Terms Of Intraclass Correlation Coefficient Research Articles

Manual versus deep learning measurements to evaluate cumulus expansion of bovine oocytes and its relationship with embryo development in vitro

Cumulus expansion is an important indicator of oocyte maturation and has been suggested to be indicative of greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a gold standard. Additionally, these methods are subjective and time-consuming. In this manuscript, the reliability of three cumulus expansion measurement methods was assessed, and a deep learning model was created to automatically perform the measurement. Cumulus expansion of 232 cumulus-oocyte complexes was evaluated by three independent observers using three methods: (1) measurement of the cumulus area, (2) measurement of three distances between the zona pellucida and outer cumulus, and (3) scoring cumulus expansion on a 5-point Likert scale. The reliability of the methods was calculated in terms of intraclass-correlation coefficients (ICC) for both inter- and intra-observer agreements. The area method resulted in the best overall inter-observer agreement with an ICC of 0.89 versus 0.54 and 0.30 for the 3-distance and scoring methods, respectively. Therefore, the area method served as the base to create a deep learning model, AI-xpansion, which reaches a human-level performance in terms of average rank, bias and variance. To evaluate the accuracy of the methods, the results of cumulus expansion calculations were linked to embryonic development. Cumulus expansion had increased significantly in oocytes that achieved successful embryo development when measured by AI-xpansion, the area- or 3-distance method, while this was not the case for the scoring method. Measuring the area is the most reliable method to manually evaluate cumulus expansion, whilst deep learning automatically performs the calculation with human-level precision and high accuracy and could therefore be a valuable prospective tool for embryologists.

The effect of short-course point-of-care echocardiography training on the performance of medical interns in children.

Point-of-care ultrasound (POCUS) can add complementary information to physical examination. Despite its development in several medical specialties, there is a lack of similar studies on children by medical interns and cardiologists. Therefore, investigators aimed to assess the effect of short-course training on the performance of medical interns in point-of-care echocardiography in children. This analytic cross-sectional study was conducted on 161 hospitalized children in 17 Shahrivar children's hospital, Iran, from January 2021 to May 2021. Seven interns (trainees) participated in a short course of point-of-care echocardiography to assess left ventricular ejection fraction (LVEF), inferior vena cava collapsibility index (IVCCI), and the presence of pericardial effusion (PEff). Each patient underwent point-of-care echocardiography by one of the trainees. Then, in less than one hour, the echocardiography was performed by a single cardiologist. Agreement between the cardiologist and trainees was examined using Cohen's kappa coefficient and Prevalence-Adjusted Bias-Adjusted Kappa (PABAK). For numerical variables, the agreement was examined using the concordance correlation coefficient (CCC) and intraclass correlation coefficient (ICC). Results showed that the cardiologist and trainees detected LVEF >50, IVCCI >50%, and the absence of PEff in most of the participants. A good agreement in terms of ICC and CCC for LVEF (0.832 and 0.831, respectively) and a good agreement in terms of ICC and CCC for IVCCI (0.878 and 0.877, respectively) were noted. Using categorical scoring of LVEF and IVCCI showed 94.4% and 87.6% complete agreement, respectively. Furthermore, using categorical scoring of LVEF and IVCCI, Cohen's kappa coefficient was 0.542 (moderate) and 0.619 (substantial), respectively. The PABAK for LVEF and IVCCI were 0.886 (almost perfect) and 0.752 (substantial), respectively. For PEff, Cohen's kappa and PABAK were 0.797 (moderate) and 0.988 (almost perfect), respectively, and the complete agreement was noted in 160 patients (99.4%). This study showed that a short teaching course could help medical interns to assess LVEF, IVCCI, and PEff in children. Therefore, it seems that adding this course to medical interns' curricula can be promising.

Compartmental model describing the physiological basis for the HepQuant SHUNT test

The HepQuant SHUNT test quantifies hepatic functional impairment from the simultaneous clearance of cholate from the systemic and portal circulations for the purpose of monitoring treatment effects or for predicting risk for clinical outcome. Compartmental models are defined by distribution volumes and transfer rates between volumes to estimate parameters not defined by noncompartmental analyses. Previously, a noncompartmental analysis method, called the minimal model (MM), demonstrated reproducible and reliable measures of liver function (Translational Research 2021). The aim of this study was to compare the reproducibility and reliability of a new physiologically based compartmental model (CM) vs the MM. Data were analyzed from 16 control, 16 nonalcoholic steatohepatitis (NASH), and 16 hepatitis C virus (HCV) subjects, each with 3 replicate tests conducted on 3 separate days. The CM describes transfer of cholates between systemic, portal, and liver compartments with assumptions from measured or literature-derived values and unknown parameters estimated by nonlinear least-squares regression. The CM was compared to the MM for 6 key indices of hepatic disease in terms of intraclass correlation coefficient (ICC) with a lower acceptable limit of 0.7. The CM correlated well with the MM for disease severity index (DSI) with R2 (95% confidence interval) of 0.96 (0.94–0.98, P < 0.001). Acceptable reproducibility (ICC > 0.7) was observed for 6/6 and 5/6 hepatic disease indices for CM and MM, respectively. SHUNT, a measure of the absolute bioavailability, had ICC of 0.73 (0.60–0.83, P = 0.3095) for MM and 0.84 (0.76–0.90, P = 0.0012) for CM. The CM, but not the MM, allowed determination of anatomic shunt and hepatic extraction and improved the within individual reproducibility.

Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy

Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width). While we found a remarkable dependence of myocardial radiomic features from T1 and T2 mapping on image filters, many radiomic features showed a limited sensitivity to resampling voxel size/bin width, in terms of intraclass correlation coefficient (> 0.75) and coefficient of variation (< 30%). The estimate of most textural radiomic features showed a linear significant (p < 0.05) correlation with resampling voxel size/bin width. Overall, radiomic features from T2 maps have proven to be less sensitive to image preprocessing than those from T1 maps, especially when varying bin width. Our results might corroborate the potential of radiomics from T1/T2 mapping in HCM and hopefully in other myocardial diseases.

Can AI Automatically Assess Scan Quality of Hip Ultrasound?

Ultrasound images can reliably detect Developmental Dysplasia of the Hip (DDH) during early infancy. Accuracy of diagnosis depends on the scan quality, which is subjectively assessed by the sonographer during ultrasound examination. Such assessment is prone to errors and often results in poor-quality scans not being reported, risking misdiagnosis. In this paper, we propose an Artificial Intelligence (AI) technique for automatically determining scan quality. We trained a Convolutional Neural Network (CNN) to categorize 3D Ultrasound (3DUS) hip scans as ‘adequate’ or ‘inadequate’ for diagnosis. We evaluated the performance of this AI technique on two datasets—Dataset 1 (DS1) consisting of 2187 3DUS images in which each image was assessed by one reader for scan quality on a scale of 1 (lowest quality) to 5 (optimal quality) and Dataset 2 (DS2) consisting of 107 3DUS images evaluated semi-quantitatively by four readers using a 10-point scoring system. As a binary classifier (adequate/inadequate), the AI technique gave highly accurate predictions on both datasets (DS1 accuracy = 96% and DS2 accuracy = 91%) and showed high agreement with expert readings in terms of Intraclass Correlation Coefficient (ICC) and Cohen’s kappa coefficient (K). Using our AI-based approach as a screening tool during ultrasound scanning or postprocessing would ensure high scan quality and lead to more reliable ultrasound hip examination in infants.

Comparison of test\u2013retest reliability of BOLD and pCASL fMRI in a two-center study

