Surgery

A machine learning-based prediction model for delayed clinically important postoperative nausea and vomiting in high-risk patients undergoing laparoscopic gastrointestinal surgery.

BACKGROUND: Delayed clinically important postoperative nausea and vomiting (CIPONV) could lead to significant consequences following surgery. We aimed to develop a prediction model for it using machine learning algorithms utilizing perioperative data from patients undergoing laparoscopic gastrointestinal surgery.

Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers.

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis...

Diagnosing Sarcopenia with AI-Aided Ultrasound (DINOSAUR)-A Pilot Study.

Sarcopenia has been recognized as a determining factor in surgical outcomes and is associated with an increased risk of postoperative complications a...

Artificial intelligence facilitates the potential of simulator training: An innovative laparoscopic surgical skill validation system using artificial intelligence technology.

PURPOSE: The development of innovative solutions, such as simulator training and artificial intelligence (AI)-powered tutoring systems, has significan...

Artificial intelligence-aided ultrasound imaging in hepatopancreatobiliary surgery: where are we now?

BACKGROUND: Artificial intelligence (AI) models have been applied in various medical imaging modalities and surgical disciplines, however the current ...

Comparing the effectiveness of robotic plantarflexion resistance and biofeedback between overground and treadmill walking.

Individuals with diminished walking performance caused by neuromuscular impairments often lack plantar flexion muscle activity. Robotic devices have b...

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for de...

Tree-based ensemble machine learning models in the prediction of acute respiratory distress syndrome following cardiac surgery: a multicenter cohort study.

BACKGROUND: Acute respiratory distress syndrome (ARDS) after cardiac surgery is a severe respiratory complication with high mortality and morbidity. T...

Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.

Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk factors for PONV is crucial because it is associat...

Generalized M-sparse algorithms for constructing fault tolerant RBF networks.

In the construction process of radial basis function (RBF) networks, two common crucial issues arise: the selection of RBF centers and the effective u...

Improving the diagnostic value of lineup rejections.

Erroneous eyewitness identification evidence is likely the leading cause of wrongful convictions. To minimize this error, scientists recommend collect...

Robotic flytrap with an ultra-sensitive 'trichome' and fast-response 'lobes'.

Nature abounds with examples of ultra-sensitive perception and agile body transformation for highly efficient predation as well as extraordinary adapt...

Advancing aneurysm management: the potential of AI and machine learning in enhancing safety and predictive accuracy.

Cerebral aneurysm rupture, the predominant cause of non-traumatic subarachnoid hemorrhage, underscores the need for effective treatment and early dete...

Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.

To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) bur...

A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics.

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection...

Assessing the Effect of Cervical Transcutaneous Spinal Stimulation With an Upper Limb Robotic Exoskeleton and Surface Electromyography.

Transcutaneous spinal stimulation (TSS) is a promising rehabilitative intervention to restore motor function and coordination for individuals with spi...

Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.

Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, AD...

Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer.

PURPOSE: Conventional normal tissue complication probability (NTCP) models for patients with head and neck cancer are typically based on single-value ...

Predicting serious postoperative complications and evaluating racial fairness in machine learning algorithms for metabolic and bariatric surgery.

BACKGROUND: Predicting the risk of complications is critical in metabolic and bariatric surgery (MBS).

Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods.

BACKGROUND: Postoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged...