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Background/Objectives: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they are often challenging and complex to apply due to the absence of a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged as a promising approach to effectively monitor and mitigate biomechanical risks. This study aimed to evaluate the potential of machine learning models, trained on postural sway metrics derived from an inertial measurement unit (IMU) placed at the lumbar region, to classify risk levels associated with load lifting based on the Revised NIOSH Lifting Equation. Methods: To compute postural sway parameters, the IMU captured acceleration data in both anteroposterior and mediolateral directions, aligning closely with the body's center of mass. Eight participants undertook two scenarios, each involving twenty consecutive lifting tasks. Eight machine learning classifiers were tested utilizing two validation strategies, with the Gradient Boost Tree algorithm achieving the highest accuracy and an Area under the ROC Curve of 91.2% and 94.5%, respectively. Additionally, feature importance analysis was conducted to identify the most influential sway parameters and directions. Results: The results indicate that the combination of sway metrics and the Gradient Boost model offers a feasible approach for predicting biomechanical risks in load lifting. Conclusions: Further studies with a broader participant pool and varied lifting conditions could enhance the applicability of this method in occupational ergonomics. [ABSTRACT FROM AUTHOR]

Background/Objectives: Gait analysis, traditionally performed with lab-based optical motion capture systems, offers high accuracy but is costly and impractical for real-world use. Wearable technologies, especially inertial measurement units (IMUs), enable portable and accessible assessments outside the lab, though challenges with sensor placement, signal selection, and algorithm design can affect accuracy. This systematic review aims to bridge the benchmarking gap between IMU-based and traditional systems, validating the use of wearable inertial systems for gait analysis. Methods: This review examined English studies between 2012 and 2023, retrieved from the Scopus database, comparing wearable sensors to optical motion capture systems, focusing on IMU body placement, gait parameters, and validation metrics. Exclusion criteria for the search included conference papers, reviews, unavailable papers, studies without wearable inertial sensors for gait analysis, and those not involving agreement studies or optical motion capture systems. Results: From an initial pool of 479 articles, 32 were selected for full-text screening. Among them, the lower body resulted in the most common site for single IMU placement (in 22 studies), while the most frequently used multi-sensor configuration involved IMU positioning on the lower back, shanks, feet, and thighs (10 studies). Regarding gait parameters, 11 studies out of the 32 included studies focused on spatial-temporal parameters, 12 on joint kinematics, 2 on gait events, and the remainder on a combination of parameters. In terms of validation metrics, 24 studies employed correlation coefficients as the primary measure, while 7 studies used a combination of error metrics, correlation coefficients, and Bland–Altman analysis. Validation metrics revealed that IMUs exhibited good to moderate agreement with optical motion capture systems for kinematic measures. In contrast, spatiotemporal parameters demonstrated greater variability, with agreement ranging from moderate to poor. Conclusions: This review highlighted the transformative potential of wearable IMUs in advancing gait analysis beyond the constraints of traditional laboratory-based systems. [ABSTRACT FROM AUTHOR]

Background and purpose: Susceptibility estimates derived from quantitative susceptibility mapping (QSM) images for the cerebral cortex and major subcortical structures are variably reported in brain magnetic resonance imaging (MRI) studies, as average of all (μall${{{{\mu}}}_{{\mathrm{all}}}}$), absolute (μabs${{{{\mu}}}_{{\mathrm{abs}}}}$), or positive‐ (μp${{{{\mu}}}_{\mathrm{p}}}$) and negative‐only (μn${{{{\mu}}}_{\mathrm{n}}}$) susceptibility values using a region of interest (ROI) approach. This pilot study presents a reliability analysis of currently used ROI‐QSM metrics and an alternative ROI‐based approach to obtain voxel‐weighted ROI‐QSM metrics (μwp${{{{\mu}}}_{{\mathrm{wp}}}}$ and μwn${{{{\mu}}}_{{\mathrm{wn}}}}$). Methods: Ten healthy subjects underwent repeated (test‐retest) 3‐dimensional multi‐echo gradient‐echo (3DMEGE) 3 Tesla MRI measurements. Complex‐valued 3DMEGE images were acquired and reconstructed with slice thicknesses of 1 and 2 mm (3DMEGE1, 3DMEGE2) along with 3DT1‐weighted isometric (voxel 1 mm3) images for independent registration and ROI segmentation. Agreement, consistency, and reproducibility of ROI‐QSM metrics were assessed through Bland‐Altman analysis, intraclass correlation coefficient, and interscan and intersubject coefficient of variation (CoV). Results: All ROI‐QSM metrics exhibited good to excellent consistency and test‐retest agreement with no proportional bias. Interscan CoV was higher for μall${{{{\mu}}}_{{\mathrm{all}}}}$ in comparison to the other metrics where it was below 15%, in both 3DMEGE1 and 3DMEGE2 datasets. Intersubject CoV for μall${{{{\mu}}}_{{\mathrm{all}}}}$ and μabs${{{{\mu}}}_{{\mathrm{abs}}}}$ exceeded 50% in all ROIs. Conclusions: Among the evaluated ROI‐QSM metrics, μall${{{{\mu}}}_{{\mathrm{all}}}}$ and μabs${{{{\mu}}}_{{\mathrm{abs}}}}$ estimates were less reliable, whereas separating positive and negative values (using μp,μn,μwp,μwn${{{{\mu}}}_{\mathrm{p}}},\ {{{{\mu}}}_{\mathrm{n}}},\ {{{{\mu}}}_{{\mathrm{wp}}}},\ {{{{\mu}}}_{{\mathrm{wn}}}}$) improved the reproducibility within, and the comparability between, subjects, even when reducing the slice thickness. These preliminary findings may offer valuable insights toward standardizing ROI‐QSM metrics across different patient cohorts and imaging settings in future clinical MRI studies. [ABSTRACT FROM AUTHOR]

