Lin's Concordance Correlation Research Articles

Validation of Vetscan Imagyst®, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.

Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology. The primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples (n = 108) for ova from two parasites that commonly infect horses, strongyles and Parascaris spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO3 and Sheather's sugar solution. Diagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO3 solution and 100.0% for samples prepared with Sheather's sugar solution. Sensitivity for Parascaris spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Lin's concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924-0.978 for strongyles and 0.944-0.955 for Parascaris spp., depending on the flotation solution. Sensitivity and specificity results for detecting strongyles and Parascaris spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings.

A standard area diagram for Fusarium yellows rating in sugar beet (Beta vulgaris)

AbstractMembers of the Fusarium oxysporum species complex are pathogens of sugar beet causing Fusarium yellows. Fusarium yellows can reduce plant stand, yield and extractable sugar. Improving host plant resistance against Fusarium‐induced diseases, like Fusarium yellows, represents an important long‐term breeding target in sugar beet breeding programmes. Current methods for rating Fusarium yellows disease severity rely on an ordinal scale, which limits precision for intermediate phenotypes. In this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD). Two SAD versions were created using images of sugar beets infected with Fusarium oxysporum strain F19. Each version was tested using inexperienced raters. Version 1 and the improved version 2 SAD showed no statistical differences in Lin's concordance correlation coefficient (LCC) values, which was used to assess accuracy and precision between the two versions (Cb = 0.99 for both versions, c = 0.97 and 0.96 for version 1 and 2, respectively). In addition, five naïve Bayesian machine‐learning models that used pixel classification to determine disease score were tested for congruency to human estimates in SAD version 2. Root mean square error was lowest compared to the ‘true’ values for the unweighted model and a model where necrotic tissue was given a 2× weight (12.4 and 12.6, respectively). The creation of this SAD enables breeding programmes to make consistent, accurate disease ratings regardless of personnel's previous experience with Fusarium yellows. Additionally, more iterations of pixel quantification equations may overcome accuracy issues for rating Fusarium yellows.

Monitoring cattle liveweight using a mobile, in-paddock weigh platform: Validation, attendance and utility

Cattle liveweight (LW) monitoring is essential for the effective management of animal productivity and welfare, particularly in decision-making on farms. Traditional static weigh (SW) systems require animals to be moved to fixed scales, posing challenges in extensive beef systems due to labour demands, costs, stress, and weight loss during muster. This study evaluated the relationship between LW measured by a SW system and a mobile in-field weighing system, Optiweigh (OW), in 65 weaners (Angus, Shorthorn, and Angus-Shorthorn cross) grazing forage oats on a commercial beef property in north-west NSW, Australia. Over 22 weeks, cattle were weighed fortnightly using SW scales in the cattle yards while OW continuously monitored LW in the paddock. Lin's concordance correlation coefficient showed a strong association between OW and SW liveweight (CCC = 0.97; P < 0.001), with no influence of breed or sex. However, OW slightly over-predicted LW for lighter cattle (≤ 382 kg) and under-predicted for heavier cattle (> 382 kg), prompting the development of a correction. Further research is needed to understand the reasons for these discrepancies, potentially related to diet. Additionally, cattle attendance at OW was affected by size, season, and individual variation (P < 0.001). Overall, the OW system simply and accurately monitored the temporal changes in cattle LW through voluntary animal attendance in remote systems.

Opportunistic Bone Mineral Density Measurement Using Photon-Counting Detector CT Spectral Localizer Images: A Prospective Study.