BackgroundThe establishment of test–retest reliability and reproducibility (TRR) is an important part of validating any research tool, including functional magnetic resonance imaging (fMRI). The primary objective of this study is to investigate the reliability of pseudo-Continuous Arterial Spin Labeling (pCASL) and Blood Oxygen Level Dependent (BOLD) fMRI data acquired across two different scanners in a sample of healthy adults. While single site/single scanner studies have shown acceptable repeatability, TRR of both in a practical multisite study occurring in two facilities spread out across the country with weeks to months between scans is critically needed.MethodsTen subjects were imaged with similar 3 T MRI scanners at the University of Pittsburgh and Massachusetts General Hospital. Finger-tapping and Resting-state data were acquired for both techniques. Analysis of the resting state data for functional connectivity was performed with the Functional Connectivity Toolbox, while analysis of the finger tapping data was accomplished with FSL. pCASL Blood flow data was generated using AST Toolbox. Activated areas and networks were identified via pre-defined atlases and dual-regression techniques. Analysis for TRR was conducted by comparing pCASL and BOLD images in terms of Intraclass correlation coefficients, Dice Similarity Coefficients, and repeated measures ANOVA.ResultsBoth BOLD and pCASL scans showed strong activation and correlation between the two locations for the finger tapping tasks. Functional connectivity analyses identified elements of the default mode network in all resting scans at both locations. Multivariate repeated measures ANOVA showed significant variability between subjects, but no significant variability for location. Global CBF was very similar between the two scanning locations, and repeated measures ANOVA showed no significant differences between the two scanning locations.ConclusionsThe results of this study show that when similar scanner hardware and software is coupled with identical data analysis protocols, consistent and reproducible functional brain images can be acquired across sites. The variability seen in the activation maps is greater for pCASL versus BOLD images, as expected, however groups maps are remarkably similar despite the low number of subjects. This demonstrates that multi-site fMRI studies of task-based and resting state brain activity is feasible.

Test–retest reliability of the Cost for Patients Questionnaire

To investigate the test-retest reliability of the Costs for Patients Questionnaire (CoPaQ). Through an online survey, individuals were invited to participate in a two-step study to assess the test-retest reliability of the CoPaQ. Participants to the first step were invited to complete the questionnaire a second time 2weeks after. Reliability was assessed by calculating Cohen's Kappa coefficients and intraclass correlation coefficients (ICC) for discrete and continuous data, respectively. A sensitivity analysis was carried out. From a total of 1,200 participants who completed the first test, 403 completed the second test. The ICC varied from -0.00 to 0.98 with poor, moderate, good, and excellent results. The Kappa coefficients varied from -0.004 to 0.65 and were poor, slight, fair, moderate, and substantial. The sensitivity analysis showed the median value of ICC and Kappa coefficients for each category before and after the outliers' exclusion. The median value of ICC changed from 0.30 (before) to 0.70 (after), and from 0.12 (before) to 0.04 (after), respectively, for each category. The median value of the Cohen's Kappa coefficient increased from 0.44 (before) to 0.46 (after) and decreased from 0.32 (before) to 0.30 (after), respectively. Test-retest reliability results indicated that the CoPaQ has a moderate reliability in terms of ICC and Kappa coefficients. The moderate reliability observed gives additional support for the applicability of this tool in economic evaluations of health interventions. Additional studies including on other properties and a cultural adaptation could further enhance the use of the tool.

Fully automated deep learning-based localization and segmentation of the locus coeruleus in aging and Parkinson\u2019s disease using neuromelanin-sensitive MRI

PurposeDevelopment and performance measurement of a fully automated pipeline that localizes and segments the locus coeruleus in so-called neuromelanin-sensitive magnetic resonance imaging data for the derivation of quantitative biomarkers of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease.MethodsWe propose a pipeline composed of several 3D-Unet-based convolutional neural networks for iterative multi-scale localization and multi-rater segmentation and non-deep learning-based components for automated biomarker extraction. We trained on the healthy aging cohort and did not carry out any adaption or fine-tuning prior to the application to Parkinson’s disease subjects.ResultsThe localization and segmentation pipeline demonstrated sufficient performance as measured by Euclidean distance (on average around 1.3mm on healthy aging subjects and 2.2mm in Parkinson’s disease subjects) and Dice similarity coefficient (overall around 71% on healthy aging subjects and 60% for subjects with Parkinson’s disease) as well as promising agreement with respect to contrast ratios in terms of intraclass correlation coefficient of ge 0.80 for healthy aging subjects compared to a manual segmentation procedure. Lower values (ge 0.48) for Parkinson’s disease subjects indicate the need for further investigation and tests before the application to clinical samples.ConclusionThese promising results suggest the usability of the proposed algorithm for data of healthy aging subjects and pave the way for further investigations using this approach on different clinical datasets to validate its practical usability more conclusively.

Artificial Intelligence to Automatically Assess Scan Quality in Hip Ultrasound.

