Showing papers on "Intraclass correlation published in 2022"

The clinician-administered PTSD Scale for DSM-5 (CAPS-5) structured interview for PTSD: A French language validation study.

Abstract: The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) is a structured interview that assesses the frequency and severity of each symptom of posttraumatic stress disorder (PTSD) in relation to a single traumatic stressor over a 1-month period, allowing the trained interviewer to infer a current or lifetime diagnosis congruent with the 5th Edition of the Diagnostic and Statistical Manual of the American Psychiatric Association. This study evaluated the psychometric properties of the original English language CAPS-5 translated to French. Participants (N = 168) were recruited in clinical settings of France, Lebanon, and Canada. The psychometric properties of the measure were found to be excellent, as good-to-strong interitem consistency was found (α = .90; ITC = .52; ICC = .30), while also finding strong convergent validity between the CAPS-5 total score and the severity score of a self-report PTSD measure (r = .82): the PCL-5. The test-retest reliability was excellent, with Cohen's κ = 1.00 and the intraclass coefficient (ICC) = .95. However, no latent factor structure model was deemed a strong fit to the data. Overall, the reliability and validity of the French CAPS-5 and are consistent with those of the original CAPS-5. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Three-dimensional Multistructural Quantitative Photoacoustic and US Imaging of Human Feet in Vivo.

Abstract: Background Monitoring the microcirculation in human feet is crucial in assessing peripheral vascular diseases, such as diabetic foot. However, conventional imaging modalities are more focused on diagnosis in major arteries, and there are limited methods to provide microvascular information in early stages of the disease. Purpose To investigate a three-dimensional (3D) noncontrast bimodal photoacoustic (PA)/US imaging system that visualizes the human foot morphologically and also reliably quantifies podiatric vascular parameters noninvasively. Materials and Methods A clinically relevant PA/US imaging system was combined with a foot scanner to obtain 3D PA and US images of the human foot in vivo. Healthy participants were recruited from September 2020 to June 2021. The collected 3D PA and US images were postprocessed to present structural information about the foot. The quantitative reliability was evaluated in five repeated scans of 10 healthy feet by calculating the intraclass correlation coefficient and minimal detectable change, and the detectability of microvascular changes was tested by imaging 10 healthy feet intentionally occluded with use of a pressure cuff (160 mm Hg). Statistically significant difference is indicated with P values. Results Ten feet from six healthy male volunteers (mean age ± standard deviation, 27 years ± 3) were included. The foot images clearly visualized the structure of the vasculature, bones, and skin and provided such functional information as the total hemoglobin concentration (HbT), hemoglobin oxygen saturation (SO2), vessel density, and vessel depth. Functional information from five independent measurements of 10 healthy feet was moderately reliable (intraclass correlation coefficient, 0.51-0.74). Significant improvements in HbT (P = .006) and vessel density (P = .046) as well as the retention of SO2 were observed, which accurately described the microvascular change due to venous occlusion. Conclusion Three-dimensional photoacoustic and US imaging was able to visualize morphologic and physiologic features of the human foot, including the peripheral microvasculature, in healthy volunteers. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Mezrich in this issue.

Chest Radiograph Scoring Alone or Combined with Other Risk Scores for Predicting Outcomes in COVID-19

