Latest AI and machine learning research in surgery for healthcare professionals.
PURPOSE: Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recently been introduced and widely investigated. We aimed to investigate the performance of deep learning models in segmentation in robot-assisted radical p...
OBJECTIVE: To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches.
Artificial intelligence (AI) is increasingly significant in neurosurgery, enhancing differential diagnosis, preoperative evaluation, and surgical prec...
BACKGROUND: Gallbladder cancer is often associated with poor prognosis, especially when patients experience early recurrence after surgery. Machine le...
Machine learning techniques have enabled researchers to leverage neuroimaging data to decode speech from brain activity, with some amazing recent su...
Testing autonomous robotic manipulators is challenging due to the complex software interactions between vision and control components. A crucial ele...
Autonomous surgical robots have demonstrated significant potential to standardize surgical outcomes, driving innovations that enhance safety and con...
The mechanical competence and suturing ability of collagen-based membranes are paramount in Guided Bone Regeneration (GBR) therapy, to ensure damage...
Federated Learning (FL) is a widespread approach that allows training machine learning (ML) models with data distributed across multiple devices. In...
Importance: Many individuals with drug-resistant epilepsy continue to have seizures after resective surgery. Accurate identification of focal brain ...
Robots-based smart pharmacies are essential for modern healthcare systems, enabling efficient drug delivery. However, a critical challenge exists in...
Surgical robot is the national strategic diagnostic and therapeutic equipment research focus, get a number of scientific research institutes, colleges...
Reoperation is the most significant complication following any surgical procedure. Developing machine learning methods that predict the need for reope...
Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the I...
Building generalist robotic systems involves effectively endowing robots with the capabilities to handle novel objects in an open-world setting. Ins...
Finite element method applications are a common approach to simulate a handful of phenomena but can take a lot of computing power, causing elevated ...
In the rapidly advancing field of robotics, dual-arm coordination and complex object manipulation are essential capabilities for developing advanced...
Vision-based surgical navigation has received increasing attention due to its non-invasive, cost-effective, and flexible advantages. In particular, ...
Dense colored point clouds enhance visual perception and are of significant value in various robotic applications. However, existing learning-based ...
PURPOSE: The purpose of this study was to develop deep learning models for surgical video analysis, capable of identifying minimally invasive glaucoma...