Since it is fast, inexpensive and increasingly portable, ultrasound can be used for early detection of Developmental Dysplasia of the Hip (DDH) in infants at point-of-care. However, accurate interpretation\is highly dependent on scan quality. Poor-quality images lead to misdiagnosis, but inexperienced users may not even recognize the deficiencies in the images. Currently, users assess scan quality subjectively, based on image landmarks which are prone to human errors. Instead, we propose using Artificial Intelligence (AI) to automatically assess scan quality. We trained separate Convolutional Neural Network (CNN) models to detect presence of each of four commonly used ultrasound landmarks in each hip image: straight horizontal iliac wing, labrum, os ischium and midportion of the femoral head. We used 100 3D ultrasound (3DUS) images for training and validated the technique on a set of 107 3DUS images also scored for landmarks by three non-expert readers and one expert radiologist. We got AI ≥ 85% accuracy for all four landmarks (ilium = 0.89, labrum = 0.94, os ischium = 0.85, femoral head = 0.98) as a binary classifier between adequate and inadequate scan quality. Our technique also showed excellent agreement with manual assessment in terms of Intraclass Correlation Coefficient (ICC) and Cohen's kappa coefficient (K) for ilium (ICC = 0.81, K = 0.56), os ischium (ICC = 0.89, K = 0.63) and femoral head (ICC = 0.83, K = 0.66), and moderate to good agreement for labrum (ICC = 0.65, K = 0.33). This new technique could ensure high scan quality and facilitate more widespread use of ultrasound in population screening of DDH.

Is ultrasound accurate for radiation-free quantitative assessment of spinal curvatures in patients with idiopathic scoliosis - a systematic review and meta-analysis.

Despite application of ultrasound for quantitative measurement of spinal curvatures has been reported with various studies, a systematic review for such is lacking. This systematic review aimed to evaluate (1) reliability of ultrasound; (2) validity of ultrasound using radiographic measurement as gold standard in idiopathic scoliosis patients; and (3) the use of various anatomical landmarks for measurement of spinal curvatures. MEDLINE, EMBASE, CINAHL, and CENTRAL databases were searched. QUADAS-2 quality assessment tool was adopted. Reliability of ultrasound in terms of intra-class correlation coefficient was recorded. Pearson correlation coefficients between ultrasound and radiographic measurements were extracted for meta-analysis. Subgroup analyses based on ultrasound measurement protocols of spinous process (SP), transverse processes (TP) and center of lamina (COL) were conducted. Eleven articles reporting 18 correlation analyses on 766 subjects were eligible for meta-analysis. The mean inter-rater reliability of ultrasound measurement was 0.87±0.07. Pooled correlation for all studies was 0.918 (95% CI: 0.868-0.949), exhibiting substantial heterogeneity (I2=90.50%, p<0.001). Subgroup analyses showed that pooled correlations were 0.887 for COL method (comprising 356 subjects); 0.924 for SP method (255 subjects); and 0.941 for TP method (117 subjects); all with notable heterogeneity (I2>90%, p<0.001). The overall risk of bias was rated moderate; yet publication bias was noted. Evidences showed that ultrasound was a promising non-invasive method with satisfactory validity and reliability for measuring coronal curvatures utilizing the SP, TP or COL methods. Further development of three-dimensional ultrasound towards scoliosis assessment will facilitate its translational application for managing scoliosis.

Reliability on multiband diffusion NODDI models: A test retest study on children and adults

Neurite Orientation Dispersion and Density Imaging (NODDI) and Bingham-NODDI diffusion MRI models are nowadays very well-known models in the field of diffusion MRI as they represent powerful tools for the estimation of brain microstructure. In order to efficiently translate NODDI imaging findings into the diagnostic clinical practice, a test-retest approach would be useful to assess reproducibility and reliability of NODDI biomarkers, thus providing validation on precision of different fitting toolboxes. In this context, we conducted a test-retest study with the aim to assess the effects of different factors (i.e. fitting algorithms, multiband acceleration, shell configuration, age of subject and hemispheric side) on diffusion models reliability, assessed in terms of Intra-class Correlation Coefficient (ICC) and Variation Factor (VF). To this purpose, data from pediatric and adult subjects were acquired with Simultaneous-MultiSlice (SMS) imaging method with two different acceleration factor (AF) and four b-values, subsequently combined in seven shell configurations. Data were then fitted with two different GPU-based algorithms to speed up the analysis. Results show that each factor investigated had a significant effect on reliability of several diffusion parameters. Particularly, both datasets reveal very good ICC values for higher AF, suggesting that faster acquisitions do not jeopardize the reliability and are useful to decrease motion artifacts. Although very small reliability differences appear when comparing shell configurations, more extensive diffusion parameters variability results when considering shell configuration with lower b-values, especially for simple model like NODDI. Also fitting tools have a significant effect on reliability, but their difference occurs in both datasets and AF, so it appears to be independent from either misalignment and motion artifacts, or noise and SNR. The main achievement of the present study is to show how 10 min multi-shell diffusion MRI acquisition for NODDI acquisition can have reliable results in WM. More complex models do not appear to be more prone to less data acquisition as well as noisier data thus stressing the idea of Bingham-NODDI having greater sensitivity to true subject variability.