Abstract: Background Radiographic severity may help predict patient deterioration and outcomes from COVID-19 pneumonia. Purpose To assess the reliability and reproducibility of three chest radiograph reporting systems (radiographic assessment of lung edema [RALE], Brixia, and percentage opacification) in patients with proven SARS-CoV-2 infection and examine the ability of these scores to predict adverse outcomes both alone and in conjunction with two clinical scoring systems, National Early Warning Score 2 (NEWS2) and International Severe Acute Respiratory and Emerging Infection Consortium: Coronavirus Clinical Characterization Consortium (ISARIC-4C) mortality. Materials and Methods This retrospective cohort study used routinely collected clinical data of patients with polymerase chain reaction-positive SARS-CoV-2 infection admitted to a single center from February 2020 through July 2020. Initial chest radiographs were scored for RALE, Brixia, and percentage opacification by one of three radiologists. Intra- and interreader agreement were assessed with intraclass correlation coefficients. The rate of admission to the intensive care unit (ICU) or death up to 60 days after scored chest radiograph was estimated. NEWS2 and ISARIC-4C mortality at hospital admission were calculated. Daily risk for admission to ICU or death was modeled with Cox proportional hazards models that incorporated the chest radiograph scores adjusted for NEWS2 or ISARIC-4C mortality. Results Admission chest radiographs of 50 patients (mean age, 74 years ± 16 [standard deviation]; 28 men) were scored by all three radiologists, with good interreader reliability for all scores, as follows: intraclass correlation coefficients were 0.87 for RALE (95% CI: 0.80, 0.92), 0.86 for Brixia (95% CI: 0.76, 0.92), and 0.72 for percentage opacification (95% CI: 0.48, 0.85). Of 751 patients with a chest radiograph, those with greater than 75% opacification had a median time to ICU admission or death of just 1-2 days. Among 628 patients for whom data were available (median age, 76 years [interquartile range, 61-84 years]; 344 men), opacification of 51%-75% increased risk for ICU admission or death by twofold (hazard ratio, 2.2; 95% CI: 1.6, 2.8), and opacification greater than 75% increased ICU risk by fourfold (hazard ratio, 4.0; 95% CI: 3.4, 4.7) compared with opacification of 0%-25%, when adjusted for NEWS2 score. Conclusion Brixia, radiographic assessment of lung edema, and percentage opacification scores all reliably helped predict adverse outcomes in SARS-CoV-2 infection. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Little in this issue.

Coronary Computed Tomography Angiography-Based Calcium Scoring

Abstract: The aim of this study was to evaluate coronary computed tomography angiography (CCTA)-based in vitro and in vivo coronary artery calcium scoring (CACS) using a novel virtual noniodine reconstruction (PureCalcium) on a clinical first-generation photon-counting detector-computed tomography system compared with virtual noncontrast (VNC) reconstructions and true noncontrast (TNC) acquisitions.Although CACS and CCTA are well-established techniques for the assessment of coronary artery disease, they are complementary acquisitions, translating into increased scan time and patient radiation dose. Hence, accurate CACS derived from a single CCTA acquisition would be highly desirable. In this study, CACS based on PureCalcium, VNC, and TNC, reconstructions was evaluated in a CACS phantom and in 67 patients (70 [59/80] years, 58.2% male) undergoing CCTA on a first-generation photon counting detector-computed tomography system. Coronary artery calcium scores were quantified for the 3 reconstructions and compared using Wilcoxon test. Agreement was evaluated by Pearson and Spearman correlation and Bland-Altman analysis. Classification of coronary artery calcium score categories (0, 1-10, 11-100, 101-400, and >400) was compared using Cohen κ .Phantom studies demonstrated strong agreement between CACS PureCalcium and CACS TNC (60.7 ± 90.6 vs 67.3 ± 88.3, P = 0.01, r = 0.98, intraclass correlation [ICC] = 0.98; mean bias, 6.6; limits of agreement [LoA], -39.8/26.6), whereas CACS VNC showed a significant underestimation (42.4 ± 75.3 vs 67.3 ± 88.3, P < 0.001, r = 0.94, ICC = 0.89; mean bias, 24.9; LoA, -87.1/37.2). In vivo comparison confirmed a high correlation but revealed an underestimation of CACS PureCalcium (169.3 [0.7/969.4] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.98; mean bias, -113.5; LoA, -470.2/243.2). In comparison, CACS VNC showed a similarly high correlation, but a substantially larger underestimation (24.3 [0/272.3] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.54; mean bias, -551.6; LoA, -2037.5/934.4). CACS PureCalcium showed superior agreement of CACS classification ( κ = 0.88) than CACS VNC ( κ = 0.60).The accuracy of CACS quantification and classification based on PureCalcium reconstructions of CCTA outperforms CACS derived from VNC reconstructions.

CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases

Abstract: Simple Summary The objective of the study was to assess the radiomic features obtained by computed tomography (CT) examination as prognostic biomarkers in patients with colorectal liver metastases, in order to predict histopathological outcomes following liver resection. We obtained good performance considering the single significant textural metric in the identification of the front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type, and in the detection of recurrences. Abstract Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study included a training set and an external validation set. The internal training set included 49 patients with a median age of 60 years and 119 liver colorectal metastases. The validation cohort consisted of 28 patients with single liver colorectal metastasis and a median age of 61 years. Radiomic features were extracted using PyRadiomics on CT portal phase. Nonparametric Kruskal–Wallis tests, intraclass correlation, receiver operating characteristic (ROC) analyses, linear regression modeling, and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The median value of intraclass correlation coefficients for the features was 0.92 (range 0.87–0.96). The best performance in discriminating expansive versus infiltrative front of tumor growth was wavelet_HHL_glcm_Imc2, with an accuracy of 79%, a sensitivity of 84%, and a specificity of 67%. The best performance in discriminating expansive versus tumor budding was wavelet_LLL_firstorder_Mean, with an accuracy of 86%, a sensitivity of 91%, and a specificity of 65%. The best performance in differentiating the mucinous type of tumor was original_firstorder_RobustMeanAbsoluteDeviation, with an accuracy of 88%, a sensitivity of 42%, and a specificity of 100%. The best performance in identifying tumor recurrence was the wavelet_HLH_glcm_Idmn, with an accuracy of 85%, a sensitivity of 81%, and a specificity of 88%. The best linear regression model was obtained with the identification of recurrence considering the linear combination of the 16 significant textural metrics (accuracy of 97%, sensitivity of 94%, and specificity of 98%). The best performance for each outcome was reached using KNN as a classifier with an accuracy greater than 86% in the training and validation sets for each classification problem; the best results were obtained with the identification of tumor front growth considering the seven significant textural features (accuracy of 97%, sensitivity of 90%, and specificity of 100%). Conclusions: This study confirmed the capacity of radiomics data to identify several prognostic features that may affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network

Abstract: The industrial gearboxes usually work in harsh and variable conditions, which results in partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of gearbox under variable conditions maybe increase the intraclass difference and reduce the interclass difference for the monitored samples. To this end, a new intelligent fault diagnosis method of gearbox based on adaptive intraclass and interclass convolutional neural network (AIICNN) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to improve the distribution differences of samples. Meanwhile, the adaptive activation function is added into the 1-D convolutional neural network (1dCNN) to enlarge the heterogeneous distance and narrow the homogeneous distance of samples. Specifically, the training sample subset with intraclass and interclass spacing fluctuations under variable conditions is first converted into frequency domain through the fast Fourier transform (FFT), and the designed AIICNN algorithm is employed for model training. Afterward, the testing subset is provided to the trained AIICNN algorithm for fault diagnosis. The experimental data of the planetary gearbox test rig verify the feasibility of the proposed diagnosis method and algorithm. Compared with other methods, this method can eliminate the difference of sample distribution under variable conditions and improve its diagnostic generalization.

EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases

Abstract: Simple Summary The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. Ours results confirmed the capacity of radiomics to identify, as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach. These results were confirmed by external validation dataset. We obtained a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Abstract The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS “Fondazione Pascale”. Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH_glcm_MaximumProbability extraxted on VIBE_FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE_FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL_glszm_ZoneVariance extraxted on VIBE_FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL_glcm_Correlation extraxted on VIBE_FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE_FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE_FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance

Abstract: To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2-weighted magnetic resonance imaging (MRI).This retrospective study included 101 patients with histology-proven spine bone tumor (22 benign; 38 primary malignant; 41 metastatic). All tumor volumes were manually segmented on morphologic T2-weighted sequences. The same region of interest (ROI) was used to perform radiomic analysis on ADC map. A total of 1702 radiomic features was considered. Feature stability was assessed through small geometrical transformations of the ROIs mimicking multiple manual delineations. Intraclass correlation coefficient (ICC) quantified feature stability. Feature selection consisted of stability-based (ICC > 0.75) and significance-based selections (ranking features by decreasing Mann-Whitney p-value). Class balancing was performed to oversample the minority (i.e., benign) class. Selected features were used to train and test a support vector machine (SVM) to discriminate benign from malignant spine tumors using tenfold cross-validation.A total of 76.4% radiomic features were stable. The quality metrics for the SVM were evaluated as a function of the number of selected features. The radiomic model with the best performance and the lowest number of features for classifying tumor types included 8 features. The metrics were 78% sensitivity, 68% specificity, 76% accuracy and AUC 0.78.SVM classifiers based on radiomic features extracted from T2- and diffusion-weighted imaging with ADC map are promising for classification of spine bone tumors. Radiomic features of spine bone tumors show good reproducibility rates.

Artificial Intelligence Application in Assessment of Panoramic Radiographs

Abstract: The aim of this study was to assess the reliability of the artificial intelligence (AI) automatic evaluation of panoramic radiographs (PRs). Thirty PRs, covering at least six teeth with the possibility of assessing the marginal and apical periodontium, were uploaded to the Diagnocat (LLC Diagnocat, Moscow, Russia) account, and the radiologic report of each was generated as the basis of automatic evaluation. The same PRs were manually evaluated by three independent evaluators with 12, 15, and 28 years of experience in dentistry, respectively. The data were collected in such a way as to allow statistical analysis with SPSS Statistics software (IBM, Armonk, NY, USA). A total of 90 reports were created for 30 PRs. The AI protocol showed very high specificity (above 0.9) in all assessments compared to ground truth except from periodontal bone loss. Statistical analysis showed a high interclass correlation coefficient (ICC > 0.75) for all interevaluator assessments, proving the good credibility of the ground truth and the reproducibility of the reports. Unacceptable reliability was obtained for caries assessment (ICC = 0.681) and periapical lesions assessment (ICC = 0.619). The tested AI system can be helpful as an initial evaluation of screening PRs, giving appropriate credibility reports and suggesting additional diagnostic methods for more accurate evaluation if needed.

Psychometric properties of the Persian version of Depression Anxiety Stress Scale-21 Items (DASS-21) in a sample of health professionals: a cross-sectional study

Abstract: Although the Depression Anxiety Stress Scale-21 Items (DASS-21) has been used in different countries and translated into different languages, the Persian version of this scale has not been validated for healthcare professions in Iran. Therefore, the purpose of this study was to examine the psychometric properties of the Persian version of DASS-21 for nurses.This cross-sectional study was conducted among 1135 nurses working in public hospitals, who were selected through convenience sampling. DASS-21, which consists of 21 items and three dimensions (depression, anxiety, and stress), has been translated into Persian, and there is an online version available. A confirmatory factor analysis (CFA) was performed to examine the factor structure of this scale. Cronbach's alpha coefficient was also measured to establish internal consistency. Besides, the intraclass correlation coefficient (ICC) was calculated to assess the test-retest reliability.The Cronbach's alpha coefficient was acceptable for anxiety (0.79), stress (0.91), and depression (0.93). An acceptable test-retest reliability (0.740-0.881, P < 0.01) was also reported for DASS-21 and its three dimensions. The results of CFA showed acceptable model fit (χ2/(df) = 1457/(186), P < 0.001), root mean square error of approximation (RMSEA = 0.078), Tucker-Lewis index (TLI = 0.906), comparative fit index (CFI = 0.917), and standardized root mean square residual (SRMR = 0.047). Fifty-seven nurses were included in the test-retest. The ICCs for all dimensions ranged from 0.75 to 0.86, indicating the acceptable test-retest reliability of the scale.The Persian version of DASS-21 showed good psychometric characteristics, and it was confirmed as a valid and reliable tool for evaluating depression, anxiety, and stress among Iranian nurses. However, further validation studies of the Persian DSASS-21 are needed among other healthcare professionals, including physicians, midwives, and allied health professionals.

