Mean Intraclass Correlation Coefficient Research Articles

Accuracy of Three-Dimensional Neo Left Ventricular Outflow Tract Simulations With Transcatheter Mitral Valve Replacement in Different Mitral Phenotypes.

Transcatheter mitral valve replacement (TMVR) is emerging in the context of annular calcification (valve-in-MAC; ViMAC), failing surgical mitral annuloplasty (mitral-valve-in-ring; MViR) and failing mitral bioprosthesis (mitral-valve-in-valve; MViV). A notorious risk of TMVR is neo left ventricular outflow tract (neo-LVOT) obstruction. Three-dimensional computational models (3DCM) are derived from multi-slice computed tomography (MSCT) and aim to predict neo-LVOT area after TMVR. Little is known about the accuracy of these neo-LVOT predictions for various mitral phenotypes. Preprocedural 3DCMs were created for ViMAC, MViR and MViV cases. Throughout the cardiac cycle, neo-LVOT dimensions were semi-automatically calculated on the 3DCMs. We compared the predicted neo-LVOT area on the preprocedural 3DCM with the actual neo-LVOT as measured on the post-procedural MSCT. Across 12 TMVR cases and examining 20%-70% of the cardiac phase, the mean difference between predicted and post-TMVR neo-LVOT area was -23 ± 28 mm2 for MViR, -21 ± 34 mm2 for MViV and -73 ± 61 mm2 for ViMAC. The mean intra-class correlation coefficient for absolute agreement between predicted and post-procedural neo-LVOT area (throughout the whole cardiac cycle) was 0.89 (95% CI 0.82-0.94, p < 0.001) for MViR, 0.81 (95% CI 0.62-0.89, p < 0.001) for MViV, and 0.41 (95% CI 0.12-0.58, p = 0.002) for ViMAC. Three-dimensional computational models accurately predict neo-LVOT dimensions post TMVR in MViR and MViV but not in ViMAC. Further research should incorporate device host interactions and the effect of changing hemodynamics in these simulations to enhance accuracy in all mitral phenotypes.

Core laboratory versus center-reported echocardiographic assessment of the native and bioprosthetic aortic valve

Abstract Background The use of a central echocardiographic core laboratory (ECL) is advised to minimize measurement variation and heterogeneity in clinical trials. Insights into quantitative differences between ECL and center-reported echocardiographic assessment of the native and bioprosthetic aortic valve are lacking. We aimed to explore clinically relevant differences between these evaluations. Methods Data were used from the PERIcardial SurGical AOrtic Valve ReplacemeNt (PERIGON) Pivotal Trial for the Avalus valve. In this trial, patients with an indication for surgical aortic valve replacement (SAVR) due to aortic stenosis or regurgitation (AR) were enrolled. Serial echocardiographic examinations were performed at each center and reanalyzed by an independent ECL. For the bioprosthetic valve analysis, postoperative data throughout 5-year follow-up were pooled. Differences between the ECL and the centers in continuous parameters were quantified in mean differences and intraclass correlation coefficients (ICCs). Between-center differences were illustrated by plotting standardized mean differences (SMDs) between centers and the ECL for the 10 sites that implanted the most prostheses. Agreement on AR, paravalvular leak (PVL), and prosthesis-patient mismatch (PPM) classification was investigated using Cohen’s kappa coefficients. Results The analysis on the native aortic valve was performed on 1118 patients. The relative mean difference was largest for the left ventricular outflow tract (LVOT) area, followed by stroke volume and effective orifice area (index), with center-reported values being 11-7% higher (Table 1). High ICCs of around 0.90 were observed for peak aortic jet velocity, mean pressure gradient, and the velocity-time integral across the aortic valve. More than 5000 echocardiograms were available for the bioprosthetic valve analysis. Therein, comparable results were observed. In Figure 1, differences in assessment on center level are illustrated. The SMDs for peak velocity were between 0 and 0.5 for all centers, while there was more heterogeneity between centers for other parameters. The kappa coefficient was 0.59 (95% confidence interval [CI] 0.56, 0.63) for agreement on native AR, 0.28 (95% CI 0.18, 0.37) for PVL, and 0.42 (95% CI 0.40, 0.44) for PPM. Conclusions For echocardiographic assessment of the native and bioprosthetic aortic valve, agreement between the ECL and the clinical centers varies by parameter and by center. There is high agreement on continuous-wave Doppler-related measurements. On the contrary, agreement is low for parameters that involve measurement of the LVOT diameter. These results provide important context for the interpretation of aortic valve performance in studies that lack central ECL evaluation.