Background: Spectral localizer images from photon-counting detector (PCD) CT can be used for bone mineral density (BMD) evaluation given their 2D-projectional nature and material decomposition capability. As all CT examinations include localizer images, this approach could allow opportunistic osteoporosis screening in patients undergoing clinically indicated imaging by PCD CT. Objective: To assess the utility of PCD-CT spectral localizer images for opportunistic derivation of area BMD (aBMD) values and T-scores, using dual-energy X-ray absorptiometry (DXA) as the reference standard. Methods: This prospective study included patients ≥18 years old scheduled for clinically indicated lumbar spine CT between October 2023 and February 2024 and who underwent DXA within the 13 prior months or were scheduled for DXA within the subsequent 13 months. Participants underwent lumbar spine CT by PCD CT including spectral localizer images. Lumbar spine aBMD was extracted from clinical DXA reports. ROIs were placed on lumbar vertebral bodies and background soft tissues to extract areal densities from spectral localizer images using material decomposition; areal densities were used to derive lumbar spine aBMD values. The aBMD values were used to derive T-scores, which were classified as representing normal (≥-1) or abnormal (<-1) bone mass. DXA-derived and PCD-CT derived measurements were compared. Results: The study included 51 participants (mean age: 62 years [range, 28-83 years]; 31 female, 20 male). Mean DXA-derived T-score was 0.39±1.64; mean PCD-CT derived T-score was 0.28±1.77 (p=.29). Lin's concordance correlation coefficient between DXA-derived and PCD-CT T-scores was 0.90. The difference between DXA-derived and PCD-CT derived T-scores showed a small correlation with patient age (r=-0.13), absolute interval between DXA and PCD CT (r=.15), and BMI (r=0.28); this difference in scores did not show a significant difference between male and female patients (0.08 vs 0.13, respectively; p=.81). PCD-CT T-scores had sensitivity of 97%, specificity of 71%, PPV of 90%, and NPV of 91% for detecting abnormal bone mass using DXA-derived T-scores as the reference standard. Conclusion: PCD-CT spectral localizers showed clinical utility for deriving aBMD values and, consequently, T-scores. Clinical Impact: The T-score derived from PCD-CT spectral localizers may serve as an opportunistic screening tool for low bone mass and osteoporosis.

Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020

It remains unclear what changes have occurred in the distribution pattern of and trend in soil organic carbon (SOC) in China against the background of climate and land use change. Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R2) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources.

Tooth segmentation by low-dose CBCT for orthodontic treatment planning : Explorative ex vivo validation.

Three-dimensional imaging has become an increasingly important component of orthodontics. Associated with this, however, is ahigher radiation exposure for patients. New cone-beam computed tomography (CBCT) devices have been developed that can provide low-dose CBCT(LD-CBCT). We hypothesized that LD-CBCT is as precise and reproducible as standard high-dose CBCT (HD-CBCT) in segmenting roots and crowns as well as measuring tooth length. HD-CBCT and LD-CBCT scans were taken of four human cadaveric heads. Thirty single-rooted teeth were segmented twice by one investigator. The length of each tooth was also measured. Lin's concordance correlation coefficient (CCC) was calculated to assess the agreement of HD-CBCT and LD-CBCT measurements and the intraclass correlation coefficient (ICC) was calculated to assess intrarater reliability. Analyses were supported by Bland-Altman plots. Volume measurements obtained using HD-CBCT were significantly higher than those obtained using LD-CBCT (p < 0.001). CCC was 0.975 (95% confidence interval [CI] = 0.956-0.986) indicating excellent agreement between the two modalities. Intrarater reliability between the two sets of LD-CBCT and HD-CBCT volume measurements was excellent (ICC = 0.998, 95%CI = 0.995-0.999 [HD-CBCT], ICC = 0.997, 95%CI = 0.992-0.998 [LD-CBCT]). CCC for tooth length measurements was 0.991 (95% CI = 0.983-0.995), indicating excellent agreement between HD-CBCT and LD-CBCT. Intrarater reliabilities between the two sets of tooth length measurements were also excellent for both methods (ICC = 0.998, 95%CI = 0.995-0.999 [HD-CBCT], ICC = 0.997, 95%CI = 0.992-0.998 [LD-CBCT]). Within the limitations of this experimental setting, LD-CBCT is as valid as HD-CBCT for measuring tooth length. Regarding the volume differences, in vivo studies are required to determine their clinical relevance.

Impact of Photon-counting Detector Computed Tomography on a Quantitative Interstitial Lung Disease Machine Learning Model.

Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model. A QML model analyzed 52 CT exams from patients who underwent same-day conventional and PCD-CT for suspected ILD. Lin's concordance correlation coefficient (CCC) assessed agreement between conventional and PCD-CT QML results. A CCC >0.90 was regarded as excellent, 0.9 to 0.8 as good, and <0.80 as a poor concordance. Spearman rank correlation evaluated the association between pulmonary function test results (PFT) and QML features (reticulation [R], honeycombing [HC], ground glass [GG], interstitial lung disease [ILD], and vessel-related structures [VRS]). Correlations were statistically significant if the 95% CI did not include 0.00 and P value <0.05. Conventional and PCD-CT QML results had good to excellent concordance (CCC ≥0.8) except for total HC (CCC <0.8), likely related to better PCD-CT honeycombing delineation. Overall, compared with conventional CT, PCD-CT had consistently more statistically significant correlation with PFT for HC (9 PCD vs. 2 conventional of 28 total and regional associations), similar correlation for R (20 PCD vs. 18 conventional of 28 associations) and VRS (19 PCD vs. 23 conventional of 28 associations), and less correlation for GG extent (12 PCD vs. 20 conventional associations). There is strong agreement between conventional and PCD-CT QML ILD features except for HC. PCD-CT improved HC but decreased GG extent correlation with PFT. Therefore, even though most quantitative features were not impacted by the newer PCD-CT technology, model adjustment is necessary.

Comparison of Micro-CT and Morphometric Outcomes in a Modified Experimental Rat Model of Periodontitis.

To assess the correlation between micro-computed tomography (micro-CT) and linear morphometric measurements in terms of mandibular bone levels in a modified experimental periodontitis model in rodents to study the mechanisms of association between periodontal destruction and neuroinflammation. The proposed invivo experimental periodontitis model involves the administration of oral rinses with Porphyromonas gingivalis and Fusobacterium nucleatum, four times per week during 4, 8 or 12 weeks, in 24 male Wistar Hannover rats (180 g, 5 weeks old). After euthanasia, hemi-mandibles were collected. One hemi-mandible was analysed using morphometry, while the other was assessed with micro-CT. Linear measurements were taken at the buccal aspect and furcation level for both techniques, and volumetric measurements were also obtained with micro-CT. Passing-Bablok regression analysis was used to compare the results of both techniques, with morphometric measurements serving as the reference. Moreover, Lin's Concordance correlation coefficient was calculated to assess the level of agreement. Periodontal clinical variables with neuroinflammatory parameters from the frontal cortex were used to evaluate the association between the resulting condition and neuroinflammation. Twenty-one out of the initial 24 rats were analysed. The micro-CT linear measurements demonstrated high concordance values with the linear morphometric measurements at the buccal surfaces of the roots in molars (r = 0.714) but not at the furcation area (r = 0.052). At 12 weeks, there was a significant impact on neuroinflammation with significant decreases in iNOS levels and p-mTOR levels at 4 and 8 weeks. The proposed invivo experimental periodontitis model demonstrated a high degree of correlation between morphometric and micro-CT measurements in buccal areas but not at the furcation level. Concomitantly, there was a significant temporary modulation of the neuroinflammatory response.

Validation of a Remote Sampling Sensor for Measuring Urine Volume and Nitrogen Concentration in Grazing Dairy Cattle.

The purpose of this research was to validate a urine sensor (Lincoln University PEETER V2.0, Canterbury, New Zealand) that records the time and volume of urination events for dairy cows in addition to collecting a proportional urine sample from all urination events. Sixteen multiparous Holstein × Jersey mid-lactating cows (101 ± 5 days in milk, 498 ± 24.2 kg body weight, 26.2 ± 3.07 kg/d milk yield; mean ± standard deviation) were allocated herbage diets ranging in protein and sodium content to generate a range of urine volumes and urine nitrogen (UN) concentrations. Total collection of individual urination events occurred during a 72-h measurement period where PEETER V2.0 sensors were attached to cows. A mixed model ANOVA using lme4 package (version 1.1-35.5) in R (version 4.3.3) were used to compare the means. The average urine event size was 2.65 ± 1.1 L for total collection by observers and 2.68 ± 1.1 L as recorded by the sensor (mean ± standard deviation; p = 0.730). The urine nitrogen concentration was 5.76 ± 1.2 g N/L for samples collected by observers and 5.85 ± 1.3 g N/L for the samples collected by the sensor (p = 0.583). The calculated UN excretion was 156 ± 45.1 g/day for direct measurements and 162 ± 40.0 g/day for the sensor (p = 0.539. Contrasts with simultaneously measured data were undertaken using Lin's Concordance Correlation Coefficient (CCC) and a Pearson correlation coefficient (r). Correlations between the actual values and sensor values were strong, with little to moderate variability in the urine volume (CCC = 0.936, r = 0.937; n = 222), UN concentration (CCC = 0.840, r = 0.837, n = 48) and total UN excretion (CCC = 0.827, r = 0.836, n = 24). Considering the findings, the PEETER V2.0 urine sensor has the potential to reliably measure urine volumes and UN concentrations for estimations of the UN excretion of dairy cattle under grazing systems.