Psychometric Properties of the Chinese Version of the Functional Assessment of Self-Mutilation (FASM) in Chinese Clinical Adolescents

Abstract: Background Functional Assessment of Self-Mutilation (FASM) is one of the most widely used tools assessing adolescent's non-suicidal self-injury. However, the Chinese version of FASM (C-FASM) is lacking. The present study aimed to adapt the FASM to the Chinese patients and examine its reliability and validity. Methods The original English version of the FASM was translated into Chinese following Brislin's model of cross-culture translation, and then, pilot study and cognitive interview were carried out with 15 adolescent patients to assess the acceptability and comprehensibility of all items. The items were subsequently tested in a sample of 621 Chinese adolescent patients recruited by 20 psychiatric or general hospitals in nine provinces across China. We examined the distribution of responses for each item. Factor analysis, Cronbach's α and McDonald's Ω, intraclass coefficient, and Spearman's rank correlations were deployed to assess the dimensional structure, internal consistency reliability, test–retest reliability, and criterion validity. Results The final adapted C-FASM included a 10-item method checklist and a 15-item function checklist of NSSI, and other characteristics of NSSI. C-FASM exhibited acceptable internal consistency (α = 0.81 and Ω = 0.80 for method checklist; α = 0.80 and Ω = 0.76 for function checklist) and test–retest reliability (method checklist: 0.79; function checklist: 0.87). Factor analysis for NSSI functions yielded a three-factor model with a good model fit. In addition, the instrument showed an expected correlation with the instrument of the Deliberate Self-Harm Behavior Inventory (r = 0.84, p < 0.001). Conclusions The C-FASM has good content, structural validity, and reliability. The instrument can be helpful to Chinese adolescents as a comprehensive measure of NSSI behaviors.

The Autism Biomarkers Consortium for Clinical Trials: Initial Evaluation of a Battery of Candidate EEG Biomarkers.

Abstract: OBJECTIVE Numerous candidate EEG biomarkers have been put forward for use in clinical research on autism spectrum disorder (ASD), but biomarker development has been hindered by limited attention to the psychometric properties of derived variables, inconsistent results across small studies, and variable methodology. The authors evaluated the basic psychometric properties of a battery of EEG assays for their potential suitability as biomarkers in clinical trials. METHODS This was a large, multisite, naturalistic study in 6- to 11-year-old children who either had an ASD diagnosis (N=280) or were typically developing (N=119). The authors evaluated an EEG battery composed of well-studied assays of resting-state activity, face perception (faces task), biological motion perception, and visual evoked potentials (VEPs). Biomarker psychometrics were evaluated in terms of acquisition rates, construct performance, and 6-week stability. Preliminary evaluation of use was explored through group discrimination and phenotypic correlations. RESULTS Three assays (resting state, faces task, and VEP) show promise in terms of acquisition rates and construct performance. Six-week stability values in the ASD group were moderate (intraclass correlations ≥0.66) for the faces task latency of the P1 and N170, the VEP amplitude of N1 and P1, and resting alpha power. Group discrimination and phenotype correlations were primarily observed for the faces task P1 and N170. CONCLUSIONS In the context of a large-scale, rigorous evaluation of candidate EEG biomarkers for use in ASD clinical trials, neural response to faces emerged as a promising biomarker for continued evaluation. Resting-state activity and VEP yielded mixed results. The study's biological motion perception assay failed to display construct performance. The results provide information about EEG biomarker performance that is relevant for the next stage of biomarker development efforts focused on context of use.

Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson’s disease

Abstract: Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson's disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test-retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society-Unified Parkinson's Disease Rating Scale items (rho: 0.12-0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.

Automated Whole-Liver MRI Segmentation to Assess Steatosis and Iron Quantification in Chronic Liver Disease

Abstract: Background Standardized manual region of interest (ROI) sampling strategies for hepatic MRI steatosis and iron quantification are time consuming, with variable results. Purpose To evaluate the performance of automatic MRI whole-liver segmentation (WLS) for proton density fat fraction (PDFF) and iron estimation (transverse relaxometry [R2*]) versus manual ROI, with liver biopsy as the reference standard. Materials and Methods This prospective, cross-sectional, multicenter study recruited participants with chronic liver disease who underwent liver biopsy and chemical shift-encoded 3.0-T MRI between January 2017 and January 2021. Biopsy evaluation included histologic grading and digital pathology. MRI liver sampling strategies included manual ROI (two observers) and automatic whole-liver (deep learning algorithm) segmentation for PDFF- and R2*-derived measurements. Agreements between segmentation methods were measured using intraclass correlation coefficients (ICCs), and biases were evaluated using Bland-Altman analyses. Linear regression analyses were performed to determine the correlation between measurements and digital pathology. Results A total of 165 participants were included (mean age ± standard deviation, 55 years ± 12; 96 women; 101 of 165 participants [61%] with nonalcoholic fatty liver disease). Agreements between mean measurements were excellent, with ICCs of 0.98 for both PDFF and R2*. The median bias was 0.5% (interquartile range, -0.4% to 1.2%) for PDFF and 2.7 sec-1 (interquartile range, 0.2-5.3 sec-1) for R2* (P < .001 for both). Margins of error were lower for WLS than ROI-derived parameters (-0.03% for PDFF and -0.3 sec-1 for R2*). ROI and WLS showed similar performance for steatosis (ROI AUC, 0.96; WLS AUC, 0.97; P = .53) and iron overload (ROI AUC, 0.85; WLS AUC, 0.83; P = .09). Correlations with digital pathology were high (P < .001) between the fat ratio and PDFF (ROI r = 0.89; WLS r = 0.90) and moderate (P < .001) between the iron ratio and R2* (ROI r = 0.65; WLS r = 0.64). Conclusion Proton density fat fraction and transverse relaxometry measurements derived from MRI automatic whole-liver segmentation (WLS) were accurate for steatosis and iron grading in chronic liver disease and correlated with digital pathology. Automated WLS estimations were higher, with a lower margin of error than manual region of interest estimations. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Moura Cunha and Fowler in this issue.

ALIF Versus TLIF for L5-S1 Isthmic Spondylolisthesis: ALIF Demonstrates Superior Segmental and Regional Radiographic Outcomes and Clinical Improvements Across More Patient-reported Outcome Measures Domains

