Surgery

Predicting Postoperative Mortality After Metastatic Intraspinal Neoplasm Excision: Development of a Machine-Learning Approach.

OBJECTIVE: Mortality following surgical resection of spinal tumors is a devastating outcome. Naïve Bayes machine learning algorithms may be leveraged in surgical planning to predict mortality. In this investigation, we use a Naïve Bayes classification algorithm to predict mortality following spinal tumor excision within 30 days of surgery.

Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography.

OBJECTIVE: The timely reporting of critical results in radiology is paramount to improved patient outcomes. Artificial intelligence has the ability to improve quality by optimizing clinical radiology workflows. We sought to determine the impact of a United States Food and Drug Administration-approved machine learning (ML) algorithm, meant to mark computed tomography (CT) head examinations pending ...

Longitudinal analysis of trifecta outcome in Japanese patients with prostate cancer following robot-assisted laparoscopic radical prostatectomy.

PURPOSE: To analyze the trifecta outcome (continence, potency, and cancer control) longitudinally using robot-assisted laparoscopic radical prostatect...

Comparing machine and deep learning-based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging.

Previous work using logistic regression suggests that cognitive control-related frontoparietal activation in early psychosis can predict symptomatic i...

A deep learning framework for quality assessment and restoration in video endoscopy.

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, sp...

Comparison of Therapeutic Effects Among Different Surgical Approaches in Robot-Assisted Partial Nephrectomy: A Systematic Review and Meta-Analysis.

To systematically explore the superiority of the transperitoneal approach in robot-assisted partial nephrectomy (TP-RAPN) and retroperitoneal approac...

Effect of Social Anxiety on the Adoption of Robotic Training Partner.

With the development of artificial intelligence technologies, robotic training partner is becoming a reality, which is a substitute for human training...

Robot-Assisted Radical Cystectomy with Intracorporeal Urinary Diversion: A New Standard of Urinary Diversion.

To summarize the current evidence on robot-assisted radical cystectomy (RARC) with intracorporeal urinary diversion (ICUD) and compare perioperative ...

Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage.

OBJECTIVE: To determine whether machine learning (ML) algorithms can improve the prediction of delayed cerebral ischemia (DCI) and functional outcomes...

Prediction of vascular aging based on smartphone acquired PPG signals.

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is...

Robot-assisted adrenalectomy: state of the art.

Currently, laparoscopic adrenalectomy is worldwide considered the gold standard technique. Both transperitoneal and retroperitoneal approaches have pr...

Accuracy of Touch-Based Registration During Robotic Image-Guided Partial Nephrectomy Before and After Tumor Resection in Validated Phantoms.

Image-guided surgery (IGS) allows for accurate, real-time localization of subsurface critical structures during surgery. No prior IGS systems have de...

Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.

We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists with...

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy.

Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale ti...

Initial introduction of robot-assisted, minimally invasive esophagectomy using the microanatomy-based concept in the upper mediastinum.

BACKGROUND: We have recently standardized upper mediastinal lymph node dissection (UMLND) using a microanatomy-based concept in thoracoscopic esophage...

Closing the Digital Health Evidence Gap: Development of a Predictive Score to Maximize Patient Outcomes.

Clinical studies of telemedicine (TM) programs for chronic illness have demonstrated mixed results across settings and populations. With recent uptak...

Machine Learning Enhanced Spectrum Recognition Based on Computer Vision (SRCV) for Intelligent NMR Data Extraction.

A machine learning enhanced spectrum recognition system called spectrum recognition based on computer vision (SRCV) for data extraction from previousl...

Worldwide Techniques and Outcomes in Robot-assisted Minimally Invasive Esophagectomy (RAMIE): Results From the Multicenter International Registry.

OBJECTIVE: This international multicenter study by the Upper GI International Robotic Association aimed to gain insight in current techniques and outc...

A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT.

This project aimed to develop and evaluate a fast and fully-automated deep-learning method applying convolutional neural networks with deep supervisio...

Safety and efficacy of robot-assisted versus open pancreaticoduodenectomy: a meta-analysis of multiple worldwide centers.

The objective of the study is to compare the safety and efficacy of robot-assisted pancreaticoduodenectomy (PD) with open PD. The PubMed, EMBASE and C...