Latest AI and machine learning research in surgery for healthcare professionals.
We introduce a neural network approach for generating and customizing the trajectory of a robotic arm, that guarantees precision and repeatability. To highlight the potential of this novel method, we describe the design and implementation of the technique and show its application in an experimental setting of cognitive robotics. In this scenario, the NICO robot was characterized by the ability t...
The complexity and diversity of surgical workflows, driven by heterogeneous operating room settings, institutional protocols, and anatomical variability, present a significant challenge in developing generalizable models for cross-institutional and cross-procedural surgical understanding. While recent surgical foundation models pretrained on large-scale vision-language data offer promising trans...
Sphincter-preserving surgery has become the mainstream approach for mid-to-low rectal cancer, yet postoperative anal dysfunction (low anterior resecti...
Transabdominal and transanal endoscopic approaches have become mainstream in colorectal surgery. With the substantial improvement in survival outcomes...
Laparoscopic liver surgery, while minimally invasive, poses significant challenges in accurately identifying critical anatomical structures. Augment...
The convergence of robotics and virtual reality (VR) has enabled safer and more efficient workflows in high-risk laboratory settings, particularly v...
Rapid and reliable vascular access is critical in trauma and critical care. Central vascular catheterization enables high-volume resuscitation, hemo...
Deep learning in medical imaging faces obstacles: limited data diversity, ethical issues, high acquisition costs, and the need for precise annotatio...
Online surgical phase recognition has drawn great attention most recently due to its potential downstream applications closely related to human life...
Surgical training integrates several years of didactic learning, simulation, mentorship, and hands-on experience. Challenges include stress, technic...
Purpose: Accurate intraoperative X-ray/CT registration is essential for surgical navigation in orthopedic procedures. However, existing methods stru...
Solving Inverse Kinematics (IK) problems is fundamental to robotics, but has primarily been successful with single serial manipulators. For multi-ar...
In clinical operations, teamwork can be the crucial factor that determines the final outcome. Prior studies have shown that sufficient collaboration...
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after ...
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after ...
Accurate in-hand pose estimation is crucial for robotic object manipulation, but visual occlusion remains a major challenge for vision-based approac...
Recent advances in medical imaging have established deep learning-based segmentation as the predominant approach, though it typically requires large...
. Accurately and in real-time simulating soft tissue deformation and visualizing stress distribution are crucial for advancing surgical simulators clo...
BACKGROUND: Although much progress has been made in artificial intelligence (AI), several challenges remain substantial obstacles to the development a...
Accurate 6D pose estimation of complex objects in 3D environments is essential for effective robotic manipulation. Yet, existing benchmarks fall sho...