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. Three research questions are addressed: (1) which technologies are used to assess the physical and cognitive well-being of workers in the workplace, (2) how the acquired data are processed, and (3) what purpose this well-being is evaluated for. This way, individual factors within the holistic assessment of worker well-being are highlighted, and information is provided synthetically. The analysis was conducted following the PRISMA 2020 statement guidelines. From the sixty-five articles collected, the most adopted (1) technological solutions, (2) parameters, and (3) data analysis and processing were identified. Wearable inertial measurement units and RGB-D cameras are the most prevalent devices used for physical monitoring; in the cognitive ergonomics, and cardiac activity is the most adopted physiological parameter. Furthermore, insights on practical issues and future developments are provided. Future research should focus on developing multi-modal systems that combine these aspects with particular emphasis on their practical application in real industrial settings.

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. METHODS: To achieve this, a multi-task learning (MTL) CNN was introduced, leveraging a modified version of the U-Net architecture. The aim was to categorize video input as either "absence of blood accumulation" (0) or "presence of blood accumulation" (1). To facilitate seamless interaction with the neural networks, the Bleeding Artificial Intelligence-based Detector (BLAIR) software was created using the Python Keras API and built upon the PyQT framework. A subsequent clinical assessment of BLAIR's efficacy was performed, comparing its bleeding identification performance against that of a urologist. Various perioperative variables were also gathered. For optimal MTL-CNN training parameterization, a multi-task loss function was adopted to enhance the accuracy of event detection by taking advantage of surgical tools' semantic segmentation. Additionally, the Multiple Correspondence Analysis (MCA) approach was employed to assess software performance. RESULTS: The MTL-CNN demonstrated a remarkable event recognition accuracy of 90.63%. When evaluating BLAIR's predictive ability and its capacity to pre-warn surgeons of potential bleeding incidents, the density plot highlighted a striking similarity between BLAIR and human assessments. In fact, BLAIR exhibited a faster response. Notably, the MCA analysis revealed no discernible distinction between the software and human performance in accurately identifying instances of bleeding. CONCLUSION: The BLAIR software proved its competence by achieving over 90% accuracy in predicting bleeding events during RARP. This accomplishment underscores the potential of AI to assist surgeons during interventions. This study exemplifies the positive impact AI applications can have on surgical procedures.

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. Hard and soft tissue landmarks were selected basing on the interventions. FSTT were computed, and measurements from pre- to post-operative were compared. The relationship between FSTT, sex, and BMI was investigated. RESULTS: Considering the comparison between pre- and post-operative measurements, any significant difference emerged (p > .05). The Pearson's correlation coefficient computed between BMI and the FSTT (pre-operative) showed a correlation in normal-weight patients; the region-specific analysis highlighted a stronger correlation for specific landmarks. Higher median values emerged for women than for men; the subset-based analysis showed that women presented higher values in the malar region, while men presented higher values in the nasal region. CONCLUSIONS: The considered surgeries did not affect the FSTT of the patients; differences related to BMI and sex were found. A collection of FSTT mean values was provided for twenty landmarks of pre- and post-operative of female and male subjects. CLINICAL RELEVANCE: This exploratory analysis gave insights on the behaviour of STT after maxillofacial surgeries that can be applied in the development of predictive methodologies for soft tissue displacements and to study modifications in the facial aspect of the patients.

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.

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. This study aims to propose a novel 3D approach for the evaluation of soft tissue zygomatic modifications after zygomatic osteotomy; geometrical descriptors usually involved in face analysis tasks, i.e., face recognition and facial expression recognition, are here applied to soft tissue malar region to detect changes in surface shape. As ground truth for zygomatic changes, a zygomatic openness angular measure is adopted. The results show a high sensibility of geometrical descriptors in detecting shape modification of the facial surface, outperforming the results obtained from the angular evaluation.

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. The method relies on geometrical descriptors, such as curvatures and Shape Index, for computing candidate and initial points, and on a statistical model based on Procrustes Analysis and Principal Component Analysis, which is fitted to candidate points, for extracting the final landmarks. The elaborated method is independent on face pose.

Computer-aided morphological analysis for maxillo-facial diagnostic: a preliminary study.

This article compares most of the three-dimensional (3D) morphometric methods currently proposed by the technical literature to evaluate their morphological informative value, while applying them to a case study of five patients affected by the malocclusion pathology. The compared methods are: conventional cephalometric analysis (CCA), generalised Procrustes superimposition (GPS) with principal-components analysis (PCA), thin-plate spline analysis (TPS), multisectional spline (MS) and clearance vector mapping (CVM). The results show that MS provides more reliable and useful diagnostic information.