Test-Retest Reliability of Sole Morphology Measurements Using a Novel Single-Image-Based Pin-Array Impression Reconstruction Method

Background: Assessment of the sole morphology provides a clinically useful measure of the foot condition, but existing methods are mostly limited to two-dimensional or non-weight-bearing conditions. The current study aimed to assess the reliability of a novel method called Single-Image-Based Pin-Array Impression Reconstruction Method (SIBPAIR) implemented on a commercial foot assessment system, and the intra-rater, inter-rater and inter-session reliability of the SIBPAIR-based protocol for three-dimensional sole morphological measurements. Methods: The reliability of the SIBPAIR method, and the intra-rater, inter-rater, and inter-session reliability of the SIBPAIR-based protocol in measuring morphological parameters of the sole were assessed by repeated measurements of fifteen young healthy adults by two male physical therapists, in terms of intra-class correlation coefficients (ICC) and standard error of measurement (SEM). Results and Conclusions: The SIBPAIR method was found to have very high reliability with very small SEM values, and the SIBPAIR-based protocol also showed very high intra-rater, inter-rater, and inter-session reliability with small SEM. These results indicate that accurate and reliable measurements could be obtained by following the protocol regardless of the rater’s experience or time of measurement. This will be useful for foot assessment and subsequent applications, such as design and manufacture of customized orthoses or shoes.

Acetabular and Femoral Component Positioning Using Direct Anterior Approach Versus Posterior Approach in Total Hip Arthroplasty.

Direct anterior approach (DAA) has recently become popular in total hip arthroplasty (THA). However, irrespective of the surgical approach used, component malposition is an important factor affecting function and complications after THA. This study aims to compare component positioning on the femoral and acetabular side between DAA and posterior approach (PA) to the hip joint. We hypothesized that the two approaches are similar in terms of component positioning. We prospectively studied 50 patients, matched according to age, sex, and body mass index, undergoing THA, divided non-randomly into 2 groups. Group 1 comprised 25 patients (35 hips) undergoing THA using DAA and group 2 comprised 25 patients (25 hips) undergoing THA using PA. Ten patients from group 1 had simultaneous bilateral THA. Radiological parameters studied were acetabular inclination (AI), coronal femoral stem alignment (CFA), leg length difference (LLD), acetabular cup version (AV), and femoral stem version (FV). There was no significant difference in AI, CFA, LLD, AV, and FV between the two groups. Excellent to good inter and intra-observer reliability expressed in terms of intraclass correlation coefficient (ICC) was noted for all the radiographic measurements. Both DAA and PA for THA achieve comparable radiological component positioning. DAA may not provide any advantage over PA in terms of positioning of the prosthesis. Level II, non-randomized comparative study.

Stability of dosomics features extraction on grid resolution and algorithm for radiotherapy dose calculation.

Dosomics is a novel texture analysis method to parameterize regions of interest and to produce dose features that encode the spatial and statistical distribution of radiotherapy dose at higher resolution than organ-level dose-volume histograms. This study investigates the stability of dosomics features extraction, as their variation due to changes of grid resolution and algorithm dose calculation. Dataset has been generated considering all the possible combinations of four grid resolutions and two algorithms dose calculation of 18 clinical delivered dose distributions, leading to a 144 3D dose distributions dataset. Dosomics features extraction has been performed with an in-house developed software. A total number of 214 dosomics features has been extracted from four different region of interest: PTV, the two closest OARs and a RING structure. Reproducibility and stability of each extracted dosomic feature (Rfe, Sfe), have been analyzed in terms of intraclass correlation coefficient (ICC) and coefficient of variation. Dosomics features extraction was found reproducible (ICC>0.99). Dosomic features, across the combination of grid resolutions and algorithms dose calculation, are more stable in the RING for all the considered feature's families. Sfe is higher in OARs, in particular for GLSZM features' families. Highest Sfe have been found in the PTV, in particular in the GLCM features' family. Stability and reproducibility of dosomics features have been evaluated for a representative clinical dose distribution case mix. These results suggest that, in terms of stability, dosomic studies should always perform a reporting of grid resolution and algorithm dose calculation.

