Surgery

Exploring Long-Term Prediction of Type 2 Diabetes Microvascular Complications

Electronic healthcare records (EHR) contain a huge wealth of data that can support the prediction of clinical outcomes. EHR data is often stored and analysed using clinical codes (ICD10, SNOMED), however these can differ across registries and healthcare providers. Integrating data across systems involves mapping between different clinical ontologies requiring domain expertise, and at times resul...

DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps

In medical imaging, efficient segmentation of colon polyps plays a pivotal role in minimally invasive solutions for colorectal cancer. This study introduces a novel approach employing two parallel encoder branches within a network for polyp segmentation. One branch of the encoder incorporates the dual convolution blocks that have the capability to maintain feature information over increased dept...

Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment

This work introduces the first framework for reconstructing surgical dialogue from unstructured real-world recordings, which is crucial for characte...

[Application progress and future prospects of interventional robotics in vascular injury hemostasis].

The field of traumatic hemostasis is currently confronted with numerous challenges, particularly in addressing the treatment of non-compressible torso...

Automated Segmentation of Fetal Intracranial Volume in Three-Dimensional Ultrasound Using Deep Learning: Identifying Sex Differences in Prenatal Brain Development.

The human brain undergoes major developmental changes during pregnancy. Three-dimensional (3D) ultrasound images allow for the opportunity to investig...

Prognostic Effect of Trigeminal Neuralgia Treated With Percutaneous Balloon Compression by Machine Learning-based Modeling of Radiomic Morphological Features.

BACKGROUND: Trigeminal neuralgia (TN) is defined as spontaneous pain in the region of the trigeminal nerve that seriously affects a patient's quality ...

[THE SURGICAL INTELLIGENCE REVOLUTION - APPLYING AI TO IMPROVE SURGICAL QUALITY AND SAFETY].

The use of artificial intelligence (AI) in medicine is rising fast. We continually hear about novel AI-based technologies being deployed to aid clinic...

Predictive Capacities of a Machine Learning Decision Tree Model Created to Analyse Feasibility of an Open or Robotic Kidney Transplant.

BACKGROUND: Machine learning has emerged as a potent tool in healthcare. A decision tree model was built to improve the decision-making process when d...

Demographic Predictability in 3D CT Foundation Embeddings

Self-supervised foundation models have recently been successfully extended to encode three-dimensional (3D) computed tomography (CT) images, with ex...

Spatially Visual Perception for End-to-End Robotic Learning

Recent advances in imitation learning have shown significant promise for robotic control and embodied intelligence. However, achieving robust genera...

Machine learning for cerebral blood vessels' malformations

Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often e...

Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward.

In a recent paper, Hong developed an artificial intelligence (AI)-driven predictive scoring system for potential complications following laparoscopic...

Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications

Artificial intelligence based predictive models trained on the clinical notes can be demographically biased. This could lead to adverse healthcare d...

Artificial Intelligence Chatbots in Patient Communication: Current Possibilities.

ChatGPT, an artificial intelligence (AI) chatbot, can generate text prompts based on user input. This study investigated the possibility of utilizing ...

[Influencing factors of implant accuracy of oral implant robot].

In recent years, robots have been gradually applied in the field of oral implantation. Compared with static guide and dynamic navigation, robot-assist...

Enhancing neuro-oncology care through equity-driven applications of artificial intelligence.

The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on t...

Federated Voxel Scene Graph for Intracranial Hemorrhage

Intracranial Hemorrhage is a potentially lethal condition whose manifestation is vastly diverse and shifts across clinical centers worldwide. Deep-l...

Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.

Purpose To develop a highly generalizable weakly supervised model to automatically detect and localize image-level intracranial hemorrhage (ICH) by us...

Deep Learning Reconstruction in Abdominopelvic Contrast-Enhanced CT for The Evaluation of Hemorrhages.

PURPOSE: To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrha...

Leveraging machine learning for preoperative prediction of supramaximal ablation in laser interstitial thermal therapy for brain tumors.

OBJECTIVE: Maximizing safe resection in neuro-oncology has become paramount to improving patient survival and outcomes. Laser interstitial thermal the...