Classification of AO/OTA 31A/B femur fractures in X-ray images using YOLOv8 and advanced data augmentation techniques.

Femur fractures are a significant worldwide public health concern that affects patients as well as their families because of their high frequency, morbidity, and mortality. When employing computer-aided diagnostic (CAD) technologies, promising results have been shown in the efficiency and accuracy of fracture classification, particularly with the growing use of Deep Learning (DL) approaches. Nevertheless, the complexity is further increased by the need to collect enough input data to train these algorithms and the challenge of interpreting the findings.

Workplace Well-Being in Industry 5.0: A Worker-Centered Systematic Review.

The paradigm of Industry 5.0 pushes the transition from the traditional to a novel, smart, digital, and connected industry, where well-being is key to enhance productivity, optimize man-machine interaction and guarantee workers' safety. This work aims to conduct a systematic review of current methodologies for monitoring and analyzing physical and cognitive ergonomics.

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy.

Background: Addressing intraoperative bleeding remains a significant challenge in the field of robotic surgery. This research endeavors to pioneer a groundbreaking solution utilizing convolutional neural networks (CNNs). The objective is to establish a system capable of forecasting instances of intraoperative bleeding during robot-assisted radical prostatectomy (RARP) and promptly notify the surgeon about bleeding risks.

Understanding the contagiousness of Covid-19 strains: A geometric approach.

Protein-protein interaction occurs on surface patches with some degree of complementary geometric and chemical features. Building on this understanding, this study endeavors to characterize the spike protein of the SARS-CoV-2 virus at the morphological and geometrical levels in its Alpha, Delta, and Omicron variants. In particular, the affinity between different SARS-CoV-2 spike proteins and the ACE2 receptor present on the membrane of the human respiratory system cells is investigated.

Do facial soft tissue thicknesses change after surgeries correcting dental malocclusions? An intra- and inter-patient statistical analysis on soft-tissue thicknesses in BSSO + LFI surgeries.

Objectives: The aim of this study was to analyse changes in facial soft tissue thickness (FSTT) after corrective surgeries for dental malocclusion. The correlation between body mass index (BMI) and sex of patients and their FSTT before undergoing surgery was analysed.

Materials And Methods: Cone beam computed tomography of seventeen patients that underwent Le Fort I osteotomy in combination with bilateral sagittal split osteotomy were collected.

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.

The impact of 3D models on positive surgical margins after robot-assisted radical prostatectomy.

Purpose: To evaluate the role of 3D models on positive surgical margin rate (PSM) rate in patients who underwent robot-assisted radical prostatectomy (RARP) compared to a no-3D control group. Secondarily, we evaluated the postoperative functional and oncological outcomes.

Methods: Prospective study enrolling patients with localized prostate cancer (PCa) undergoing RARP with mp-MRI-based 3D model reconstruction, displayed in a cognitive or augmented-reality fashion, at our Centre from 01/2016 to 01/2020.

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.

Indocyanine Green Drives Computer Vision Based 3D Augmented Reality Robot Assisted Partial Nephrectomy: The Beginning of "Automatic" Overlapping Era.

Augmented reality robot-assisted partial nephrectomy (AR-RAPN) is limited by the need of a constant manual overlapping of the hyper-accuracy 3D (HA3D) virtual models to the real anatomy. To present our preliminary experience with automatic 3D virtual model overlapping during AR-RAPN. To reach a fully automated HA3D model overlapping, we pursued computer vision strategies, based on the identification of landmarks to link the virtual model.

Development of an affective database made of interactive virtual environments.

Despite the great potential of Virtual Reality (VR) to arouse emotions, there are no VR affective databases available as it happens for pictures, videos, and sounds. In this paper, we describe the validation of ten affective interactive Virtual Environments (VEs) designed to be used in Virtual Reality. These environments are related to five emotions.

Affective Virtual Reality: How to Design Artificial Experiences Impacting Human Emotions.

Computer graphics is-in many cases-about visualizing what you cannot see. However, virtual reality (VR), from its beginnings, aimed at stimulating all human senses: not just the visual channel. Moreover, this set of multisensory stimuli allows users to feel present and able to interact with the virtual environment.

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.

Three-Dimensional Evaluation of Soft Tissue Malar Modifications after Zygomatic Valgization Osteotomy via Geometrical Descriptors.