Validity and Reliability of a New Wearable Chest Strap to Estimate Respiratory Frequency in Elite Soccer Athletes.

Assessing respiratory frequency (fR) is practical in monitoring training progress in competitive athletes, especially during exercise. This study aimed to validate a new wearable chest strap (wCS) to estimate fR against ergospirometry as a criterion device in soccer players. A total of 26 elite professional soccer players (mean [standard deviation]: 23.6 [4.8] years; 180.6 [5.7] cm; 77.2 [5.4] kg) from three Italian Serie A League teams participated in this cross-sectional study. The sample included attackers, midfielders, and defenders. fR was assessed during a maximal cardiopulmonary exercise test (CPET) on a treadmill using (i) a breath-by-breath gas exchange analyzer (Vyntus® CPX, Vyaire Medical) and (ii) a novel wCS with sensors designed to assess breath frequency following chest expansions. Pearson's correlation coefficient (r), adjusted coefficient of determination (aR2), Bland-Altman plot analysis, and Lin's concordance correlation coefficient (ρc) were used for comparative analysis (correlation and concordance) among the methods. The repeated measures correlation coefficient (rrm) was used to assess the strength of the linear association between the methods. The intraclass correlation coefficient (ICC) and the Finn coefficient (rF) were used for inter-rater reliability. All statistical analyses were performed within the R statistical computing environment, with 95% confidence intervals (95% CIs) reported and statistical significance set at p < 0.05. A total of 16529 comparisons were performed after collecting the CPET data. The robust time series analysis with Hodges-Lehmann estimation showed no significant differences between both methods (p > 0.05). Correlation among devices was statistically significant and very large (r [95% CI]: 0.970 [0.970, 0.971], p < 0.01; aR2 [95% CI]: 0.942 [0.942, 0.943], p < 0.01) with strong evidence supporting consistency of the new wCS (BF10 > 100). In addition, a high concordance was found (ρc [95% CI]: 0.970 [0.969, 0.971], bias correction factor: 0.999). VyntusTM CPX, as a standard criterion, showed moderate agreement with wCS after Bland-Altman analysis (bias [95% lower to the upper limit of agreement]; % agree: 0.170 [-4.582 to 4.923] breaths·min-1; 69.9%). A strong association between measurements (rrm [95% CI]: 0.960 [0.959, 0.961]), a high absolute agreement between methods (ICC [95% CI]: 0.970 [0.970, 0.971]), and high inter-rater reliability (rF: 0.947) were found. With an RMSE = 2.42 breaths·min-1, the new wCS seems to be an valid and reliable in-field method to evaluate fR compared to a breath-by-breath gas exchange analyzer. Notwithstanding, caution is advised if methods are used interchangeably while further external validation occurs.

Validation of Preterm Birth Related Perinatal and Neonatal Data in the Canadian Discharge Abstract Database to Facilitate Long-term Outcomes Research of Individuals Born Preterm

IntroductionThe Canadian Institute of Health Information's (CIHI) Discharge Abstract Database (DAD) contains standardised administrative data on all hospitalisations in Canada, excluding Quebec. ObjectivesWe aimed to validate preterm birth related perinatal and neonatal data in DAD by assessing its accuracy against the reference standard of the Canadian Neonatal Network (CNN) database. MethodsWe linked birth hospitalization data between the DAD and CNN databases for all neonates born $&lt;$33 weeks gestational age (GA) admitted to the Neonatal Intensive Care Units in Winnipeg, Canada, between 2010 and 2022. A comprehensive list of maternal and neonatal variables relevant to preterm birth was chosen \textit{a priori} for validation. For categorical variables, we measured correlation using Cohen's weighted kappa (k) and for continuous variables, we measured agreement using Lin's concordance correlation coefficient (LCCC). Results2084 neonates were included (mean GA 29.4 ± 2.4 weeks; birth weight 1430 ± 461g). Baseline continuous maternal and neonatal variables showed excellent accuracy in DAD [Maternal age: LCCC = 0.99 (0.99, 0.99); GA: LCCC = 0.95 (0.95, 0.96); birth weight: LCCC = 0.97 (0.96, 0.97); sex: k = 0.99 (0.98-0.99)]. In contrast, the accuracy of the maternal baseline categorical variables and neonatal outcomes and interventions ranged from very good to poor [e.g., Caesarean section: k = 0.91 (0.89-0.93), pre-gestational diabetes: k = 0.04 (0.03-0.05), neonatal sepsis: k = 0.37 (0.31-0.42), bronchopulmonary dysplasia: k = 0.26 (0.19-0.33), neonatal laparotomy: k = 0.55 (0.43-067)]. ConclusionNeonatal variables such as gestational age and birth weight had high accuracy in DAD, while the accuracy of maternal and neonatal morbidities and interventions were variable, with some being poor. Reasons for the inaccuracy of these variables should be identified and measures taken to improve them.

Design and validity of an instrument - run cross hopping throw and catch (rcht&amp;c) test for assessing motor competence in athletics

The aim of this study was to design and validate a tool for assessing motor competence (MC) and detecting talent in children aged between 6 and 10 years in athletics. Ten experts were carefully selected to collaborate in the validation. Cronbach's alpha and intraclass correlation coefficient (ICC) were used, respectively, to check for construct's validity and to reliability. Lin's Concordance Correlation Coefficient (CCC) was calculated as a complementary test. Additionally, the Aiken's V value was used to validate the tool. ICC (0.855) and Cronbach's alpha (0.922) showed acceptable reliability and consistency, respectively, and Lin's CCC (0.786) indicated excellent reproducibility, thus proving stability and consistency over a two-week period. Aiken's V value was 0.92, confirming the validity of the test. By parameter, univocity had Aiken's V of 0.92, relevance of 0.91, and importance of 0.91. Therefore, we conclude that this tool can be a valid test for assessing MC in athletics. Keywords: Validation, Instrument, Motor Competence, Talent, Children and Athletics

Spatial variation of soil carbon, nitrogen, and phosphorus in the Caatinga dry forest

Soil nutrients are crucial for understanding the impact of global changes on terrestrial ecosystems. Carbon (C), nitrogen (N), and phosphorus (P) are required in specific quantities by plants and are related to soil fertility. In the Caatinga, one of the world’s largest and most diverse tropical dry forests, there have been limited studies investigating the factors that contribute to the spatial distribution of organic carbon (OC), N, and P in the soil and, even fewer, those that explored the use of Machine learning (ML) modeling. In this research, we aim to predict the spatial distribution of these properties at depths ranging from 0 to 20 cm in the Caatinga biome and assess the predictive capability of environmental and geographic variables. We used the Random Forest model in Google Earth Engine to forecast maps with a spatial resolution of 30 m. The best result was obtained for predicting P [Lin's concordance correlation coefficient (LCCC) of 0.32 and R2 of 0.25], followed by OC (LCCC of 0.25 and R2 of 0.17), N (LCCC of 0.21 and R2 of 0.12) and C/N ratio (LCCC of 0.14 and R2 of 0.10). The final maps exhibited a consistent spatial pattern, with OC, N, and C/N ratio showing correlation with climatic covariates (air and soil temperature in the first 15 cm deep), topographic data (altitude), and geographic regions (longitude and latitude). The variation in P content was primarily influenced by parent material. We highlight the relevance of ecotones, which recorded the highest average levels of C and N and C/N, demonstrating the importance of these areas for the maintenance and dynamics of these ecosystems.

Validity and Concordance of a Linear Position Transducer (Vitruve) for Measuring Movement Velocity during Resistance Training.