Assessment of Interrater Reliability and Accuracy of Cerebral Aneurysm Morphometry Using 3D Virtual Reality, 2D Digital Subtraction Angiography, and 3D Reconstruction: A Randomized Comparative Study.

Detailed morphometric analysis of an aneurysm and the related vascular bifurcation are critical factors when determining rupture risk and planning treatment for unruptured intracranial aneurysms (UIAs). The standard visualization of digital subtraction angiography (DSA) and its 3D reconstruction on a 2D monitor provide precise measurements but are subject to variability based on the rater. Visualization using virtual (VR) and augmented reality platforms can overcome those limitations. It is, however, unclear whether accurate measurements of the aneurysm and adjacent arterial branches can be obtained on VR models. This study aimed to assess interrater reliability and compare measurements between 3D VR, standard 2D DSA, and 3D DSA reconstructions, evaluating the reliability and accuracy of 3D VR as a measurement tool. A pool of five neurosurgeons performed three individual analyses on each of the ten UIA cases, measuring them in completely immersed 3D VR and the standard on-screen format (2D DSA and 3D reconstruction). This resulted in three independent measurements per modality for each case. Interrater reliability of measurements and morphology characterization, comparative differences, measurement duration, and VR user experience were assessed. Interrater reliability for 3D VR measurements was significantly higher than for 3D DSA measurements (3D VR mean intraclass correlation coefficient [ICC]: 0.69 ± 0.22 vs. 3D DSA mean ICC: 0.36 ± 0.37, p = 0.042). No significant difference was observed between 3D VR and 2D DSA (3D VR mean ICC: 0.69 ± 0.22 vs. 2D DSA mean ICC: 0.43 ± 0.31, p = 0.12). A linear mixed-effects model showed no effect of 3D VR and 3D DSA (95% CI = -0.26-0.28, p = 0.96) or 3D VR and 2D DSA (95% CI = -0.02-0.53, p = 0.066) on absolute measurements of the aneurysm in the anteroposterior, mediolateral, and craniocaudal dimensions. 3D VR technology allows for reproducible, accurate, and reliable measurements comparable to measurements performed on a 2D screen. It may also potentially improve precision for measurements of non-planar aneurysm dimensions.

Non-invasive measurement of wall shear stress in microfluidic chip for osteoblast cell culture using improved depth estimation of defocus particle tracking method.

The development of a non-invasive method for measuring the internal fluid behavior and dynamics of microchannels in microfluidics poses critical challenges to biological research, such as understanding the impact of wall shear stress (WSS) in the growth of a bone-forming osteoblast. This study used the General Defocus Particle Tracking (GDPT) technique to develop a non-invasive method for quantifying the fluid velocity profile and calculated the WSS within a microfluidic chip. The GDPT estimates particle motion in a three-dimensional space by analyzing two-dimensional images and video captured using a single camera. However, without a lens to introduce aberration, GDPT is prone to error in estimating the displacement direction for out-of-focus particles, and without knowing the exact refractive indices, the scaling from estimated values to physical units is inaccurate. The proposed approach addresses both challenges by using theoretical knowledge on laminar flow and integrating results obtained from multiple analyses. The proposed approach was validated using computational fluid dynamics (CFD) simulations and experimental video of a microfluidic chip that can generate different WSS levels under steady-state flow conditions. By comparing the CFD and GDPT velocity profiles, it was found that the Mean Pearson Correlation Coefficient is 0.77 (max = 0.90) and the Mean Intraclass Correlation Coefficient is 0.66 (max = 0.82). The densitometry analysis of osteoblast cells cultured on the designed microfluidic chip for four days revealed that the cell proliferation rate correlates positively with the measured WSS values. The proposed analysis can be applied to quantify the laminar flow in microfluidic chip experiments without specialized equipment.

The morphometry of distal tibia and posterior malleolus and its clinical implications in total ankle prosthesis.

The aim of this study is to reveal the morphometry of the distal tibia and posterior malleolus and to generate morphometric reference data for the tibial component of total ankle prosthesis. This study was performed on 121 human dry tibiae (47 right, 74 left). The morphometric measurements of distal tibial structures, tibial length and the distance between the medial and posterior malleolus were measured in this study. Measurements on 44 tibiae were repeated three times and averaged for minimizing intra-observer error. The tibial length was found 34.19 ± 2.31cm. Mean values of width of fibular notch at tibial plafond and 10mm proximal to the tibial plafond were 25.71 ± 2.44mm and 17.81 ± 2.46mm, respectively. Mean depth of fibular notch at tibial plafond and 10mm proximal to the tibial plafond were 3.60 ± 1.04mm and 3.37 ± 1.24mm, respectively. Mean height of fibular notch was found 48.21 ± 10.51mm. Mean width and height of medial malleolus were 25.08 ± 2.13mm and 14.73 ± 1.85mm, respectively. Mean width and length of tibial plafond were 27.71 ± 2.74mm and 26.96 ± 2.62mm, respectively. Mean values of width and height of posterior malleolus were measured 21.41 ± 3.26mm and 6.74 ± 1.56mm, respectively. Mean distance between medial and posterior malleolus was found 37.17 ± 3.53mm. Mean width and depth of malleolar groove were 10.26 ± 1.84mm and 1.73 ± 0.75mm, respectively. The mean intra-class correlation values were found between the 0.959 and 0.999. Knowing the distal tibial morphometry is crucial for designing convenient ankle replacement implants for Turkish population. To our knowledge, this study is the first in the literature that identifies posterior malleolar morphometry on dry tibiae. We believe that this study will make a significant contribution to the literature about distal tibial morphometry and especially the posterior malleolus and the data of our study can be used for designing total ankle prosthesis in Turkish population.

Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features.

This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques. Under IRB approval and informed consent, we prospectively collected undersampled coronal T2-weighted MR images of the abdomen (1.5 T; Philips Healthcare) from 17 pediatric and adult subjects and reconstructed them using a conventional image reconstruction technique (compressed sensitivity encoding [C-SENSE]) and two DL-based reconstruction techniques (SmartSpeed [Philips Healthcare, US FDA cleared] and SmartSpeed with Super Resolution [SmartSpeed-SuperRes, not US FDA cleared to date]). Eight regions of interest (ROIs) across organs/tissues (liver, spleen, kidney, pancreas, fat, and muscle) were manually placed. Eighty-six MRI radiomic features were then extracted. Pearson's correlation coefficients (PCCs) and intraclass correlation coefficients (ICCs) were calculated between (A) C-SENSE versus SmartSpeed, and (B) C-SENSE versus SmartSpeed-SuperRes. To quantify the impact from the perspective of the whole MR image, cross-ROI mean PCCs and ICCs were calculated for individual radiomic features. The impact of image reconstruction on individual radiomic features in different organs/tissues was evaluated using ANOVA analyses. According to cross-ROI mean PCCs, 50 out of 86 radiomic features were highly correlated (PCC, ≥0.8) between SmartSpeed and C-SENSE, whereas only 15 radiomic features were highly correlated between SmartSpeed-SuperRes and C-SENSE reconstructions. According to cross-ROI mean ICCs, 58 out of 86 radiomic features had high agreements (ICC ≥0.75) between SmartSpeed and C-SENSE, whereas only 9 radiomic features had high agreements between SmartSpeed-SuperRes and C-SENSE reconstructions. For SmartSpeed reconstruction, the psoas muscle ROI appeared to be impacted most with the lowest median (IQR) correlation of 0.57 (0.25). The circular liver ROI was impacted most by SmartSpeed-SuperRes (PCC, 0.60 [0.22]). ANOVA analyses suggest that the impact of DL reconstruction algorithms on radiomic features varies significantly among different organs/tissues (P < 0.001). MRI radiomic features are significantly altered by DL-based reconstruction compared to a conventional reconstruction technique. The impact of DL reconstruction algorithms on radiomic features varies significantly between different organs/tissues.

