Surgery

Automated assessment of simulated laparoscopic surgical skill performance using deep learning.

Artificial intelligence (AI) has the potential to improve healthcare and patient safety and is currently being adopted across various fields of medicine and healthcare. AI and in particular computer vision (CV) are well suited to the analysis of minimally invasive surgical simulation videos for training and performance improvement. CV techniques have rapidly improved in recent years from accuratel...

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.

This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in everyday tasks while interacting with a robotic hand. We evaluated the effectiveness of this BCI-robot system using the Berlin Bimanual Test for Stro...

Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.

Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated...

New navigation for laparoscopic and robotic cholecystectomy using artificial intelligence.

Enhancing Accuracy of Operative Reports with Automated Artificial Intelligence Analysis of Surgical Video.

BACKGROUND: The creation of operative reports is a tedious documentation task that increases administrative burden, which is a potential driver of bur...

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in ...

Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations.

Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive...

Single-Cell Sequencing-Guided Annotation of Rare Tumor Cells for Deep Learning-Based Cytopathologic Diagnosis of Early Lung Cancer.

Deep learning (DL) models for medical image analysis are majorly bottlenecked by the lack of well-annotated datasets. Bronchoalveolar lavage (BAL) is ...

Prediction of postoperative intensive care unit admission with artificial intelligence models in non-small cell lung carcinoma.

BACKGROUND: There is no standard practice for intensive care admission after non-small cell lung cancer surgery. In this study, we aimed to determine ...

Biophysical modeling and artificial intelligence for quantitative assessment of anastomotic blood supply in laparoscopic low anterior rectal resection.

PURPOSE: Fluorescence imaging is critical for intraoperative intestinal perfusion assessment in colorectal surgery, yet its clinical adoption remains ...

Comparative study of XGBoost and logistic regression for predicting sarcopenia in postsurgical gastric cancer patients.

The use of machine learning (ML) techniques, particularly XGBoost and logistic regression, to predict sarcopenia among postsurgical gastric cancer pat...

Our tools redefine what it means to be us: perceived robotic agency decreases the importance of agency in humanity.

Past work has primarily focused on how the perception of robotic agency influences human-robot interaction and the evaluation of robotic progress, whi...

Risk Factors and Outcomes of Hemorrhagic Transformation in Acute Ischemic Stroke Following Thrombolysis: Analysis of a Single-Center Experience and Review of the Literature.

: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, providing valuable insights into hemorrhagic transforma...

Bi-variational physics-informed operator network for fractional flow reserve curve assessment from coronary angiography.

The coronary angiography-derived fractional flow reserve (FFR) curve, referred to as the Angio-FFR curve, is crucial for guiding percutaneous coronary...

Video-based multi-target multi-camera tracking for postoperative phase recognition.

PURPOSE: Deep learning methods are commonly used to generate context understanding to support surgeons and medical professionals. By expanding the cur...

SwinCVS: a unified approach to classifying critical view of safety structures in laparoscopic cholecystectomy.

PURPOSE: Laparoscopic cholecystectomy is one of the most commonly performed surgeries in the UK. Despite its safety, the volume of operations leads to...

Peripheral blood immune landscape and NXPE3 as a novel biomarker for hypertensive intracerebral hemorrhage risk prediction and targeted therapy.

We employed bulk RNA-seq and scRNA-seq techniques to analyze the immune dysregulation in patients with intracerebral hemorrhage (ICH). The study revea...

Examining the development, effectiveness, and limitations of computer-aided diagnosis systems for retained surgical items detection: a systematic review.

Retained surgical items (RSIs) can lead to severe complications, and infections, with morbidity rates up to 84.32%. Computer-aided detection (CAD) sys...

Micro- and Nano-Bots for Infection Control.

Medical micro- and nano-bots (MMBs and MNBs) have attracted a lot of attention owing to their precise motion for accessing difficult-to-reach areas in...

Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks.

OBJECTIVE: The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies a...