A Multi-Task Convolutional Neural Network for Semantic Segmentation and Event Detection in Laparoscopic Surgery.

The current study presents a multi-task end-to-end deep learning model for real-time blood accumulation detection and tools semantic segmentation from a laparoscopic surgery video. Intraoperative bleeding is one of the most problematic aspects of laparoscopic surgery. It is challenging to control and limits the visibility of the surgical site.

Vision Transformer for femur fracture classification.

Introduction: In recent years, the scientific community focused on developing Computer-Aided Diagnosis (CAD) tools that could improve clinicians' bone fractures diagnosis, primarily based on Convolutional Neural Networks (CNNs). However, the discerning accuracy of fractures' subtypes was far from optimal. The aim of the study was 1) to evaluate a new CAD system based on Vision Transformers (ViT), a very recent and powerful deep learning technique, and 2) to assess whether clinicians' diagnostic accuracy could be improved using this system.

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.

Dynamic evaluation of THA components by Prosthesis Impingement Software (PIS).

Background and aim of the work Implant dislocation in total hip arthroplasties (THA) is a common concern amongst the orthopedic surgeons and represents the most frequent complication after primary implant. Several causes could be responsible for the dislocation, including the malpositioning of the components. Conventional imaging techniques frequently fail to detect the mechanical source of dislocation mainly because they could not reproduce a dynamic evaluation of the components.

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

Purpose: 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.

Methods: This approach was carried out using a Convolutional Neural Network (CNN) based structure for semantic segmentation and a subsequent elaboration of the obtained output, which produced the needed parameters for attaching the 3D model.

Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach.

Purpose: Suspected fractures are among the most common reasons for patients to visit emergency departments and often can be difficult to detect and analyze them on film scans. Therefore, we aimed to design a Deep Learning-based tool able to help doctors in diagnosis of bone fractures, following the hierarchical classification proposed by the Arbeitsgemeinschaft für Osteosynthesefragen (AO) Foundation and the Orthopaedic Trauma Association (OTA).

Methods: 2453 manually annotated images of proximal femur were used for the classification in different fracture types (1133 Unbroken femur, 570 type A, 750 type B).

Intraoperative surgery room management: A deep learning perspective.

Purpose: 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.

Methods: A literature search with classic databases was performed: we identified, with the use of specific keywords, a total of 996 papers. Among them, we selected 52 for effective analysis, focusing on articles published after January 2015.