Development and Assessment of a Rubric for Evaluating Teaching Portfolios Developed by Teaching and Learning Curriculum (TLC) Program Participants

ObjectiveTo describe the validation and reliability assessment of a rubric designed to assess the participants’ teaching portfolios and identify teaching excellence among teaching and learning curriculum (TLC) program participants. MethodsFollowing focus groups with program leadership at a single TLC program, an initial rubric was developed, consisting of criteria mapped to 5 domains, to be rated on a 4-point scale. The rubric was then redistributed to the TLC program leadership and external stakeholders for evaluation of face and content validity. The rubric was piloted using teaching portfolios from 3 cycles of the program. Cronbach αwas used to measure internal consistency and a 2-way random-effects model was used to assess the inter-rater reliability. ResultsA total of 18 portfolios were independently evaluated by 4 raters. The overall mean Cronbach α for internal consistency was 0.90 and ranged from 0.65 to 0.84 by domain. The overall mean intraclass correlation coefficient for inter-rater reliability was 0.95 and ranged from 0.57 to 0.91 by domain. ConclusionThe rubric evaluates characteristics of teaching portfolios important to internal and external stakeholders and had good to excellent internal consistency and inter-rater reliability. It can be adapted and applied by TLC programs to identify teaching excellence.

Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups

Spinal deformities, including adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD), affect many patients. The measurement of the Cobb angle on coronal radiographs is essential for their diagnosis and treatment planning. To enhance the precision of Cobb angle measurements for both AIS and ASD, we developed three distinct artificial intelligence (AI) algorithms: AIS/ASD-trained AI (trained with both AIS and ASD cases); AIS-trained AI (trained solely on AIS cases); ASD-trained AI (trained solely on ASD cases). We used 1612 whole-spine radiographs, including 1029 AIS and 583 ASD cases with variable postures, as teaching data. We measured the major and two minor curves. To assess the accuracy, we used 285 radiographs (159 AIS and 126 ASD) as a test set and calculated the mean absolute error (MAE) and intraclass correlation coefficient (ICC) between each AI algorithm and the average of manual measurements by four spine experts. The AIS/ASD-trained AI showed the highest accuracy among the three AI algorithms. This result suggested that learning across multiple diseases rather than disease-specific training may be an efficient AI learning method. The presented AI algorithm has the potential to reduce errors in Cobb angle measurements and improve the quality of clinical practice.

Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT.

The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). One hundred eighty-three patients with pulmonary nodules underwent standard-dose computed tomography (SDCT) (4.30 ± 0.36 mSv) and ULD-CT (UL-A, 0.57 ± 0.09 mSv or UL-B, 0.33 ± 0.04 mSv). SDCT was the reference standard using (ASIR-V) at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). Radiomics analysis extracted 102 features, and the intraclass correlation coefficient (ICC) quantified reproducibility between ULD-CT and SDCT reconstructed by 50%ASIR-V, DLIR-M, and DLIR-H for each feature. Among 102 radiomic features, the percentages of reproducibility of 50%ASIR-V, DLIR-M, and DLIR-H were 48.04% (49/102), 49.02% (50/102), and 52.94% (54/102), respectively. Shape and first order features demonstrated high reproducibility across different reconstruction algorithms and radiation doses, with mean ICC values exceeding 0.75. In texture features, DLIR-M and DLIR-H showed improved mean ICC values for pure ground glass nodules (pGGNs) from 0.69 ± 0.23 to 0.75 ± 0.18 and 0.81 ± 0.12, respectively, compared with 50%ASIR-V. Similarly, the mean ICC values for solid nodules (SNs) increased from 0.60 ± 0.19 to 0.66 ± 0.14 and 0.69 ± 0.13, respectively. Additionally, the mean ICC values of texture features for pGGNs and SNs in both ULD-CT groups decreased with reduced radiation dose. DLIR can improve the reproducibility of radiomic features at ultra-low doses compared with ASIR-V. In addition, pGGNs showed better reproducibility at ultra-low doses than SNs.

