Exploring the intersection of obesity and gender in COVID-19 outcomes in hospitalized Mexican patients: a comparative analysis of risk profiles using unsupervised machine learning.

Introduction: Obesity and gender play a critical role in shaping the outcomes of COVID-19 disease. These two factors have a dynamic relationship with each other, as well as other risk factors, which hinders interpretation of how they influence severity and disease progression. This work aimed to study differences in COVID-19 disease outcomes through analysis of risk profiles stratified by gender and obesity status.

Breast cancer risk prediction using machine learning: a systematic review.

Background: Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models.

Clinical performance of a multiparametric MRI-based post concussive syndrome index.

Introduction: Diffusion Tensor Imaging (DTI) has revealed measurable changes in the brains of patients with persistent post-concussive syndrome (PCS). Because of inconsistent results in univariate DTI metrics among patients with mild traumatic brain injury (mTBI), there is currently no single objective and reliable MRI index for clinical decision-making in patients with PCS.

Purpose: This study aimed to evaluate the performance of a newly developed PCS Index (PCSI) derived from machine learning of multiparametric magnetic resonance imaging (MRI) data to classify and differentiate subjects with mTBI and PCS history from those without a history of mTBI.

Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review.

Background: Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes.

Hybrid morphological-convolutional neural networks for computer-aided diagnosis.

Training deep Convolutional Neural Networks (CNNs) presents challenges in terms of memory requirements and computational resources, often resulting in issues such as model overfitting and lack of generalization. These challenges can only be mitigated by using an excessive number of training images. However, medical image datasets commonly suffer from data scarcity due to the complexities involved in their acquisition, preparation, and curation.

Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis.

Purpose: Multiomics cancer subtyping is becoming increasingly popular for directing state-of-the-art therapeutics. However, these methods have never been systematically assessed for their ability to capture cancer prognosis for identified subtypes, which is essential to effectively treat patients.

Methods: We systematically searched PubMed, The Cancer Genome Atlas, and Pan-Cancer Atlas for multiomics cancer subtyping studies from 2010 through 2019.

Benchmarking machine learning models for late-onset alzheimer's disease prediction from genomic data.

Background: Late-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.

Baseline knee adduction moment interacts with body mass index to predict loss of medial tibial cartilage volume over 2.5 years in knee Osteoarthritis.

This study aimed to determine the extent to which changes over 2.5 years in medial knee cartilage thickness and volume were predicted by: (1) Peak values of the knee adduction (KAM) and flexion moments; and (2) KAM impulse and loading frequency, representing cumulative load, after controlling for age, sex and body mass index (BMI). Adults with clinical knee osteoarthritis participated.

Scan-rescan precision of subchondral bone curvature maps from routine 3D DESS water excitation sequences: Data from the Osteoarthritis Initiative.

Background: Subchondral bone (SCB) undergoes changes in the shape of the articulating bone surfaces and is currently recognized as a key target in osteoarthritis (OA) treatment. The aim of this study was to present an automated system that determines the curvature of the SCB regions of the knee and to evaluate its cross-sectional and longitudinal scan-rescan precision

Methods: Six subjects with OA and six control subjects were selected from the Osteoarthritis Initiative (OAI) pilot study database. As per OAI protocol, these subjects underwent 3T MRI at baseline and every twelve months thereafter, including a 3D DESS WE sequence.

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI.

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects.

Do Knee Moments Normalized to Measures of Knee Cartilage Area Better Classify the Severity of Knee Osteoarthritis?

Investigations of joint loading in knee osteoarthritis (OA) typically normalize the knee adduction moment to global measures of body size (eg, body mass, height) to allow comparison between individuals. However, such measurements may not reflect knee size. This study used a morphometric measurement of the cartilage surface area on the medial tibial plateau, which better represents medial knee size.

Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment.

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between AD, MCI, and healthy controls, which may impact future AD biomarkers.

GridMass: a fast two-dimensional feature detection method for LC/MS.

One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time.

On the use of coupled shape priors for segmentation of magnetic resonance images of the knee.

Active contour techniques have been widely employed for medical image segmentation. Significant effort has been focused on the use of training data to build prior statistical models applicable specifically to problems where the objects of interest are embedded in cluttered background. Usually, the training data consist of whole shapes of certain organs or structures obtained manually by clinical experts.

SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis.

Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians.

Minimum joint space width (mJSW) of patellofemoral joint on standing "skyline" radiographs: test-retest reproducibility and comparison with quantitative magnetic resonance imaging (qMRI).

Objective: To assess the intraobserver, interobserver, and test-retest reproducibility of minimum joint space width (mJSW) measurement of medial and lateral patellofemoral joints on standing "skyline" radiographs and to compare the mJSW of the patellofemoral joint to the mean cartilage thickness calculated by quantitative magnetic resonance imaging (qMRI).

Materials And Methods: A couple of standing "skyline" radiographs of the patellofemoral joints and MRI of 55 knees of 28 volunteers (18 females, ten males, mean age, 48.5 ± 16.

Unsupervised segmentation and quantification of anatomical knee features: data from the Osteoarthritis Initiative.

This paper presents a fully automated method for segmenting articular knee cartilage and bone from in vivo 3-D dual echo steady state images. The magnetic resonance imaging (MRI) datasets were obtained from the Osteoarthritis Initiative (OAI) pilot study and include longitudinal images from controls and subjects with knee osteoarthritis (OA) scanned twice at each visit (baseline, 24 month). Initially, human experts segmented six MRI series.

Computed tomography to assess pelvic lysis after total hip replacement.

To assess the accuracy of a computer-assisted computed tomography image analysis program in determining the location and volume of periacetabular osteolysis, we designed an osteolysis model by implanting bilateral total hip replacements in human pelvic cadavers and creating osteolytic lesions of varying sizes. The volumes of 48 defects were measured physically, and axial computed tomography scans were obtained. The computed tomography images were processed with streak artifact reduction and segmentation algorithms.

Local force model for cardiac dynamics analysis from volumetric image sequences.

We propose a local force model based on the kinetics of the left ventricle (LV) surfaces to characterize the complex nonrigid motion that the heart undergoes. The left ventricle motion is analyzed in a coarse-to-fine fashion. First, global motion and deformation are analyzed and compensated with hierarchical surface modeling.

The use of sequential MR image sets for determining tibiofemoral motion: reliability of coordinate systems and accuracy of motion tracking algorithm.

The use of magnetic resonance imaging has been proposed by many investigators for establishment of joint reference systems and kinematic tracking of musculoskeletal joints. In this study, the intraobserver and interobserver reliability of a strategy to establish anatomic reference systems using manually selected fiducial points were quantified for seven sets of MR images of the human knee joint. The standard error of the measurement of the intraobserver and interobserver errors were less than 2.