Latest AI and machine learning research in surgery for healthcare professionals.
Surgical phase recognition from video enables various downstream applications. Transformer-based sliding window approaches have set the state-of-the-art by capturing rich spatial-temporal features. However, while transformers can theoretically handle arbitrary-length sequences, in practice they are limited by memory and compute constraints, resulting in fixed context windows that struggle with m...
Robotic surgery offers enhanced precision and adaptability, paving the way for automation in surgical interventions. Cholecystectomy, the gallbladder removal, is particularly well-suited for automation due to its standardized procedural steps and distinct anatomical boundaries. A key challenge in automating this procedure is dissecting with accuracy and adaptability. This paper presents a vision...
Purpose To apply conformal prediction to a deep learning (DL) model for intracranial hemorrhage (ICH) detection and evaluate model performance in dete...
OBJECTIVE: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged t...
Artificial intelligence (AI) is a colossal buzzword, a confusing subject matter, but also an inevitable reality. Generative and nongenerative AI are t...
Artificial intelligence (AI) experience among nurses in perioperative settings is crucial for effective healthcare delivery. This study aimed to asses...
Uncertainty quantification is necessary for developers, physicians, and regulatory agencies to build trust in machine learning predictors and improv...
Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, thei...
Accurate tumor detection in digital pathology whole-slide images (WSIs) is crucial for cancer diagnosis and treatment planning. Multiple Instance Le...
Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and i...
The lack of labeled datasets in 3D vision for surgical scenes inhibits the development of robust 3D reconstruction algorithms in the medical domain....
Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature. We demonstrate that SVM can be u...
Transparent objects are prevalent in everyday environments, but their distinct physical properties pose significant challenges for camera-guided rob...
This work presents a motion planning framework for robotic manipulators that computes collision-free paths directly in image space. The generated pa...
This paper introduces a novel pipeline to enhance the precision of object masking for robotic manipulation within the specific domain of masking pro...
Existing tracheal tumor resection methods often lack the precision required for effective airway clearance, and robotic advancements offer new poten...
We present an image blending pipeline, \textit{IBURD}, that creates realistic synthetic images to assist in the training of deep detectors for use o...
Despite the potential of synthetic medical data for augmenting and improving the generalizability of deep learning models, memorization in generativ...
Introduction: Computer vision (CV) has had a transformative impact in biomedical fields such as radiology, dermatology, and pathology. Its real-worl...
Materials synthesis is vital for innovations such as energy storage, catalysis, electronics, and biomedical devices. Yet, the process relies heavily...