Psychometric Properties of the Persian Version of the Warwick-Edinburgh Mental Wellbeing Scale (Pr-WEMWBS)

Background: The Warwick-Edinburgh Mental Well-being Scale (WEMWBS) is one of the most important scales for measuring mental well-being, and it has been translated into various languages. Objectives: This study aims to examine the psychometric properties of the Persian version of the Warwick-Edinburgh Mental Well-being Scale (Pr-WEMWBS). Methods: Face and content validity, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), Cronbach’s alpha coefficient, and intraclass correlation coefficient (ICC) were assessed. The WEMWBS was translated using the forward-backward translation method. Eleven employees and eight experts evaluated the face and content validity of the scale. A total of 193 employees were recruited for construct validity, and 36 employees were involved in the test-retest reliability assessment. Results: The impact score for all items was above 4.30. In the EFA, the Kaiser–Meyer–Olkin (KMO) value was 0.87, and Bartlett's test (χ2 = 1216.28, P ≤ 0.000) confirmed the adequacy of the sample size. Using direct Oblimin rotation, three latent factors were extracted with eigenvalues greater than 1. In the CFA, the model fit summary indices were satisfactory (GFI = 1.000, RMSEA = 0.10, CFI = 1.00, CMIN/DF = 2.91, PNFI = 0.673, PCFI = 0.714). Cronbach's alpha coefficient was 0.901. The mean ICC was 0.889 (range: 0.863 to 0.912). Conclusions: The Pr-WEMWBS demonstrates good validity and reliability across most psychometric indices, making it suitable for use in Persian-speaking communities.

Inter- and Intra-Observer Variability of the AMADEUS Tool for Osteochondral Lesions of the Talus.

Managing osteochondral cartilage defects (OCDs) of the talus is a common daily challenge in orthopaedics as they predispose patients to further cartilage damage and progression to osteoarthritis. Therefore, the implementation of a reliable tool to quantify the amount of cartilage damage that is present is of the essence. We retrospectively identified 15 adult patients diagnosed with uncontained OCDs of the talus measuring <150 mm2, which were treated arthroscopically with bone marrow stimulation. Five independent assessors evaluated the pre-operative MRI scans with the AMADEUS scoring system (i.e., MR-based pre-operative assessment system) and the intra-/inter-observer variability was then calculated by means of the intraclass correlation coefficients (ICC) and Kappa (κ) statistics, respectively. In addition, the correlation between the mean AMADEUS scores and pre-operative self-reported outcomes as measured by the Manchester-Oxford foot questionnaire (MOxFQ) was assessed. The mean ICC and the κ statistic were 0.82 (95% CI [0.71, 0.94]) and 0.42 (95% CI [0.25, 0.59]). The Pearson correlation coefficient was found to be r = -0.618 (p = 0.014). The AMADEUS tool, which was originally designed to quantify knee osteochondral defect severity prior to cartilage repair surgery, demonstrated good reliability and moderate inter-observer variability for small OCDs of the talar shoulder. Given the strong negative correlation between the AMADEUS tool and pre-operative clinical scores, this tool could be implemented in clinical practise to reliably quantify the extent of the osteochondral defects of the talus.