This study aimed to analyze the intra-device agreement of a new linear position transducer (Vitruve, VT) and the inter-device agreement with a previously validated linear velocity transducer (T-Force System, TF) in different range of velocities. A group of 50 healthy, physically active men performed a progressive loading test during a bench press (BP) and full-squat (SQ) exercise with a simultaneous recording of two VT and one TF devices. The mean propulsive velocity (MPV) and peak of velocity (PV) were recorded for subsequent analysis. A set of statistics was used to determine the degree of agreement (Intraclass correlation coefficient [ICC], Lin's concordance correlation coefficient [CCC], mean square deviation [MSD], and variance of the difference between measurements [VMD]) and the error magnitude (standard error of measurement [SEM], smallest detectable change [SDC], and maximum errors [ME]) between devices. The established velocity ranges were as follows: >1.20 m·s-1; 1.20-0.95 m·s-1; 0.95-0.70 m·s-1; 0.70-0.45 m·s-1; ≤0.45 m·s-1 for BP; and >1.50 m·s-1; 1.50-1.25 m·s-1; 1.25-1.00 m·s-1; 1.00-0.75 m·s-1; and ≤0.75 m·s-1 for SQ. For the MPV, the VT system showed high intra- and inter-device agreement and moderate error magnitude with pooled data in both exercises. However, the level of agreement decreased (ICC: 0.790-0.996; CCC: 0.663-0.992) and the error increased (ME: 2.8-13.4% 1RM; SEM: 0.035-0.01 m·s-1) as the velocity range increased. For the PV, the magnitude of error was very high in both exercises. In conclusion, our results suggest that the VT system should only be used at MPVs below 0.45 m·s-1 for BP and 0.75 m·s-1 for SQ in order to obtain an accurate and reliable measurement, preferably using the MPV variable instead of the PV. Therefore, it appears that the VT system may not be appropriate for objectively monitoring resistance training and assessing strength performance along the entire spectrum of load-velocity curve.

External Validation of SpineNetV2 on a Comprehensive Set of Radiological Features for Grading Lumbosacral Disc Pathologies

BackgroundIn recent years, the integration of Artificial Intelligence (AI) models has revolutionized the diagnosis of Low Back Pain (LBP) and associated disc pathologies. Among these, SpineNetV2 stands out as a state-of-the-art, open-access model for detecting and grading various intervertebral disc pathologies. However, ensuring the reliability and applicability of AI models like SpineNetV2 is paramount. Rigorous validation is essential to guarantee their robustness and generalizability across diverse patient cohorts and imaging protocols. MethodsWe conducted a retrospective analysis of MRI images of 1747 lumbosacral intervertebral discs (IVDs) from 353 patients (mean age, 54 ± 15.4 years, 44.5% female) with various spinal disorders, collected between September 2021 and February 2023 at X-Ray Service s.r.l. The SpineNetV2 system was used to grade 11 distinct lumbosacral disc pathologies, including Pfirrmann grading, disc narrowing, central canal stenosis, spondylolisthesis, (upper & lower) endplate defects, (upper & lower) marrow changes, (right & left) foraminal stenosis, and disc herniation, using T2-weighted sagittal MR images. Performance metrics included accuracy, balanced accuracy, precision, F1 score, Matthew's Correlation Coefficient, Brier Score Loss, Lin's concordance correlation coefficients, and Cohen's kappa coefficients. Two expert radiologists provide annotations for these discs. The evaluation of SpineNetV2′s grading is compared against expert radiologists' assessments. ResultsSpineNetV2 demonstrated strong performance across various metrics, with high agreement scores (Cohen's Kappa, Lin's Concordance, and Matthew's Correlation Coefficient exceeding 0.7) for most pathologies. However, lower agreement was found for foraminal stenosis and disc herniation, underscoring the limitations of sagittal MR images for evaluating these conditions. ConclusionsThis study highlights the importance of external validation, emphasizing the need for comprehensive assessments of deep learning models. SpineNetV2 exhibits promising results in predicting disc pathologies, with findings guiding further improvements. The open-source release of SpineNetV2 enables researchers to independently validate and extend the model's capabilities. This collaborative approach promotes innovation and accelerates the development of more reliable and comprehensive deep learning tools for the assessment of spine pathology.

Validity of Endometrial Cavity Length on 3D Pelvic Ultrasound before Endometrial Ablation.

