Surgery

Predicting the occurrence of surgical site infections using text mining and machine learning.

In this study we propose the use of text mining and machine learning methods to predict and detect Surgical Site Infections (SSIs) using textual descriptions of surgeries and post-operative patients' records, mined from the database of a high complexity University hospital. SSIs are among the most common adverse events experienced by hospitalized patients; preventing such events is fundamental to ...

Role of Image Guided Navigation in Endoscopic Surgery of Paranasal Sinuses: A Comparative Study.

The aim of the current study is to share our experience with surgical outcomes of Functional Endoscopic Sinus Surgery using an image-guidance system. The study was a randomised control trial with the comparison between two groups. Image guidance system (Electromagnetic) was used for endoscopic surgery on patients with disease of the paranasal sinuses (n = 30). Results were compared with those in c...

Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage.

PURPOSE: To analyze the implementation of deep learning software for the detection and worklist prioritization of acute intracranial hemorrhage on non...

Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization.

We conducted a systematic review of literature to better understand the role of new technologies in the perioperative period; in particular we focus o...

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis a...

Representation learning in intraoperative vital signs for heart failure risk prediction.

BACKGROUND: The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascu...

Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage.

Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectiv...

A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves.

Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life...

Deep segmentation leverages geometric pose estimation in computer-aided total knee arthroplasty.

Knee arthritis is a common joint disease that usually requires a total knee arthroplasty. There are multiple surgical variables that have a direct imp...

Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks.

Twin-to-Twin Transfusion Syndrome is commonly treated with minimally invasive laser surgery in fetoscopy. The inter-foetal membrane is used as a refer...

Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation.

In this paper, a novel method of active tactile perception based on 3D neural networks and a high-resolution tactile sensor installed on a robot gripp...

Comparison of the oncological, perioperative and functional outcomes of partial nephrectomy versus radical nephrectomy for clinical T1b renal cell carcinoma: A systematic review and meta-analysis of retrospective studies.

OBJECTIVE: To conduct a meta-analysis assessing the perioperative, functional and oncological outcomes of partial nephrectomy (PN) and radical nephrec...

Twin Robotic X-Ray System for 3D Cone-Beam CT of the Wrist: An Evaluation of Image Quality and Radiation Dose.

The purpose of this study was to assess image quality and radiation dose of a novel twin robotic x-ray system's 3D cone-beam CT (CBCT) function for t...

Towards the Exploitation of Physical Compliance in Segmented and Electrically Actuated Robotic Legs: A Review Focused on Elastic Mechanisms.

Physical compliance has been increasingly used in robotic legs, due to its advantages in terms of the mechanical regulation of leg mechanics and energ...

Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach.

BACKGROUND: Automatic surgical workflow recognition is a key component for developing the context-aware computer-assisted surgery (CA-CAS) systems. Ho...

Re: Robotic Transanal Minimally Invasive Rectal Mucosa Harvest.

Re: Perioperative Outcomes and Complications after Robotic Radical Cystectomy with Intracorporeal or Extracorporeal Ileal Conduit Urinary Diversion: Head-to-Head Comparison from a Single-Institutional Prospective Study.

Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study.

INTRODUCTION: About 42 million surgeries are performed annually in the USA. While the postoperative mortality is less than 2%, 12% of all patients in ...

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.

IMPORTANCE: Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate ...

A “human-proof pointy-end”: a robotically applied hemostatic clamp for care-under-fire.

Providing the earliest hemorrhage control is now recognized as a shared responsibility of all members of society, including both the lay public and pr...