Test-Retest Reliability of a Physical Activity Behavior, Health and Wellbeing Questionnaire in Adolescents

Background The aim of this study was to examine the test-retest reliability of the physical activity behavior, health and wellbeing questionnaire, in adolescent populations, administered by teachers in school settings, in the Republic of Ireland. Methods A cross-sectional, mixed sample of 55 participants (45.5% males: Age, 13.94 (±.40) years) were included. The participants completed the questionnaire on two occasions (T1 and T2), on the same day and time, one week apart following identical procedures. Variables for testing included physical activity behavior (n=13), health (n=11) and wellbeing (n=2). Test-retest reliability of the questionnaire’s covariates, including family affluence and physical impairments were also examined. Results Systematic error (Bland-Altman plots) was found to be near to zero for each of the physical activity behavior, health and wellbeing variables. The combined mean coefficient of variation was lower for females (10.19%) in comparison to males (13.01%). The combined mean intraclass correlation coefficients were higher for females (0.901) than males (0.822). Similarly, the combined mean Cronbach alpha coefficient were higher for girls (0.908) than boys (0.821). Conclusions This study found the physical activity behavior, health and wellbeing questionnaire to be reliable for use in adolescent populations.

Dairy cow personality: Consistency in a familiar testing environment

Personality affects the behavior of dairy cows in response to various situations on farm, such as novel or social challenges, which can affect the welfare of individual cows. Personality traits are evaluated through behavioral testing, typically in unfamiliar environments, but this can be a time-consuming process. Using a novel object test (NOT) and a forced human approach test (FHAT), we tested 222 individual dairy cows and retested a subset of cows (n = 78) 8 mo afterward. The aim was to see if personality traits can be identified in a familiar environment and if traits were consistent in between test and retest. Principal component analysis was used to identify personality traits. Extracted components (personality traits) were named according to determining variables (loadings <−0.63 or >0.63). Based on identified personality traits we calculated the intraclass correlation (ICC) coefficient and the differences between personality trait in test and retest to assess consistency. We identified 3 personality traits from NOT measurements (“explorative,” “boldness,” and “sociable”) explaining 85% of the variance and one trait from behavioral measurements in the FHAT (“trusting”) explaining 74% of the variance. Mean ICC of 0.6 with a standard deviation of 0.18 shows that although traits are consistent at herd level, cows react individually in stressful situations. As both tests were carried out in a familiar environment and results are similar to other studies that used unfamiliar environments, we can conclude that tests can be performed in a familiar environment.

Transfer accuracy of 3D printed versus CAD/CAM milled surgical guides for temporary orthodontic implants: A preclinical micro CT study

ObjectivesTemporary anchorage devices (TADs) have become an integral part of comprehensive orthodontic treatments. This study evaluated the transfer accuracy of three-dimensional (3D) printed and computer-aided design/computer-aided manufacturing (CAD/CAM) milled surgical guides for orthodontic TADs using micro-computed tomography (CT) imaging in a preclinical trial. MethodsOverall, 30 surgical guides were used to place TADs into typodonts; 3D printing and CAD/CAM milling were used to produce the guides. The virtual target positions of the TADs were compared to the real positions in terms of spatial and angular deviations using digital superimposition. Micro-CT imaging was used to detect the positions. To evaluate reliability, two investigators collected the measurements twice. Intra-rater and inter-rater correlations were tested. ResultsIn total, 60 palatal TADs were evaluated. The mean coronal deviations in the print group ranged from 0.15 ± 0.20 mm to 0.71 ± 0.22 mm, whereas in the mill group, they ranged from 0.09 ± 0.15 mm to 0.83 ± 0.23 mm. At the apical tip, the overall deviations in the print group ranged from 0.14 ± 0.56 mm to 1.27 ± 0.66 mm, whereas in the mill group, they ranged from 0.15 ± 0.57 mm to 1.09 ± 0.44 mm. The mean intra-class and inter-class correlation coefficients ranged from 0.904 to 0.987. No statistically significant differences were found between the groups. ConclusionsCAD/CAM milled guides yielded spatial and angular accuracies comparable to those of 3D printed guides with notable deviations in the vertical positioning of TADs. Clinical significanceDigital planning of orthodontic temporary implants combines clinical predictability and the safety of surrounding tissue. Therefore, the transfer accuracy of the guides is crucial. This preclinical study was the first to evaluate CAD/CAM milling for orthodontic guides and found its accuracy comparable to that of the current "gold standard".

Quantification of water exchange across the blood-brain barrier using noncontrast MR fingerprinting.