Abstract: Retrospective cohort study.The purpose of this study was to compare segmental and regional radiographic parameters between anterior interbody fusion (ALIF) and posterior interbody fusion (TLIF) for treatment of L5-S1 isthmic spondylolisthesis, and to assess for changes in these parameters over time. Secondarily, we sought to compare clinical outcomes via patient-reported outcome measures (PROMs) between techniques and within groups over time.Isthmic spondylolistheses are frequently treated with interbody fusion via ALIF or TLIF approaches. Robust comparisons of radiographic and clinical outcomes are lacking.We reviewed pre- and postoperative radiographs as well as Patient-Reported Outcomes Measurement Information System (PROMIS) elements for patients who received L5-S1 interbody fusions for isthmic spondylolisthesis in the Mass General Brigham (MGB) health system (2016-2020). Intraclass correlation testing was used for reliability assessments; Mann-Whitney U tests and Sign tests were employed for intercohort and intracohort comparative analyses, respectively.ALIFs generated greater segmental and L4-S1 lordosis than TLIF, both at first postoperative visit (mean 26 days [SE = 4]; 11.3° vs. 1.3°, P < 0.001; 6.2° vs. 0.3°, P = 0.005) and at final follow-up (mean 410days [SE = 45]; 9.6° vs. 0.2°, P < 0.001; 7.9° vs. 2.1°, P = 0.005). ALIF also demonstrated greater increase in disc height than TLIF at first (9.6 vs. 5.5 mm, P < 0.001) and final follow-up (8.7 vs. 3.6 mm, P < 0.001). Disc height was maintained in the ALIF group but decreased over time in the TLIF cohort (ALIF 9.6 vs. 8.7 mm, P = 0.1; TLIF 5.5 vs. 3.6 mm, P < 0.001). Both groups demonstrated improvements in Pain Intensity and Pain Interference scores; ALIF patients also improved in Physical Function and Global Health - Physical domains.ALIF generates greater segmental lordosis, regional lordosis, and restoration of disc height compared to TLIF for treatment of isthmic spondylolisthesis. Additionally, ALIF patients demonstrate significant improvements across more PROMs domains relative to TLIF patients.Level of Evidence: 3.

Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network

Abstract: The Cobb angle measurement of the scoliotic spine is prone to inter- and intra-observer variations in the clinical setting. This paper proposes a deep learning architecture for detecting spine vertebrae from X-ray images to evaluate the Cobb angle automatically. The public AASCE MICCAI 2019 anterior-posterior X-ray image dataset and local images were used to train and test the proposed convolutional neural network architecture. Sixty-eight landmark features of the spine were detected from the input image to obtain seventeen vertebrae on the spine. The vertebrae locations obtained were processed to automatically measure the Cobb angle. The proposed method can measure the Cobb angle with accuracies up to 93.6% and has excellent reliability compared to clinicians’ measurement (intraclass correlation coefficient > 0.95). The proposed deep learning architecture may be used as a tool to augment Cobb angle measurement in X-ray images of patients with adolescent idiopathic scoliosis in a real-world clinical setting.

Reliability of the NIH toolbox cognitive battery in children and adolescents: a 3-year longitudinal examination.

Abstract: The Cognitive Battery of the National Institutes of Health Toolbox (NIH-TB) is a collection of assessments that have been adapted and normed for administration across the lifespan and is increasingly used in large-scale population-level research. However, despite increasing adoption in longitudinal investigations of neurocognitive development, and growing recommendations that the Toolbox be used in clinical applications, little is known about the long-term temporal stability of the NIH-TB, particularly in youth.The present study examined the long-term temporal reliability of the NIH-TB in a large cohort of youth (9-15 years-old) recruited across two data collection sites. Participants were invited to complete testing annually for 3 years.Reliability was generally low-to-moderate, with intraclass correlation coefficients ranging between 0.31 and 0.76 for the full sample. There were multiple significant differences between sites, with one site generally exhibiting stronger temporal stability than the other.Reliability of the NIH-TB Cognitive Battery was lower than expected given early work examining shorter test-retest intervals. Moreover, there were very few instances of tests meeting stability requirements for use in research; none of the tests exhibited adequate reliability for use in clinical applications. Reliability is paramount to establishing the validity of the tool, thus the constructs assessed by the NIH-TB may vary over time in youth. We recommend further refinement of the NIH-TB Cognitive Battery and its norming procedures for children before further adoption as a neuropsychological assessment. We also urge researchers who have already employed the NIH-TB in their studies to interpret their results with caution.

