Surgery

Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy

Purpose: Autonomous systems in mechanical thrombectomy (MT) hold promise for reducing procedure times, minimizing radiation exposure, and enhancing patient safety. However, current reinforcement learning (RL) methods only reach the carotid arteries, are not generalizable to other patient vasculatures, and do not consider safety. We propose a safe dual-device RL algorithm that can navigate beyond...

ZeroMimic: Distilling Robotic Manipulation Skills from Web Videos

Many recent advances in robotic manipulation have come through imitation learning, yet these rely largely on mimicking a particularly hard-to-acquire form of demonstrations: those collected on the same robot in the same room with the same objects as the trained policy must handle at test time. In contrast, large pre-recorded human video datasets demonstrating manipulation skills in-the-wild alre...

[Three-Dimensional Reconstruction Technique and Its Application of Binocular Endoscopic Images Based on Deep Learning].

The clinical application of binocular endoscope relies primarily on the visual system of physicians to create a three-dimensional effect, but it canno...

Can DeepSeek Reason Like a Surgeon? An Empirical Evaluation for Vision-Language Understanding in Robotic-Assisted Surgery

The DeepSeek models have shown exceptional performance in general scene understanding, question-answering (QA), and text generation tasks, owing to ...

Co-design of materials, structures and stimuli for magnetic soft robots with large deformation and dynamic contacts

Magnetic soft robots embedded with hard magnetic particles enable untethered actuation via external magnetic fields, offering remote, rapid, and pre...

ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning

Human hands play a central role in interacting, motivating increasing research in dexterous robotic manipulation. Data-driven embodied AI algorithms...

Adaptive Resampling with Bootstrap for Noisy Multi-Objective Optimization Problems

The challenge of noisy multi-objective optimization lies in the constant trade-off between exploring new decision points and improving the precision...

A Retrieval-Based Approach to Medical Procedure Matching in Romanian

Accurately mapping medical procedure names from healthcare providers to standardized terminology used by insurance companies is a crucial yet comple...

3D Convolutional Neural Networks for Improved Detection of Intracranial bleeding in CT Imaging

Background: Intracranial bleeding (IB) is a life-threatening condition caused by traumatic brain injuries, including epidural, subdural, subarachnoi...

Surg-3M: A Dataset and Foundation Model for Perception in Surgical Settings

Advancements in computer-assisted surgical procedures heavily rely on accurate visual data interpretation from camera systems used during surgeries....

fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models

While vision-language models like CLIP have advanced zero-shot surgical phase recognition, they struggle with fine-grained surgical activities, espe...

Multi-modal 3D Pose and Shape Estimation with Computed Tomography

In perioperative care, precise in-bed 3D patient pose and shape estimation (PSE) can be vital in optimizing patient positioning in preoperative plan...

Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data

In high-dimensional and high-stakes contexts, ensuring both rigorous statistical guarantees and interpretability in feature extraction from complex ...

End-to-End Deep Learning for Real-Time Neuroimaging-Based Assessment of Bimanual Motor Skills

The real-time assessment of complex motor skills presents a challenge in fields such as surgical training and rehabilitation. Recent advancements in...

MerGen: Micro-electrode recording synthesis using a generative data-driven approach

The analysis of electrophysiological data is crucial for certain surgical procedures such as deep brain stimulation, which has been adopted for the ...

Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection.

BACKGROUND: Colorectal polyps are precancerous diseases of colorectal cancer. Early detection and resection of colorectal polyps can effectively reduc...

From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction

Surgical automation requires precise guidance and understanding of the scene. Current methods in the literature rely on bulky depth cameras to creat...

[Application Practice of AI Empowering Post-discharge Specialized Disease Management in Postoperative Rehabilitation of the Lung Cancer Patients Undergoing Surgery].

BACKGROUND: Lung cancer is the leading malignancy in China in terms of both incidence and mortality. With increased health awareness and the widesprea...

Predicting Postoperative Circulatory Complications in Older Patients: A Machine Learning Approach.

OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circu...

Shap-MeD

We present Shap-MeD, a text-to-3D object generative model specialized in the biomedical domain. The objective of this study is to develop an assista...