Patients with severe facial deformities present serious dysfunctionalities along with an unsatisfactory aesthetic facial appearance. Several methods have been proposed to specifically plan the interventions on the patient's needs, but none of these seem to achieve a sufficient level of accuracy in predicting the resulting facial appearance. In this context, a deep knowledge of what occurs in the face after bony movements in specific surgeries would give the possibility to develop more reliable systems.

Malar augmentation with zygomatic osteotomy in orthognatic surgery: Bone and soft tissue changes threedimensional evaluation.

Background: The aim of this prospective study is to objectively assess 3D soft tissue and bone changes of the malar region by using the malar valgization osteotomy in concomitant association with orthognatic surgery.

Materials And Methods: From January 2015 to January 2018, 10 patients who underwent single stage bilateral malar valgization osteotomy in conjunction with maxillo-mandibular orthognatic procedures for aesthetic and functional correction were evaluated. Clinical and surgical reports were collected and patient satisfaction was evaluated with a VAS score.

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).

A new method for protein characterization and classification using geometrical features for 3D face analysis: An example of tubulin structures.

This article reports on the results of research aimed to translate biometric 3D face recognition concepts and algorithms into the field of protein biophysics in order to precisely and rapidly classify morphological features of protein surfaces. Both human faces and protein surfaces are free-forms and some descriptors used in differential geometry can be used to describe them applying the principles of feature extraction developed for computer vision and pattern recognition. The first part of this study focused on building the protein dataset using a simulation tool and performing feature extraction using novel geometrical descriptors.

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.

3D augmentation of the surgical video stream: Toward a modular approach.

Background And Objective: We present an original approach to the development of augmented reality (AR) real-time solutions for robotic surgery navigation. The surgeon operating the robotic system through a console and a visor experiences reduced awareness of the operatory scene. In order to improve the surgeon's spatial perception during robot-assisted minimally invasive procedures, we provide him/her with a solid automatic software system to position, rotate and scale in real-time the 3D virtual model of a patient's organ aligned over its image captured by the endoscope.

Artificial intelligence and neural networks in urology: current clinical applications.

Introduction: As we enter the era of "big data," an increasing amount of complex health-care data will become available. These data are often redundant, "noisy," and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the artificial intelligence (AI) with machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future.

Optimal marker set assessment for motion capture of 3D mimic facial movements.

Nowadays, facial mimicry studies have acquired a great importance in the clinical domain and 3D motion capture systems are becoming valid tools for analysing facial muscles movements, thanks to the remarkable developments achieved in the 1990s. However, the face analysis domain suffers from a lack of valid motion capture protocol, due to the complexity of the human face. Indeed, a framework for defining the optimal marker set layout does not exist yet and, up to date, researchers still use their traditional facial point sets with manually allocated markers.

3D landmarking in multiexpression face analysis: a preliminary study on eyebrows and mouth.

Unlabelled: The application of three-dimensional (3D) facial analysis and landmarking algorithms in the field of maxillofacial surgery and other medical applications, such as diagnosis of diseases by facial anomalies and dysmorphism, has gained a lot of attention. In a previous work, we used a geometric approach to automatically extract some 3D facial key points, called landmarks, working in the differential geometry domain, through the coefficients of fundamental forms, principal curvatures, mean and Gaussian curvatures, derivatives, shape and curvedness indexes, and tangent map. In this article we describe the extension of our previous landmarking algorithm, which is now able to extract eyebrows and mouth landmarks using both old and new meshes.

A pose-independent method for 3D face landmark formalization.

Recently, 3D landmark extraction has been widely researched and experimented in medical field, for both corrective and aesthetic purposes. Automation of these procedures on three-dimensional face renderings is something desirable for the specialists who work in this field. In this work we propose a new method for accurate landmark localization on facial scans.

Soft tissue diagnosis in maxillofacial surgery: a preliminary study on three-dimensional face geometrical features-based analysis.

To obtain the best surgical results in orthognathic surgery, treatment planning and evaluation of results should be performed. In these operations it is necessary to provide the physician with powerful tools that can underline the behavior of soft tissue. For this reason, considering the improvements provided by the use of 3D scanners in medical diagnosis, we propose a methodology for analyzing facial morphology working with geometrical features.