology enable medical professionals to harness the full potential of AI in surgical applications, contributing to improved patient care and medical advancements.
