Deploying computer vision technology in a real-world surgical setting poses several challenges and limitations compared to the controlled conditions of a study.
One major challenge is the variability in lighting conditions, camera angles, and clutter in a real operating room. The computer vision algorithms developed in a controlled setting may struggle to accurately identify instruments in the presence of blood, tissue, and other fluids that are common during surgery.
Another challenge is the need for real-time processing of video feeds from multiple cameras in the OR. This requires high computational power and efficient algorithms to ensure minimal latency in instrument tracking and identification.
Additionally, the diversity of surgical procedures and instruments used in different surgeries presents a challenge for the computer vision system. The model trained on a specific set of instruments may not generalize well to new instruments or procedures, requiring continuous updates and retraining.
Moreover, ensuring the privacy and security of patient data captured by the computer vision system is crucial in a real-world setting. Compliance with healthcare regulations and data protection laws adds another layer of complexity to the deployment of this technology.