Artificial intelligence‐based computer vision in surgery: Recent advances and future perspectives

Abstract

Technology has advanced surgery, especially minimally invasive surgery (MIS), including laparoscopic surgery and robotic surgery. It has led to an increase in the number of technologies in the operating room. They can provide further information about a surgical procedure, e.g. instrument usage and trajectories. Among these surgery‐related technologies, the amount of information extracted from a surgical video captured by an endoscope is especially great. Therefore, the automation of data analysis is essential in surgery to reduce the complexity of the data while maximizing its utility to enable new opportunities for research and development. Computer vision (CV) is the field of study that deals with how computers can understand digital images or videos and seeks to automate tasks that can be performed by the human visual system. Because this field deals with all the processes of real‐world information acquisition by computers, the terminology “CV” is extensive, and ranges from hardware for image sensing to AI‐based image recognition. AI‐based image recognition for simple tasks, such as recognizing snapshots, has advanced and is comparable to humans in recent years. Although surgical video recognition is a more complex and challenging task, if we can effectively apply it to MIS, it leads to future surgical advancements, such as intraoperative decision‐making support and image navigation surgery. Ultimately, automated surgery might be realized. In this article, we summarize the recent advances and future perspectives of AI‐related research and development in the field of surgery.

Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives

“…Laparoscopic videos of bariatric procedures could be collected to serve as a training database for machine learning models. By providing accurate image navigation during surgery, anatomical landmarks and unexpected intraoperative findings such as adhesions and abdominal wall hernias could be identified efficiently by machine learning models [ 51 ]. In addition, perioperative data collected from anesthesiologists could be collected such as continuous blood pressure measures or oxygen saturation as factors possibly predicting postoperative complications.…”

Robotics in laparoscopic surgery - A review

“…Future directions of surgical robots in laparoscopic surgeries will include a higher level of automation. It is anticipated that robots with the ability to make real-time surgical decisions and execute desired tasks -under the surgeon's supervision-will be used in the operating rooms in the future [10,205], with the aid of DL and providing robots with preliminary knowledge of the surgical procedures. Repetitive, time consuming, and tedious tasks, like instruments maneuvering, and suturing tasks will be autonomously performed by robots.…”

The future of artificial intelligence in neurosurgery: A narrative review

“…It can also identify potential areas of improvement, standardize procedural skills, and accurately predict the association of postoperative complications and operation time with surgeons’ skill complexity. [ 24 ]…”

“…It can also identify potential areas of improvement, standardize procedural skills, and accurately predict the association of postoperative complications and operation time with surgeons' skill complexity. [24] AI and neurosurgery e history of AI in neurosurgery roots from the 1990s when the use of ML was first evidenced in the medical literature as ANNs were developed for structured datasets analysis and tasks' supervision. From the lesion detection on reconstructed SPECT scans and grading of astrocytic gliomas, ANNs were increasingly utilized and their outcomes were comparable to manual processing.…”