Artificial Intelligence in Surgery
A special issue of Bioengineering (ISSN 2306-5354).
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 13555
Special Issue Editors
Interests: artificial intelligence in surgery; artificial intelligence in medical imaging; surgical skills assessment; surgical simulation; telemedicine; surgical robots
Interests: artificial intelligence; technology assessment; simulation; decision analysis; performance measurement; computer vision; virtual reality; augmented reality
Special Issue Information
Dear Colleagues,
Surgical data science is a fast-growing research field in both the academic and industrial worlds, and will impact all aspects of surgery considerably: training, simulation, intraoperative decision making, and the prediction of events and outcomes, assisting surgeons in the preoperative planning of major operations and reinterventions, postoperative progress, and the management of complications.
In particular, minimal access surgery generates a considerable amount of data that can be processed by artificial intelligence (AI), including data at the preoperative (e.g., clinical, laboratory, and imaging tests of patients), intraoperative (e.g., video recordings and even kinematic data in cases of robot-assisted surgery), and postoperative phases (e.g., operative times).
The availability of more and more complex AI models has led to improvements in the metrics of surgical data science tasks. At the same time, progress in hardware has significantly reduced the computation times of these models.
We therefore invite you to submit original research papers and comprehensive reviews on the theory and applications of AI in surgery, from the development of AI models on existing or new datasets to their clinical applications in laparoscopy, robot-assisted surgery, and endovascular surgery.
Topics of interest for this Special Issue include, but are not limited to, the following:
- Automatic skills assessment;
- Autonomous surgical robots;
- Computer vision;
- Natural Language Processing (NLP);
- Federated learning;
- Imitation learning;
- Intraoperative decision making;
- Predictive modeling of risks, diseases, and patients' outcomes;
- Segmentation of radiological images for preoperative planning;
- Self-supervised learning;
- Surgical simulation/training;
- Video-based assessment of surgical procedures.
Dr. Andrea Moglia
Dr. Amin Madani
Dr. Daniel Hashimoto
Guest Editors
Manuscript Submission Information
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