This study aimed to compare endometrial cavity length measurements obtained by preoperative three-dimensional (3D) reconstruction of coronal images on pelvic ultrasound with intraoperative endometrial cavity measurements obtained in advance of Minerva endometrial ablation. This was a retrospective cohort study of 41 individuals who underwent a Minerva endometrial ablation between August 1, 2018 and March 15, 2022 at a single academic medical center. Patients were excluded if they had an in-clinic ablation or no ultrasound with 3D uterine volume within 180 days before surgery. Physician sonologists measured the endometrial cavity length using 3D coronal reconstruction of the cavity. Two measurements were obtained by separate physicians who were blinded to intraoperative values. Intraoperative endometrial cavity lengths were obtained from operative reports. A Bland-Altman plot was used to evaluate the agreement of intraoperative and ultrasound measurements. The mean intraoperative endometrial cavity length (50.7 ± 7.8 mm) was greater than the mean endometrial cavity length by 3D coronal reconstruction of pelvic ultrasound (36.1 ± 6.2 mm, P < 0.0001). The average difference between intraoperative and ultrasound measurements of cavity length was 14.6 ± 9.0 mm. The agreement between measurements was poor (Lin's concordance correlation coefficient 0.06). Using a Bland-Altman plot, the limits of agreement (-3.1 to 32 mm) exceeded the a priori acceptable limits of agreement (-10 to 10 mm). Our findings suggest that preoperative coronal endometrial cavity length measurements by ultrasound are not a valid substitute for intraoperative measurements before Minerva ablation.

Technical Development and In Silico Implementation of SyntheticMR in Head and Neck Adaptive Radiation Therapy: A Prospective R-IDEAL Stage 0/1 Technology Development Report.

The purpose of this study was to investigate the technical feasibility of integrating the quantitative maps available from SyntheticMR into the head and neck adaptive radiation oncology workflow. While SyntheticMR has been investigated for diagnostic applications, no studies have investigated its feasibility and potential for MR-Simulation or MR-Linac workflow. Demonstrating the feasibility of using this technique will facilitate rapid quantitative biomarker extraction which can be leveraged to guide adaptive radiation therapy decision making. Two phantoms, two healthy volunteers, and one patient were scanned using SyntheticMR on the MR-Simulation and MR-Linac devices with scan times between four to six minutes. Images in phantoms and volunteers were conducted in a test/retest protocol. The correlation between measured and reference quantitative T1, T2, and PD values were determined across clinical ranges in the phantom. Distortion was also studied. Contours of head and neck organs-at-risk (OAR) were drawn and applied to extract T1, T2, and PD. These values were plotted against each other, clusters were computed, and their separability significance was determined to evaluate SyntheticMR for differentiating tumor and normal tissue. The Lin's Concordance Correlation Coefficient between the measured and phantom reference values was above 0.98 for both the MR-Sim and MR-Linac. No significant levels of distortion were measured. The mean bias between the measured and phantom reference values across repeated scans was below 4% for T1, 7% for T2, and 4% for PD for both the MR-Sim and MR-Linac. For T1 vs. T2 and T1 vs. PD, the GTV contour exhibited perfect purity against neighboring OARs while being 0.7 for T2 vs. PD. All cluster significance levels between the GTV and the nearest OAR, the tongue, using the SigClust method was p < 0.001. The technical feasibility of SyntheticMR was confirmed. Application of this technique to the head and neck adaptive radiation therapy workflow can enrich the current quantitative biomarker landscape.

Reproducibility Of Retinal Nerve Fiber Layer And Macular Volume Measurements In Patients Receiving Retinal Protection Pharmacotherapy

Background — High-quality monitoring of the glaucoma plays an important role in the early diagnosis and treatment of glaucoma, for which structural optical coherence tomography (OCT) is used. Technology that increases the repeatability and reproducibility of OCT may differ in different devices, thereby yielding different results. Objective — To assess the effect of the frequency of retinal protection pharmacotherapy courses on quantitative data regarding the macular volume and the thickness of the retinal nerve fiber layer (RNFL) measured by two instruments, SPECTRALIS and OPTOPOL, and also to evaluate the agreement between the data collected by these devices. Material and Methods — We monitored 17 patients (34 eyes) diagnosed with primary open-angle glaucoma in the advanced stage from 2019 to 2023. Group 1 including 9 patients (18 eyes) received a course of retinal protection pharmacotherapy with Retinalamin® at an interval of 3 months, while Group 2 comprising 8 patients (16 eyes) received similar treatment at an interval of 6 months. All observed patients underwent structural OCT on REVO NX (OPTOPOL Technology) and SPECTRALIS devices at the onset of the study, as well as after 3 and 6 months. Results — The agreement between the two devices in measuring RNFL thickness was estimated by the Lin’s concordance correlation coefficient (LCCC): in manual mode, LCCCstart=0.69 (95% CI: 0.51-0.81); while in automatic mode, LCCCstart=0.73 (95% CI: 0.57-0.84). The agreement between the macular volume measurements was as follows: in manual mode, LCCCstart=0.21 (95% CI: 0.21-0.29); while in automatic mode, LCCCstart=0.21 (95% CI: 0.21-0.29). Conclusion — The agreement between the SPECTRALIS and OPTOPOL devices in terms of RNFL thickness should be considered substantial, while in terms of macular volume, it should be classified as fair. As measured in the manual mode, the RNFL thickness in both groups receiving Retinalamin increased, which can be interpreted as a favorable effect of retinal protection pharmacotherapy.