A method is proposed to quantify cerebral blood volume ( ) and intravascular water residence time ( ) using MR fingerprinting (MRF), applied using a spoiled gradient echo sequence without the need for contrast agent. An in silico study optimized an acquisition protocol to maximize the sensitivity of the measurement to and changes. Its accuracy in the presence of variations in , , and was evaluated. The optimized protocol (scan time of 19 min) was then tested in a exploratory healthy volunteer study (10 volunteers, mean age 24 3, six males) at 3 T with a repeat scan taken after repositioning to allow estimation of repeatability. Simulations show that assuming literature values for and , no variation in , while fitting only and , leads to large errors in quantification of and , regardless of noise levels. However, simulations also show that matching , , , and , simultaneously is feasible at clinically achievable noise levels. Across the healthy volunteers, all parameter quantifications fell within the expected literature range. In addition, the maps show good agreement between hemispheres suggesting physiologically relevant information is being extracted. Expected differences between white and gray matter (p < 0.0001) and (p < 0.0001) are observed, and show no significant differences, p = 0.4 and p = 0.6, respectively. Moderate to excellent repeatability was seen between repeat scans: mean intra-class correlation coefficient of , , , and . We demonstrate that regional simultaneous quantification of , , , and using MRF is feasible in vivo.

Artificial intelligence to automate assessment of ocular and periocular measurements.

To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements. Patients presenting to the ophthalmology clinic were prospectively enrolled and had their images taken using a standardised protocol. Facial landmarks were segmented on the images to enable calculation of marginal reflex distance (MRD) 1 and 2, palpebral fissure height (PFH), inner intercanthal distance (IICD), outer intercanthal distance (OICD), interpupillary distance (IPD) and horizontal palpebral aperture (HPA). These manual segmentations were used to train a machine learning algorithm to automatically detect facial landmarks and calculate these measurements. The main outcomes were the mean absolute error and intraclass correlation coefficient. A total of 958 eyes from 479 participants were included. The testing set consisted of 290 eyes from 145 patients. The AI algorithm demonstrated close agreement with human measurements, with mean absolute errors ranging from 0.22 mm for IPD to 0.88 mm for IICD. The intraclass correlation coefficients indicated excellent reliability (ICC > 0.90) for MRD1, MRD2, PFH, OICD, IICD, and IPD, while HPA showed good reliability (ICC 0.84). The landmark detection model was highly accurate and achieved a mean error rate of 0.51% and failure rate at 0.1 of 0%. The automated facial landmark detection network provided accurate and reliable periocular measurements. This may help increase the objectivity of periocular measurements in the clinic and may facilitate remote assessment of patients with tele-health.

A method for calculating fall risk parameters from discrete stride time series regardless of sensor placement

BackgroundTo complement traditional clinical fall risk assessments, research is oriented towards adding real-life gait-related fall risk parameters (FRP) using inertial sensors fixed to a specific body position. While fixing the sensor position can facilitate data processing, it can reduce user compliance. A newly proposed step detection method, Smartstep, has been proven to be robust against sensor position and real-life challenges. Moreover, FRP based on step variability calculated from stride times (Standard deviation (SD), Coefficient of Variance (Cov), fractal exponent, and sample entropy of stride duration) proved to be useful to prospectively predict the fall risk. Research questionsTo evaluate whether Smartstep is convenient for calculating FRP from different sensor placements. Methods29 elderly performed a 6-minute walking test with IMU placed on the waist and the wrist. FRP were computed from step-time estimated from Smartstep and compared to those obtained from foot-mounted inertial sensors: precision and recall of the step detection, Root mean square error (RMSE) and Intraclass Correlation Coefficient (ICC) of stride durations, and limits of agreement of FRP. ResultsThe step detection precision and recall were respectively 99.5% and 95.9% for the waist position, and 99.4% and 95.7% for the wrist position. The ICC and RMSE of stride duration were 0.91 and 54 ms respectively for both the waist and the hand position. The limits of agreement of Cov, SD, fractal exponent, and sample entropy of stride duration are respectively 2.15%, 25 ms, 0.3, 0.5 for the waist and 1.6%, 16 ms, 0.23, 0.4 for the hand. SignificanceRobust against the elderly's gait and different body locations, especially the wrist, this method can open doors toward ambulatory measurements of steps, and calculation of different discrete stride-related falling risk indicators.