Validation of T2 -based oxygen extraction fraction measurement with 15 O positron emission tomography.

To evaluate the accuracy of T2 -based whole-brain oxygen extraction fraction (OEF) estimation by comparing it with gold standard 15 O-PET measurements. Sixteen healthy adult subjects underwent MRI and 15 O-PET OEF measurements on the same day. On MRI, whole-brain OEF was quantified by T2 -relaxation-under-spin-tagging (TRUST) MRI, based on subject-specific hematocrit. The TRUST OEF was compared to the whole-brain averaged OEF produced by 15 O-PET. Agreement between TRUST and 15 O-PET whole-brain OEF measurements was examined in terms of intraclass correlation coefficient (ICC) and in absolute OEF values. In a subset of 10 subjects, test-retest reproducibility of whole-brain OEF was also evaluated and compared between the two modalities. Across the 16 subjects, the mean whole-brain OEF of TRUST and 15 O-PET were 36.44±4.07% and 36.45±3.65%, respectively, showing no difference between the two modalities (P=.99). TRUST whole-brain OEF strongly correlated with that of 15 O-PET (N=16, ICC=0.90, P=4×10-7 ). The coefficient-of-variation of TRUST and 15 O-PET whole-brain OEF measurements were 1.79±0.67% and 2.06±1.55%, respectively, showing no difference between the two modalities (N=10, P=.64). Further analyses on the effect of hematocrit revealed that correlation between PET OEF and TRUST OEF with assumed hematocrit remained significant (ICC=0.8, P<2×10-5 ). Whole-brain OEF measured by TRUST was in excellent agreement with gold standard 15 O-PET, with highly comparable accuracy and reproducibility. These findings suggest that TRUST MRI can provide accurate quantification of whole-brain OEF noninvasively.

Inter-method reliability between automatic region of interest analytic application with multi-atlas segmentation and FreeSurfer.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the aggregation of amyloid-β and phosphorylated tau proteins. Magnetic resonance imaging (MRI) is a useful means of detecting hippocampal atrophy. However, instead of visual inspection, objective and time-saving tools for automated region of interest (ROI) analysis are needed. Advances in MRI segmentation techniques have enabled a multi-atlas approach with fewer errors than a conventional single-atlas approach. To support the clinical application of multi-atlas segmentation, an automated ROI analytic application consisting of multi-atlas segmentation with joint label fusion and corrective learning was developed: T-Proto. In the present study, we evaluated the inter-method reliability between T-Proto and a reference ROI analytic software, FreeSurfer. This was a database study. MRI data from 30 patients with AD were selected, and the inter-method reliability was assessed in terms of the intra-class correlation coefficient (ICC). A post-hoc comparison according to the severity of AD was also performed. Almost all the regional volumes estimated with T-Proto were smaller than those estimated with FreeSurfer. The regional ICC values between the two methods showed moderate to excellent reliability. A post-hoc comparison revealed a similar t-value and effect size between both methods for the hippocampus. In the present study, we showed that automated regional analysis using T-Proto was reliable in the hippocampus in terms of ICC, compared with FreeSurfer.

Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles. In this article, we propose a data-driven approach for estimating the driver's take-over readiness based purely on observable cues from in-vehicle vision sensors. We present an extensive naturalistic driving dataset of drivers in a conditionally autonomous vehicle running on Californian freeways. We collect subjective ratings for the driver's take-over readiness from multiple human observers viewing the sensor feed. Analysis of the ratings in terms of intra-class correlation coefficients (ICCs) shows a high degree of consistency in the ratings across raters. We define a metric for the driver's take-over readiness termed the `Observable Readiness Index (ORI)' based on the ratings. Finally, we propose an LSTM model for continuous estimation of the driver's ORI based on a holistic representation of the driver's state, capturing gaze, hand, pose and foot activity. Our model estimates the ORI with a mean absolute error of 0.449 on a 5 point scale.