Background: Brain parenchyma (BP) and intracranial cerebrospinal fluid (iCSF) volumes measured by fully automated segmentation of clinical brain MRI studies may be useful for the diagnosis and follow‐up of pediatric hydrocephalus. However, previously published segmentation techniques either rely on dedicated sequences, not routinely used in clinical practice, or on spatial normalization, which has limited accuracy when severe brain distortions, such as in hydrocephalic patients, are present. Purpose: We developed a fully automated method to measure BP and iCSF volumes from clinical brain MRI studies of pediatric hydrocephalus patients, exploiting the complementary information contained in T2‐ and T1‐weighted images commonly used in clinical practice. Methods: The proposed procedure, following skull‐stripping of the combined volumes, performed using a multiparametric method to obtain a reliable definition of the inner skull profile, maximizes the CSF‐to‐parenchyma contrast by dividing the T2w‐ by the T1w‐ volume after full‐scale dynamic rescaling, thus allowing separation of iCSF and BP through a simple thresholding routine. Results: Validation against manual tracing on 23 studies (four controls and 19 hydrocephalic patients) showed excellent concordance (ICC > 0.98) and spatial overlap (Dice coefficients ranging from 77.2% for iCSF to 96.8% for intracranial volume). Accuracy was comparable to the intra‐operator reproducibility of manual segmentation, as measured in 14 studies processed twice by the same experienced neuroradiologist. Results of the application of the algorithm to a dataset of 63 controls and 57 hydrocephalic patients (19 with parenchymal damage), measuring volumes' changes with normal development and in hydrocephalic patients, are also reported for demonstration purposes. Conclusions: The proposed approach allows fully automated segmentation of BP and iCSF in clinical studies, also in severely distorted brains, enabling to assess age‐ and disease‐related changes in intracranial tissue volume with an accuracy comparable to expert manual segmentation. [ABSTRACT FROM AUTHOR]

Background and purpose Methods Results Conclusions ‘Neurovascular coupling’ (NVC) alterations, assessing the interplay between local cerebral perfusion and neural activity within a given brain region or network, may reflect neurovascular unit impairment in amyotrophic lateral sclerosis (ALS). The aim was to explore NVC as a correlation between the functional connectivity and cerebral blood flow within the large‐scale resting‐state functional magnetic resonance imaging brain networks in a sample of ALS patients compared to healthy controls (HCs).Forty‐eight ALS patients (30 males; mean age 60.64 ± 9.62 years) and 32 HC subjects (14 males; mean age 55.06 ± 16 years) were enrolled and underwent 3 T magnetic resonance imaging. ALS patients were screened by clinical and neuropsychological scales and were retrospectively classified as very fast progressors (VFPs), fast progressors and slow progressors (SPs).Neurovascular coupling reduction within the default mode network (DMN) (p = 0.005) was revealed in ALS patients compared to HCs, observing, for this network, significant NVC differences between VFP and SP groups. Receiver operating characteristic curve analysis showed that impaired NVC in the DMN at baseline best discriminated VFPs and SPs (area under the curve 75%). Significant correlations were found between NVC and the executive (r = 0.40, p = 0.01), memory (r = 0.32, p = 0.04), visuospatial ability (r = 0.40, p = 0.01) and non‐ALS‐specific (r = 0.40, p = 0.01) subscores of the Edinburgh Cognitive and Behavioural ALS Screen.The reduction of brain NVC in the DMN may reflect largely distributed abnormalities of the neurovascular unit. NVC alterations in the DMN could play a role in anticipating a faster clinical progression in ALS patients, aiding patient selection and monitoring during clinical trials. [ABSTRACT FROM AUTHOR]