Economical hybrid novelty detection leveraging global aleatoric semantic uncertainty for enhanced MRI-based ACL tear diagnosis.

This study presents an innovative hybrid deep learning (DL) framework that reformulates the sagittal MRI-based anterior cruciate ligament (ACL) tear classification task as a novelty detection problem to tackle class imbalance. We introduce a highly discriminative novelty score, which leverages the aleatoric semantic uncertainty as this is modeled in the class scores outputted by the YOLOv5-nano object detection (OD) model. To account for tissue continuity, we propose using the global scores (probability vector) when the model is applied to the entire sagittal sequence.

Dense Multi-Scale Graph Convolutional Network for Knee Joint Cartilage Segmentation.

In this paper, we propose a dense multi-scale adaptive graph convolutional network () method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously.

Identifying Soccer Teams' Styles of Play: A Scoping and Critical Review.

Identifying and measuring soccer playing styles is a very important step toward a more effective performance analysis. Exploring the different game styles that a team can adopt to enable a great performance remains under-researched. To address this challenge and identify new directions in future research in the area, this paper conducted a critical review of 40 research articles that met specific criteria.

Identifying Gait-Related Functional Outcomes in Post-Knee Surgery Patients Using Machine Learning: A Systematic Review.

Modern lifestyles require new tools for determining a person's ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the aforementioned requirements and bridge the knowledge gap.

Global Distribution, Dispersal Patterns, and Trend of Several Omicron Subvariants of SARS-CoV-2 across the Globe.

Our study aims to describe the global distribution and dispersal patterns of the SARS-CoV-2 Omicron subvariants. Genomic surveillance data were extracted from the CoV-Spectrum platform, searching for BA.1*, BA.

A novel multi-atlas segmentation approach under the semi-supervised learning framework: Application to knee cartilage segmentation.

Background And Objective: Multi-atlas based segmentation techniques, which rely on an atlas library comprised of training images labeled by an expert, have proven their effectiveness in multiple automatic segmentation applications. However, the usage of exhaustive patch libraries combined with the voxel-wise labeling incur a large computational cost in terms of memory requirements and execution times.

Methods: To confront this shortcoming, we propose a novel two-stage multi-atlas approach designed under the Semi-Supervised Learning (SSL) framework.

Evaluation of anterior cruciate ligament surgical reconstruction through finite element analysis.

Anterior cruciate ligament (ACL) tear is one of the most common knee injuries. The ACL reconstruction surgery aims to restore healthy knee function by replacing the injured ligament with a graft. Proper selection of the optimal surgery parameters is a complex task.

Leveraging explainable machine learning to identify gait biomechanical parameters associated with anterior cruciate ligament injury.

Anterior cruciate ligament (ACL) deficient and reconstructed knees display altered biomechanics during gait. Identifying significant gait changes is important for understanding normal and ACL function and is typically performed by statistical approaches. This paper focuses on the development of an explainable machine learning (ML) empowered methodology to: (i) identify important gait kinematic, kinetic parameters and quantify their contribution in the diagnosis of ACL injury and (ii) investigate the differences in sagittal plane kinematics and kinetics of the gait cycle between ACL deficient, ACL reconstructed and healthy individuals.

Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning.

Coronavirus disease 2019 (COVID-19) has resulted in approximately 5 million deaths around the world with unprecedented consequences in people's daily routines and in the global economy. Despite vast increases in time and money spent on COVID-19-related research, there is still limited information about the factors at the country level that affected COVID-19 transmission and fatality in EU. The paper focuses on the identification of these risk factors using a machine learning (ML) predictive pipeline and an associated explainability analysis.

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review.

The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning.

Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology.

Knee Osteoarthritis (ΚΟΑ) is a degenerative joint disease of the knee that results from the progressive loss of cartilage. Due to KOA's multifactorial nature and the poor understanding of its pathophysiology, there is a need for reliable tools that will reduce diagnostic errors made by clinicians. The existence of public databases has facilitated the advent of advanced analytics in KOA research however the heterogeneity of the available data along with the observed high feature dimensionality make this diagnosis task difficult.

Identification of most important features based on a fuzzy ensemble technique: Evaluation on joint space narrowing progression in knee osteoarthritis patients.

Objective: Feature selection (FS) is a crucial and at the same time challenging processing step that aims to reduce the dimensionality of complex classification or regression problems. Various techniques have been proposed in the literature to address this challenge with emphasis to medical applications. However, each one of the existing FS algorithms come with its own advantages and disadvantages introducing a certain level of bias.

Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation.

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking.

Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach.

Knee osteoarthritis (KOA) is a multifactorial disease which is responsible for more than 80% of the osteoarthritis disease's total burden. KOA is heterogeneous in terms of rates of progression with several different phenotypes and a large number of risk factors, which often interact with each other. A number of modifiable and non-modifiable systemic and mechanical parameters along with comorbidities as well as pain-related factors contribute to the development of KOA.

