Predicting vulnerable coronary arteries: A combined radiomics-biomechanics approach.

Background And Objective: Nowadays, vulnerable coronary plaque detection from coronary computed tomography angiography (CCTA) is suboptimal, although being crucial for preventing major adverse cardiac events. Moreover, despite the suggestion of various vulnerability biomarkers, encompassing image and biomechanical factors, accurate patient stratification remains elusive, and a comprehensive approach integrating multiple markers is lacking. To this aim, this study introduces an innovative approach for assessing vulnerable coronary arteries and patients by integrating radiomics and biomechanical markers through machine learning methods.

Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality.

Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relies on the quality and quantity of the training data. Most of such works employed small-scale public datasets, without proving the efficacy of generalization to external datasets.

Validation of Step Detection and Distance Calculation Algorithms for Soccer Performance Monitoring.

This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created.

Simultaneous in-air and underwater 3D kinematic analysis of swimmers: Feasibility and reliability of action sport cameras.

This study explored the potential of reconstructing the 3D motion of a swimmer's hands with accuracy and consistency using action sport cameras (ASC) distributed in-air and underwater. To record at least two stroke cycles of an athlete performing a front crawl task, the cameras were properly calibrated to cover an acquisition volume of 3 m in X, 8 m in Y, and 3.5 m in Z axis, approximately.

Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools.

Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment.

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy.

Objective: Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization.

Combined Edge Loss UNet for Optimized Segmentation in Total Knee Arthroplasty Preoperative Planning.

Bone segmentation and 3D reconstruction are crucial for total knee arthroplasty (TKA) surgical planning with Personalized Surgical Instruments (PSIs). Traditional semi-automatic approaches are time-consuming and operator-dependent, although they provide reliable outcomes. Moreover, the recent expansion of artificial intelligence (AI) tools towards various medical domains is transforming modern healthcare.

Breathing Motion Pattern in Cyclists: Role of Inferior against Superior Thorax Compartment.

The thoracoabdominal breathing motion pattern is being considered in sports training because of its contribution, along with other physiological adaptations, to overall performance. We examined whether and how experience with cycling training modifies the thoracoabdominal motion patterns. We utilized optoelectronic plethysmography to monitor ten trained male cyclists and compared them to ten physically active male participants performing breathing maneuvers.

A new system for in-room image guidance in particle therapy at CNAO.

This paper describes the design, installation, and commissioning of an in-room imaging device developed at the Centro Nazionale di Adroterapia Oncologica (CNAO, Pavia, Italy). The system is an upgraded version of the one previously installed in 2014, and its design accounted for the experience gained in a decade of clinical practice of patient setup verification and correction through robotic-supported, off-isocenter in-room image guidance. The system's basic feature consists of image-based setup correction through 2D/3D and 3D/3D registration through a dedicated HW/SW platform.

Analytical Challenges in Diabetes Management: Towards Glycated Albumin Point-of-Care Detection.

Diabetes mellitus is a worldwide-spread chronic metabolic disease that occurs when the pancreas fails to produce enough insulin levels or when the body fails to effectively use the secreted pancreatic insulin, eventually resulting in hyperglycemia. Systematic glycemic control is the only procedure at our disposal to prevent diabetes long-term complications such as cardiovascular disorders, kidney diseases, nephropathy, neuropathy, and retinopathy. Glycated albumin (GA) has recently gained more and more attention as a control biomarker thanks to its shorter lifespan and wider reliability compared to glycated hemoglobin (HbA1c), currently the "gold standard" for diabetes screening and monitoring in clinics.

CEL-Unet: a novel CNN architecture for 3D Segmentation of Knee Bones affected by Severe Osteoarthritis for PSI-Based Surgical Planning.

Unet architectures are promising deep learning networks exploited to perform the automatic segmentation of bone CT images, in line with their ability to deal with pathological deformations and size-varying anatomies. However, bone degeneration, like the development of irregular osteophytes as well as mineral density alterations might interfere with this automated process and demand extensive manual refinement. The aim of this work is to implement an innovative Unet variant, the CEL-Unet, to improve the femur and tibia segmentation outcomes in osteoarthritic knee joints.

Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks.

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task).

Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images.

The Foreign body response (FBR) is a major unresolved challenge that compromises medical implant integration and function by inflammation and fibrotic encapsulation. Mice implanted with polymeric scaffolds coupled to intravital non-linear multiphoton microscopy acquisition enable multiparametric, longitudinal investigation of the FBR evolution and interference strategies. However, follow-up analyses based on visual localization and manual segmentation are extremely time-consuming, subject to human error, and do not allow for automated parameter extraction.

