Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature.

Background And Aim: Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the performance of image recognition. Herein, we thoroughly analyze and evaluate proposed AI models in the field of HCC diagnosis.

Prediction of remaining surgery duration in laparoscopic videos based on visual saliency and the transformer network.

Background: Real-time prediction of the remaining surgery duration (RSD) is important for optimal scheduling of resources in the operating room.

Methods: We focus on the intraoperative prediction of RSD from laparoscopic video. An extensive evaluation of seven common deep learning models, a proposed one based on the Transformer architecture (TransLocal) and four baseline approaches, is presented.

Surgical Gesture Recognition in Laparoscopic Tasks Based on the Transformer Network and Self-Supervised Learning.

In this study, we propose a deep learning framework and a self-supervision scheme for video-based surgical gesture recognition. The proposed framework is modular. First, a 3D convolutional network extracts feature vectors from video clips for encoding spatial and short-term temporal features.

Multiple instance convolutional neural network for gallbladder assessment from laparoscopic images.

Background: We present an artificial intelligence framework for vascularity classification of the gallbladder (GB) wall from intraoperative images of laparoscopic cholecystectomy (LC).

Methods: A two-stage Multiple Instance Convolutional Neural Network is proposed. First, a convolutional autoencoder is trained to extract feature representations from 4585 patches of GB images.

Efficiency and Safety of Patent Ductus Arteriosus Surgical Ligation in Extremely Low Birth Weight Infants Without Chest Tube Placement.

Patent ductus arteriosus (PDA) has been associated with increased morbidity and mortality in preterm infants. Surgical ligation (SL) is generally performed in symptomatic infants when medical management is contraindicated or has failed. We retrospectively reviewed our institution's experience in surgical management of PDA for extremely low birth weight (ELBW) infants without chest tube placement assessing its efficiency and safety.

Lung cancer histology classification from CT images based on radiomics and deep learning models.

Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC), responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histology classification into AC and SCC by applying different feature extraction and classification techniques on pre-treatment CT images. The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA).

Patch-based classification of gallbladder wall vascularity from laparoscopic images using deep learning.

Purpose: In this study, we propose a deep learning approach for assessment of gallbladder (GB) wall vascularity from images of laparoscopic cholecystectomy (LC). Difficulty in the visualization of GB wall vessels may be the result of fatty infiltration or increased thickening of the GB wall, potentially as a result of cholecystitis or other diseases.

Methods: The dataset included 800 patches and 181 region outlines of the GB wall extracted from 53 operations of the Cholec80 video collection.

Surgical Performance Analysis and Classification Based on Video Annotation of Laparoscopic Tasks.

Background And Objectives: Current approaches in surgical skills assessment employ virtual reality simulators, motion sensors, and task-specific checklists. Although accurate, these methods may be complex in the interpretation of the generated measures of performance. The aim of this study is to propose an alternative methodology for skills assessment and classification, based on video annotation of laparoscopic tasks.

Performance Assessment of Subjects With Nursing Education Trained in Sigmoidoscopy by Means of a Simulator.

Reports evaluating simulation-based sigmoidoscopy training among nurses are scarce. The aim of this prospective nonrandomized study was to assess the performance of nurses in simulated sigmoidoscopy training and the potential impact on their performance of endoscopy unit experience, general professional experience, and skills in manual activities requiring coordinated maneuvers. Forty-four subjects were included: 12 nurses with (Group A) and 14 nurses without endoscopy unit experience (Group B) as well as 18 senior nursing students (Group C).

Multi-instance multi-label learning for surgical image annotation.

Background: Various techniques have been proposed in the literature for phase and tool recognition from laparoscopic videos. In comparison, research in multilabel annotation of still frames is limited.

Methods: We describe a framework for multilabel annotation of images extracted from laparoscopic cholecystectomy (LC) videos based on multi-instance multiple-label learning.

Keyframe extraction from laparoscopic videos based on visual saliency detection.

Background And Objective: Laparoscopic surgery offers the potential for video recording of the operation, which is important for technique evaluation, cognitive training, patient briefing and documentation. An effective way for video content representation is to extract a limited number of keyframes with semantic information. In this paper we present a novel method for keyframe extraction from individual shots of the operational video.

Video content analysis of surgical procedures.

Background: In addition to its therapeutic benefits, minimally invasive surgery offers the potential for video recording of the operation. The videos may be archived and used later for reasons such as cognitive training, skills assessment, and workflow analysis. Methods from the major field of video content analysis and representation are increasingly applied in the surgical domain.

An automated skills assessment framework for laparoscopic training tasks.