Reliability and Validity of an Inertial Measurement System to Quantify Lower Extremity Joint Angle in Functional Movements

Abstract: The purpose of this research was to determine if the commercially available Perception Neuron motion capture system was valid and reliable in clinically relevant lower limb functional tasks. Twenty healthy participants performed two sessions on different days: gait, squat, single-leg squat, side lunge, forward lunge, and counter-movement jump. Seven IMUs and an OptiTrack system were used to record the three-dimensional joint kinematics of the lower extremity. To evaluate the performance, the multiple correlation coefficient (CMC) and the root mean square error (RMSE) of the waveforms as well as the difference and intraclass correlation coefficient (ICC) of discrete parameters were calculated. In all tasks, the CMC revealed fair to excellent waveform similarity (0.47–0.99) and the RMSE was between 3.57° and 13.14°. The difference between discrete parameters was lower than 14.54°. The repeatability analysis of waveforms showed that the CMC was between 0.54 and 0.95 and the RMSE was less than 5° in the frontal and transverse planes. The ICC of all joint angles in the IMU was general to excellent (0.57–1). Our findings showed that the IMU system might be utilized to evaluate lower extremity 3D joint kinematics in functional motions.

Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson’s disease

Abstract: Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson's disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test-retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society-Unified Parkinson's Disease Rating Scale items (rho: 0.12-0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.

Stability and reproducibility of proteomic profiles in epidemiological studies: comparing the Olink and SOMAscan platforms

Abstract: Limited data exist on the performance of high‐throughput proteomics profiling in epidemiological settings, including the impact of specimen collection and within‐person variability over time. Thus, the Olink (972 proteins) and SOMAscan7Kv4.1 (7322 proteoforms of 6596 proteins) assays were utilized to measure protein concentrations in archived plasma samples from the Nurses’ Health Studies and Health Professionals Follow‐Up Study. Spearman's correlation coefficients (r) and intraclass correlation coefficients (ICCs) were used to assess agreement between (1) 42 triplicate samples processed immediately, 24‐h or 48‐h after blood collection from 14 participants; and (2) 80 plasma samples from 40 participants collected 1‐year apart. When comparing samples processed immediately, 24‐h, and 48‐h later, 55% of assays had an ICC/r ≥ 0.75 and 87% had an ICC/r ≥ 0.40 in Olink compared to 44% with an ICC/r ≥ 0.75 and 72% with an ICC/r ≥ 0.40 in SOMAscan7K. For both platforms, >90% of the assays were stable (ICC/r ≥ 0.40) in samples collected 1‐year apart. Among 817 proteins measured with both platforms, Spearman's correlations were high (r > 0.75) for 14.7% and poor (r < 0.40) for 44.8% of proteins. High‐throughput proteomics profiling demonstrated reproducibility in archived plasma samples and stability after delayed processing in epidemiological studies, yet correlations between proteins measured with the Olink and SOMAscan7K platforms were highly variable.

Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett’s analysis: can we trust a bot to do that?

Abstract: Objective: To assess the reliability of CEFBOT, an artificial intelligence (AI)-based cephalometry software, for cephalometric landmark annotation and linear and angular measurements according to Arnett’s analysis. Methods: Thirty lateral cephalometric radiographs acquired with a Carestream CS 9000 3D unit (Carestream Health Inc., Rochester/NY) were used in this study. The 66 landmarks and the 10 selected linear and angular measurements of Arnett’s analysis were identified on each radiograph by a trained human examiner (control) and by CEFBOT (RadioMemory Ltd., Belo Horizonte, Brazil). For both methods, landmark annotations and measurements were duplicated with an interval of 15 days between measurements and the intraclass correlation coefficient (ICC) was calculated to determine reliability. The numerical values obtained with the two methods were compared by a t-test for independent variables. Results: CEFBOT was able to perform all but one of the 10 measurements. ICC values > 0.94 were found for the remaining eight measurements, while the Frankfurt horizontal plane - true horizontal line (THL) angular measurement showed the lowest reproducibility (human, ICC = 0.876; CEFBOT, ICC = 0.768). Measurements performed by the human examiner and by CEFBOT were not statistically different. Conclusion: Within the limitations of our methodology, we concluded that the AI contained in the CEFBOT software can be considered a promising tool for enhancing the capacities of human radiologists.