Surgery

Towards near real-time assessment of surgical skills: A comparison of feature extraction techniques.

BACKGROUND AND OBJECTIVE: Surgical skill assessment aims to objectively evaluate and provide constructive feedback for trainee surgeons. Conventional methods require direct observation with assessment from surgical experts which are both unscalable and subjective. The recent involvement of surgical robotic systems in the operating room has facilitated the ability of automated evaluation of the exp...

Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation.

PURPOSE: As deep neural networks achieve more success in the wide field of computer vision, greater emphasis is being placed on the generalizations of these models for production deployment. With sufficiently large training datasets, models can typically avoid overfitting their data; however, for medical imaging it is often difficult to obtain enough data from a single site. Sharing data between i...

A machine learning approach to predict surgical learning curves.

BACKGROUND: Contemporary surgical training programs rely on the repetition of selected surgical motor tasks. Such methodology is inherently open ended...

Fully Automated Segmentation Algorithm for Hematoma Volumetric Analysis in Spontaneous Intracerebral Hemorrhage.

Background and Purpose- Hematoma volume measurements influence prognosis and treatment decisions in patients with spontaneous intracerebral hemorrhage...

The digital surgeon: How big data, automation, and artificial intelligence will change surgical practice.

Exponential growth in computing power, data storage, and sensing technology has led to a world in which we can both capture and analyze incredible amo...

Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.

BACKGROUND: Current guidelines recommend surgical resection as the first-line option for patients with solitary hepatocellular carcinoma (HCC); unfort...

Robotic Cardiac Surgery Part II: Anesthetic Considerations for Robotic Coronary Artery Bypass Grafting.

Coronary artery bypass grafting represents one of the most commonly performed cardiac surgeries worldwide. Recently, interest has increased in providi...

Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks.

For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedur...

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.

PURPOSE: As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this...

Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography.

In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatm...

Real-Time Extraction of Important Surgical Phases in Cataract Surgery Videos.

The present study aimed to conduct a real-time automatic analysis of two important surgical phases, which are continuous curvilinear capsulorrhexis (C...

Segment IV approach for difficult laparoscopic cholecystectomy.

Although achieving the critical view of safety (CVS) is useful for avoiding vasculobiliary injury during laparoscopic cholecystectomy (LC), the CVS ca...

The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study.

BACKGROUND: Postoperative pancreatic fistula remains an unsolved challenge after pancreatoduodenectomy. Important in this regard is the presence of a ...

Machine Learning-Based Prediction Models for 30-Day Readmission after Hospitalization for Chronic Obstructive Pulmonary Disease.

While machine learning approaches can enhance prediction ability, little is known about their ability to predict 30-day readmission after hospitalizat...

Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.

BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed ...

Autoantibodies production and immunological abnormalities after bariatric surgery.

OBJECTIVE: Bariatric surgery is a widely used procedure for the treatment of obesity. Our aim is to describe the main immunological changes in patient...

Comparison of the initial operative experience of a single surgeon carrying out robot-assisted laparoscopic pyeloplasty, laparoendoscopic single-site pyeloplasty and conventional laparoscopic pyeloplasty.

Transanal total mesorectal excision for rectal cancer: a multicentric cohort study.

BACKGROUND: Transanal total mesorectal excision (taTME) has recently emerged as a promising novel surgical procedure for rectal cancer. It is believed...

Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network.

The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decrease...

An artificial neural network ensemble approach to generate air pollution maps.

The objective of this research is to propose an artificial neural network (ANN) ensemble in order to estimate the hourly NO concentration at unsampled...