Model ensemble techniques of machine learning algorithms for soil moisture constants in the semi‐arid climate conditions

AbstractIn recent years, the use of prediction models based on intelligent algorithms has become widespread in soil science. However, each algorithm has advantages and disadvantages, and variable results can occur on different datasets. The evaluation of ensemble techniques for solving these problems is the current approach. Water problems will arise due to global warming, and soil water will become more important. This study aims to evaluate the predictive accuracy of different machine learning algorithms (support vector machine regression (SVR), random forest (RF), artificial neural network (ANN), and multivariate linear regression (MLR)) and ensemble techniques (equal weight [EQ], Bates–Granger‐BG), Granger–Ramanathan (GR), Akaike information criterion (AIC), and Bayesian information criterion (BIC)) on the field capacity (FC), wilting point (WP) and available water content (AWC) of soils. As a result, higher prediction accuracy was obtained with the RF algorithm than with the value machine learning algorithm in the estimation of moisture constants. The coefficients of determination (R2) obtained for the prediction of FC, WP, and AWC via the RF algorithms were 0.624, 0.759 and 0.641, respectively. MLR had the highest error rate. Among the ensemble techniques, GR was the most successful. Lin's concordance correlation coefficient (LCCC) values obtained from the estimation of FC, WP, and AWC with the GR model were 0.801, 0.894, and 0.801, respectively. The root mean squared error (RMSE) and mean absolute error (MAE) values obtained in the estimation of the available water content with the MLR algorithm were 1.905 and 1.435, respectively, whereas these values were 1.173 and 0.767, respectively, when the GR model was used. As a result of the present study, better predictive results were obtained with ensemble techniques instead of evaluating the algorithms individually.

A Comparative Evaluation of Point-of-Care and Laboratory HbA1c Testing in Diabetes Care: An Indian Perspective.

This study aimed to assess the diagnostic performance of a point-of-care (POC) glycated hemoglobin (HbA1c) device in the Indian population against the standard laboratory (high-performance liquid chromatography {HPLC}) method for the effective management of diabetes in India. This study on the diagnostic accuracy of a POC HbA1c device involved 121 participants. These participants were categorized into two groups according to their HbA1c levels - one group with HbA1c values below 6.5% and another group with HbA1c values equal to or greater than 6.5%. The HbA1c levels in enrolled participants were estimated using both the POC device (HemoCueHbA1c 501; Ängelholm, Sweden: HemoCue AB) and the standard HPLC-based method. The level of agreement and concordance between the two test results were assessed by the Bland-Altman plot and Lin's concordance correlation coefficient. Sensitivity, specificity, diagnostic accuracy, positive likelihood, and negative likelihood ratios of POC-HbA1c device were assessed with a 6.5% HbA1c cut-off. The mean HbA1c values obtained by the two methods showed no statistically significant difference with a minimum effect size (Cohen's d = 0.035), indicating there was a negligible difference between these methods. The Bland-Altman plot revealed that most values were within acceptable limits (95% CI: -0.5 to 0.7) and Lin's concordance correlation coefficient showed strong agreement (p < 0.0001).The POC-HbA1c device demonstrated an area under the curve (AUC) of 0.991 (95% CI: 0.953-1.000) with sensitivity, specificity, diagnostic accuracy, positive likelihood, and negative likelihood ratios of 93.62%, 97.30%, 95.87%, 34.64 and 0.07, respectively, compared to the standard diagnostic assay. The diagnostic accuracy, sensitivity, and specificity demonstrated by the POC-HbA1c device to the standard HPLC method offers a viable and practical solution for diabetes management in India. Its ability to provide rapid and reliable results at the point of care can improve patient outcomes, reduce healthcare costs, and enhance access to diabetes care, especially in primary care, remote areas, and resource-limited settings of developing countries like India.