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients.

Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined.

A Review on Finite Element Modeling and Simulation of the Anterior Cruciate Ligament Reconstruction.

The anterior cruciate ligament (ACL) constitutes one of the most important stabilizing tissues of the knee joint whose rapture is very prevalent. ACL reconstruction (ACLR) from a graft is a surgery which yields the best outcome. Taking into account the complicated nature of this operation and the high cost of experiments, finite element (FE) simulations can become a valuable tool for evaluating the surgery in a pre-clinical setting.

Biomechanics of sit-to-stand transition after muscle damage.

The purpose of the study was to examine the effects of exercise-induced muscle damage on the biomechanics of the sit-to-stand transition (STST). Seventeen volunteers participated in an intense, eccentric based, muscle damage protocol of knee flexors and extensors via an isokinetic dynamometer. Kinematic and kinetic data were collected using a 10-camera optoelectronic system and a force plate 24h before and 48h after exercise.

Mechanical correction of dynamometer moment for the effects of segment motion during isometric knee-extension tests.

The purpose of this study was to determine the effect of dynamometer and joint axis misalignment on measured isometric knee-extension moments using inverse dynamics based on the actual joint kinematic information derived from the real-time X-ray video and to compare the errors when the moments were calculated using measurements from external anatomical surface markers or obtained from the isokinetic dynamometer. Six healthy males participated in this study. They performed isometric contractions at 90° and 20° of knee flexion, gradually increasing to maximum effort.

The effects of muscle damage on walking biomechanics are speed-dependent.

The purpose of the present study was to examine the effects of muscle damage on walking biomechanics at different speeds. Seventeen young women completed a muscle damage protocol of 5 × 15 maximal eccentric actions of the knee extensors and flexors of both legs at 60°/s. Lower body kinematics and swing-phase kinetics were assessed on a horizontal treadmill pre- and 48 h post-muscle damaging exercise at four walking speeds.

A comparison of different two-dimensional approaches for the determination of the patellar tendon moment arm length.

The purpose of this study was to estimate and compare the moment arm length of the patellar tendon (d) during passive knee extension using three different reference landmarks; instant centre of rotation (ICR), tibiofemoral contact point (TFCP) and geometrical centre of the posterior femoral condyles (GCFC). Measurements were taken on the right leg on seven healthy males during passive knee rotation performed by the motor of a Cybex Norm isokinetic dynamometer. Moment arms lengths were obtained by analysing lateral X-ray images recorded using a GE FlexiView 8800 C-arm videofluoroscopy system.

In vivo changes in the human patellar tendon moment arm length with different modes and intensities of muscle contraction.

The purpose of this study was to examine the effect of different muscle contraction modes and intensities on patellar tendon moment arm length (d(PT)). Five men performed isokinetic concentric, eccentric and passive knee extensions at an angular velocity of 60 deg/s and six men performed gradually increasing to maximum effort isometric muscle contractions at 90( composite function) and 20( composite function) of knee flexion. During the tests, lateral X-ray fluoroscopy imaging was used to scan the knee joint.

Human patellar tendon moment arm length: measurement considerations and clinical implications for joint loading assessment.

Detailed understanding of the knee joint loading requires the calculation of muscle and joint forces in different conditions. In these applications the patellar tendon moment arm length is essential for the accurate estimation of the tibiofemoral joint loading. In this article, different methods that have been used to determine the patellar tendon moment arm length under in vivo and in vitro conditions are reviewed.

Can the patellar tendon moment arm be predicted from anthropometric measurements?

The purpose of this study was to examine the relations between patellar tendon moment arm length and several relevant anthropometric characteristics of 22 healthy men. The patellar tendon moment arm length was measured using magnetic resonance imaging with two different methods: (1) measurement of patellar tendon moment arm length (d(PT)) with respect to the tibiofemoral contact point (d(PTCP)) and (2) measurement of d(PT) with respect to the intersection point of the anterior and posterior cruciate ligament (d(PTIP)). Pearson correlation coefficients and a stepwise linear regression analysis were used to examine the relationships between the d(PT) and anthropometric measurements taken.

Muscle fibre length-to-moment arm ratios in the human lower limb determined in vivo.

The purpose of this study was to quantify in vivo the fibre length (L)-to-moment arm (d) ratio (L/d) in the major ankle plantarflexors and knee extensors of 21 healthy men. Measurements of L were taken in the gastrocnemius medialis, gastrocnemius lateralis, soleus, vastus lateralis and vastus intermedius muscles using ultrasound scanning. Measurements of d were taken in the Achilles tendon (d(AT)) and patellar tendon (d(PT)) using magnetic resonance imaging.