Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient's organ with its 2D endoscopic image, to assist surgeons during the procedure

6D object position estimation from 2D images: a literature review

The 6D pose estimation of an object from an image is a central problem in many domains of Computer Vision (CV) and researchers have struggled with this issue for several years. Traditional pose estimation methods (1) leveraged on geometrical approaches, exploiting manually annotated local features, or (2) relied on 2D object representations from different points of view and their comparisons with the original image. The two methods mentioned above are also known as Feature-based and Template-based, respectively. With the diffusion of Deep Learning (DL), new Learning-based strategies have been introduced to achieve the 6D pose estimation, improving traditional methods by involving Convolutional Neural Networks (CNN). This review analyzed techniques belonging to different research fields and classified them into three main categories: Template-based methods, Feature-based methods, and Learning-Based methods. In recent years, the research mainly focused on Learning-based methods, which allow the training of a neural network tailored for a specific task. For this reason, most of the analyzed methods belong to this category, and they have been in turn classified into three sub-categories: Bounding box prediction and Perspective-n-Point (PnP) algorithm-based methods, Classification-based methods, and Regression-based methods. This review aims to provide a general overview of the latest 6D pose recovery methods to underline the pros and cons and highlight the best-performing techniques for each group. The main goal is to supply the readers with helpful guidelines for the implementation of performing applications even under challenging circumstances such as auto-occlusions, symmetries, occlusions between multiple objects, and bad lighting conditions

A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery

Introduction The current study presents a deep learning framework to determine, in real-time, position and rotation of a target organ from an endoscopic video. These inferred data are used to overlay the 3D model of patient's organ over its real counterpart. The resulting augmented video flow is streamed back to the surgeon as a support during laparoscopic robot-assisted procedures. Methods This framework exploits semantic segmentation and, thereafter, two techniques, based on Convolutional Neural Networks and motion analysis, were used to infer the rotation. Results The segmentation shows optimal accuracies, with a mean IoU score greater than 80% in all tests. Different performance levels are obtained for rotation, depending on the surgical procedure. Discussion Even if the presented methodology has various degrees of precision depending on the testing scenario, this work sets the first step for the adoption of deep learning and augmented reality to generalise the automatic registration process

[Management of acute chest pain in the emergency department]

Acute chest pain is one of the most common symptoms in emergency departments. Immediate assessment is mandatory on arrival in order to ensure the appropriate care. Diagnostic work-up should be based on conventional tools, i.e. clinical presentation, physical examination, electrocardiogram, as well as on modern information, i.e. biochemical markers of myocardial damage or provocative tests. Firstly, physicians should assess the likelihood that signs and symptoms have a cardiac origin secondary to coronary artery disease. Afterwards, the risk for ischemic complications should be stratified. To this end, several scores have been derived from clinical trials in order to improve prediction of outcome. Also, use of critical pathways can improve guideline adherence. In the "real world", a variety of barriers to optimal management of acute chest pain still exists. An agreement on specific protocols is often difficult to achieve between different specialties. Also, no official guidelines on low-risk chest pain patients or patients with non-cardiac chest pain are available. Finally, the minimal data set of diagnostic tools that should be applied in case of acute chest pain in any emergency setting is still lacking

Intraoperative surgery room management: A deep learning perspective

The current study aimed to systematically review the literature addressing the use of deep learning (DL) methods in intraoperative surgery applications, focusing on the data collection, the objectives of these tools and, more technically, the DL-based paradigms utilized

Critical pathways in the emergency department improve treatment modalities for patients with ST-elevation myocardial infarction in a European hospital

The use of protocols for patients with ST-elevation myocardial infarction (MI) is growing, but no definite conclusion regarding the value of critical pathways in Europe has been drawn