Background: Various sensors and methods are used for evaluating trainees' skills in laparoscopic procedures. These methods are usually task-specific and involve high costs or advanced setups.

Methods: In this paper, we propose a novel manoeuver representation feature space (MRFS) constructed by tracking the vanishing points of the edges of the graspers on the video sequence frames, acquired by the standard box trainer camera.

Pulmonary valve replacement in patients with corrected tetralogy of Fallot.

Development of pulmonary insufficiency in patients with surgically corrected tetralogy of Fallot (TOF) may lead to severe right heart failure with serious consequences. We herein present our experience with pulmonary valve replacement (PVR) in these patients. From 2005-2013, 99 consecutive patients (71 males/28 females, mean age 38±8 years), underwent PVR after 7 to 40 (mean 29 ± 8) years from the initial correction.

Video analysis in basic skills training: a way to expand the value and use of BlackBox training?

Background: Basic skills training in laparoscopic high-fidelity simulators (LHFS) improves laparoscopic skills. However, since LHFS are expensive, their availability is limited. The aim of this study was to assess whether automated video analysis of low-cost BlackBox laparoscopic training could provide an alternative to LHFS in basic skills training.

Virtual reality-based assessment of basic laparoscopic skills using the Leap Motion controller.

Background: The majority of the current surgical simulators employ specialized sensory equipment for instrument tracking. The Leap Motion controller is a new device able to track linear objects with sub-millimeter accuracy. The aim of this study was to investigate the potential of a virtual reality (VR) simulator for assessment of basic laparoscopic skills, based on the low-cost Leap Motion controller.

Shot boundary detection in endoscopic surgery videos using a variational Bayesian framework.

Purpose: Over the last decade, the demand for content management of video recordings of surgical procedures has greatly increased. Although a few research methods have been published toward this direction, the related literature is still in its infancy. In this paper, we address the problem of shot detection in endoscopic surgery videos, a fundamental step in content-based video analysis.

A user-oriented procedure for the commissioning and quality assurance testing of treatment planning system dosimetry in high-dose-rate brachytherapy.

Purpose: To develop a user-oriented procedure for testing treatment planning system (TPS) dosimetry in high-dose-rate brachytherapy, with particular focus to TPSs using model-based dose calculation algorithms (MBDCAs).

Methods And Materials: Identical plans were prepared for three computational models using two commercially available systems and the same (192)Ir source. Reference dose distributions were obtained for each plan using the MCNP v.

Performance comparison of various feature detector-descriptors and temporal models for video-based assessment of laparoscopic skills.

Background: Despite the significant progress in hand gesture analysis for surgical skills assessment, video-based analysis has not received much attention. In this study we investigate the application of various feature detector-descriptors and temporal modeling techniques for laparoscopic skills assessment.

Methods: Two different setups were designed: static and dynamic video-histogram analysis.

Changes in Simple Spirometric Parameters After Lobectomy for Bronchial Carcinoma.

Introduction: The purpose of this study was to describe the postoperative changes in lung function after pure open lobectomy for lung carcinoma.

Methods: 30 patients (mean age 64 ± 7 years old, 16 men and 14 women) underwent a left or right lobectomy. They underwent spirometric pulmonary tests preoperatively, and at 1 and 6 months after the operation.

A simple sensor calibration technique for estimating the 3D pose of endoscopic instruments.

Introduction: The aim of this study was to describe a simple and easy-to-use calibration method that is able to estimate the pose (tip position and orientation) of a rigid endoscopic instrument with respect to an electromagnetic tracking device attached to the handle.

Methods: A two-step calibration protocol was developed. First, the orientation of the instrument shaft is derived by performing a 360° rotation of the instrument around its shaft using a firmly positioned surgical trocar.

The Effect of Mixed-Task Basic Training in the Acquisition of Advanced Laparoscopic Skills.

The aim of this study was to assess whether mixed practice of basic tasks on a virtual reality (VR) simulator improves the performance of advanced tasks on the same device used for training as well as on a video trainer (VT). Thirty-six novices were allocated into 3 equal groups. Each group practiced on different combinations of basic tasks on a VR simulator: (A) peg transfer, (B) peg transfer and clipping, and (C) peg transfer, clipping, and cutting.

A novel augmented reality simulator for skills assessment in minimal invasive surgery.

Introduction: Over the past decade, simulation-based training has come to the foreground as an efficient method for training and assessment of surgical skills in minimal invasive surgery. Box-trainers and virtual reality (VR) simulators have been introduced in the teaching curricula and have substituted to some extent the traditional model of training based on animals or cadavers. Augmented reality (AR) is a new technology that allows blending of VR elements and real objects within a real-world scene.