Hybrid Convolutional Networks for End-to-End Event Detection in Concurrent PPG and PCG Signals Affected by Motion Artifacts.

The accurate detection of physiologically-related events in photopletismographic (PPG) and phonocardiographic (PCG) signals, recorded by wearable sensors, is mandatory to perform the estimation of relevant cardiovascular parameters like the heart rate and the blood pressure. However, the measurement performed in uncontrolled conditions without clinical supervision leaves the detection quality particularly susceptible to noise and motion artifacts. This work proposes a new fully-automatic computational framework, based on convolutional networks, to identify and localize fiducial points in time as the foot, maximum slope and peak in PPG signal and the S1 sound in the PCG signal, both acquired by a custom chest sensor, described recently in the literature by our group.

Septic arthritis following joint injections: a 17 years retrospective study in an Academic General Hospital.

Background: Septic arthritis following intra-articular infiltrations is an uncommon devastating complication correlated  to high costs for the health service and often to poor outcomes. The purpose of this study is to assess a 17-years experience in a single academic multispecialist hospital managing this uncommon complication in Orthopaedic practice.

Methods: Patients with diagnosis of septic arthritis following joint injections treated in our hospital from January 2002 to December 2019 were included in the study.

Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning.

Purpose: Cone beam computed tomography (CBCT) is a standard solution for in-room image guidance for radiation therapy. It is used to evaluate and compensate for anatomopathological changes between the dose delivery plan and the fraction delivery day. CBCT is a fast and versatile solution, but it suffers from drawbacks like low contrast and requires proper calibration to derive density values.

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT.

Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs.

Is age rating enough to investigate changes in breathing motion pattern associated with aging of physically active women?

The most common way to analyze the effect of aging on breathing is to divide subjects into age groups. However, in addition to the fact that there is no consensus in the literature regarding age group division, such design critically influences the interpretation of the effects attributed to aging. Thus, this study aimed to investigate the feasibility to distinguish different age groups from the 3D kinematic variables of breathing motion (i.

Humeral head necrosis associated to shaft non-union with massive bone loss: a case report.

Humeral non-union is a rare complication in shaft fractures, as well as humeral head necrosis is a possible complication in fracture involving the proximal third especially in four-part fractures. The presence of head osteonecrosis and diaphyseal non-union in the same arm represents a formidable challenge for an orthopaedic surgeon. We could not find any similar report in the literature dealing with this issue thus far.

Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model.

Statistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread in a set of matching surfaces by means of compact descriptive quantities, traditionally called "modes of variation" (MoVs). SSMs of bony surfaces have been proposed in biomechanics and orthopedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate the relation between MoVs and bone angular deformities causing knee instability.

A multi-metric registration strategy for the alignment of longitudinal brain images in pediatric oncology.

Survival of pediatric patients with brain tumor has increased over the past 20 years, and increasing evidence of iatrogenic toxicities has been reported. In follow-ups, images are acquired at different time points where substantial changes of brain morphology occur, due to childhood physiological development and treatment effects. To address the image registration complexity, we propose two multi-metric approaches (M, M), combining mutual information (MI) and normalized gradient field filter (NGF).

Reducing the Number of Degrees of Freedom to Control an Eye Surgical Robot through Classification of Surgical Phases.

This paper introduces an optimized input device workflow to control an eye surgical robot in a simulated vitreoretinal environment. The input device is a joystick with four Degrees of Freedom (DOF) that controls a six DOFs robot. This aim is achieved through a segmentation plan for an eye surgeon.

A Metal Oxide Gas Sensors Array for Lung Cancer Diagnosis Through Exhaled Breath Analysis.

Lung cancer high mortality rate is mainly related to late-stage tumor diagnosis. Survival rates and treatments could be greatly improved with an effective early diagnosis. Volatile organic compounds (VOCs) in exhaled breath have been known for long to be linked to the presence of a disease.

Non artheritic bilateral anterior ischaemic optic neuropathy (NAION) as devastating complication following Total Hip Arthroplasty: a case report.

Introduction: Postoperative vision loss (PVL) is an extremely rare complication following major surgical procedures. Patients with systemic hypertension, diabetes, coronary diseases and smokers are generally predisposed to this complication. More frequently, it is caused by ischemic optic neuropathy (ION), central retinal artery occlusion or retinal vein occlusion.