SAEM systematic online academic resource (SOAR) review: Gastrointestinal illnesses.

Free open access medical education (FOAM) has become an essential tool for emergency medicine (EM) education and can be valuable to clinicians as a point-of-care resource. The development of the revised Medical Education Translational Resources Impact and Quality (rMETRIQ) tool provides a standardized means of quality assessment. Previous entries of the Society for Academic Emergency Medicine systematic online academic resource (SOAR) series have focused on renal, endocrine, and sickle cell disorders. In this iteration, we strive to identify, curate, and describe FOAM topics specific to acute gastrointestinal (GI) illnesses. We searched 389 keywords across 11 GI topics that were modified from the 2019 Model of the Clinical Practice of EM (EM Model) using the search engine Google FOAM and within the top 50 websites listed on Academic Life in Emergency Medicine's Social Media Index. The sites underwent preliminary screening to eliminate resources that were not relevant to EM or GI illnesses. Identified resources were evaluated with the rMETRIQ tool by five board-certified EM physicians who received rMETRIQ tool rater training. After duplicates of the initial 39,505 resources were eliminated, 8059 remained. Primary screening resulted in a final 1202 resources. The most common categories were large bowel (18%), small bowel (13%), stomach (11%), esophagus (11%), biliary (11%), and liver (10%). Many resources covered multiple topics and subtopics. The final mean intraclass correlation coefficient among the five physicians was 0.95 (95% CI 0.92-0.98) for rMETRIQ scoring. We identified 256 sites considered "high quality" with a rMETRIQ score of 16 or higher as designated in prior reviews. This iteration of the SOAR review resulted in the highest number of high-quality resources compared to other SOAR reviews, with 21% of resources thus far scoring ≥ 16. A final list of high-quality resources can guide trainees, educator recommendations, and FOAM authors.

Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.

To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net. This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson's correlation and Bland-Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment. All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06mm) and MR models (1.36 ± 0.61mm) were significantly lower than that of the CT model (3.48 ± 5.0mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91-0.93). The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI. Deep learning-based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning. • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models.

Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.

To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI). This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets. From a collaborating institution, de-identified scans were used for external testing. The public LiverHccSeg dataset was used for further external validation. A 3D DCNN was trained to automatically segment the liver. Segmentation accuracy was quantified by theDice similarity coefficient (DSC) with respect to manual segmentation. A Mann-Whitney U test was used to compare the internal and external test sets. Agreement of volumetry and radiomic features was assessed using the intraclass correlation coefficient (ICC). In total, 470 patients met the inclusion criteria (63.9±8.2 years; 376 males) and 20 patients were used for external validation (41±12 years; 13 males). DSC segmentation accuracy of the DCNN was similarly high between the internal (0.97±0.01) and external (0.96±0.03) test sets (p=0.28) and demonstrated robust segmentation performance on public testing (0.93±0.03). Agreement of liver volumetry was satisfactory in the internal (ICC, 0.99), external (ICC, 0.97), and public (ICC, 0.85) test sets. Radiomic features demonstrated excellent agreement in the internal (mean ICC, 0.98±0.04), external (mean ICC, 0.94±0.10), and public (mean ICC, 0.91±0.09) datasets. Automated liver segmentation yields robust and generalizable segmentation performance on MRI data and can be used for volumetry and radiomic feature extraction. Liver volumetry, anatomic localization, and extraction of quantitative imaging biomarkers require accurate segmentation, but manual segmentation is time-consuming. A deep convolutional neural network demonstrates fast and accurate segmentation performance on T1-weighted portal venous MRI. • This deep convolutional neural network yields robust and generalizable liver segmentation performance on internal, external, and public testing data. • Automated liver volumetry demonstrated excellent agreement with manual volumetry. • Automated liver segmentations can be used for robust and reproducible radiomic feature extraction.