The original Akagi line is the most reliable: a systematic review of landmarks for rotational alignment of the tibial component in TKA.

There is no present consensus on the most reliable anatomical landmarks or axes for tibial rotational alignment in total knee arthroplasty (TKA). The goal was therefore to review the literature and compare accuracy and repeatability of different axes for tibial baseplate rotation in TKA. Medline and Embase were searched for articles that reported accuracy in terms of error or discrepancy from the trans-epicondylar axes (TEA), and/or repeatability in terms of intraclass correlation coefficient, of one or more axes used for tibial baseplate rotation in TKA. Twenty-one articles met criteria, and their data were extracted and tabulated. The selected articles evaluated 15 different axes, 13 for reliability, 12 for repeatability. The lowest errors or discrepancies from the projected TEA were reported for the original 'Akagi line' (posterior cruciate ligament posteriorly to medial border of tibial tuberosity), its variant using the sulcus of the tibial spines as anterior landmark, as well as the anterior tibial border and the curve-on-curve technique. The best inter-observer repeatabilities were reported for 'Akagi line' variants that use the geometric centre of the tibial plateau posteriorly and the medial border of the tibial tuberosity, or the medial sixth of the patellar tendon anteriorly. Considering accuracy and repeatability simultaneously, only two axes were found to satisfy both criteria consistently: the original 'Akagi line' and the anterior tibial border. Because of the small number of studies found, the collected evidence remains insufficient to recommend reference axes for intra-operative rotational alignment of the tibial baseplate in TKA. A combination of two or more anatomical landmarks or projected axes could be used to ensure adequate tibial baseplate rotation, while considering individual patient morphology and implant design to optimize knee kinematics and prevent prosthetic overhang. Level IV, systematic review of level III and IV studies.

Job-exposure matrices addressing lifestyle to be applied in register-based occupational health studies

ObjectivesInformation about lifestyle factors in register-based occupational health studies is often not available. The objective of this study was therefore to develop gender, age and calendar-time specific job-exposure matrices (JEMs)...

A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images

BackgroundQuality assessment of medical images is highly related to the quality assurance, image interpretation and decision making. As to magnetic resonance (MR) images, signal-to-noise ratio (SNR) is routinely used as a quality indicator, while little knowledge is known of its consistency regarding different observers.MethodsIn total, 192, 88, 76 and 55 brain images are acquired using T2*, T1, T2 and contrast-enhanced T1 (T1C) weighted MR imaging sequences, respectively. To each imaging protocol, the consistency of SNR measurement is verified between and within two observers, and white matter (WM) and cerebral spinal fluid (CSF) are alternately used as the tissue region of interest (TOI) for SNR measurement. The procedure is repeated on another day within 30 days. At first, overlapped voxels in TOIs are quantified with Dice index. Then, test-retest reliability is assessed in terms of intra-class correlation coefficient (ICC). After that, four models (BIQI, BLIINDS-II, BRISQUE and NIQE) primarily used for the quality assessment of natural images are borrowed to predict the quality of MR images. And in the end, the correlation between SNR values and predicted results is analyzed.ResultsTo the same TOI in each MR imaging sequence, less than 6% voxels are overlapped between manual delineations. In the quality estimation of MR images, statistical analysis indicates no significant difference between observers (Wilcoxon rank sum test, pw ≥ 0.11; paired-sample t test, pp ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, picc ≥ 0.74). Furthermore, Pearson correlation coefficient (rp) suggests that SNRwm correlates strongly with BIQI, BLIINDS-II and BRISQUE in T2* (rp ≥ 0.78), BRISQUE and NIQE in T1 (rp ≥ 0.77), BLIINDS-II in T2 (rp ≥ 0.68) and BRISQUE and NIQE in T1C (rp ≥ 0.62) weighted MR images, while SNRcsf correlates strongly with BLIINDS-II in T2* (rp ≥ 0.63) and in T2 (rp ≥ 0.64) weighted MR images.ConclusionsThe consistency of SNR measurement is validated regarding various observers and MR imaging protocols. When SNR measurement performs as the quality indicator of MR images, BRISQUE and BLIINDS-II can be conditionally used for the automated quality estimation of human brain MR images.