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Objectives: To develop a semi‐automated tool for measuring fetal abdominal wall thickness (AWT). To validate the software using images captured by other centers and create a nomogram for fetal AWT between 18 and 20 weeks. Methods: A semiautomated tool that measured AWT was developed using images captured at the routine 20‐week morphology scan. The software was developed using digital images captured routinely during scans of low‐risk women. Inter‐ and intraobserver reliability was assessed between manual and semi‐automated measures. The tool was validated using images acquired from other centers. Linear regression and quadratic polynomials were used to create a nomogram for AWT. Results: The semi‐automated tool was able to measure AWT in all images. Interoperator reliability was 0.90 and 0.97 (P < 0.05) for manual and semi‐automated methods, respectively. Measurement agreement varied between three operators from moderate to excellent (0.77, 0.87, 0.92), with overall agreement being good (0.85). The tool could be successfully applied to 89% of images from other centers. A nomogram was generated for AWT measurements of fetuses at 18–20 weeks in normal, low risk mothers. Conclusion: Semi‐automated measurement of AWT was feasible using images captured during the routine 20‐week scan. This approach had lower inter‐ and intraobserver variability compared to manual measurement. Synopsis: A semiautomated measurement of abdominal wall thickness (AWT) is being reported to measure fetal adiposity. This would have clinical application in gestational diabetes and fetal growth restriction. [ABSTRACT FROM AUTHOR]

This research letter published in the Clinical & Experimental Allergy journal discusses the evaluation of a new automated allergy intradermal skin test reader called Nexkin DSPT. The study compares the accuracy and reliability of Nexkin DSPT with manual measurement techniques in determining wheal size and area in skin prick tests. The results suggest that Nexkin DSPT has the potential to be a useful diagnostic tool for allergies, but further confirmation is needed. The study provides data and analysis on the reliability and effectiveness of the Nexkin DSPT device for measuring wheal size in allergy skin testing. It concludes that the device has the potential to improve the precision and consistency of allergy testing procedures. [Extracted from the article]

Breast cancer is the most common cancer in women and a leading cause of mortality. This systematic review and meta-analysis aims to evaluate the correlation between breast density measurements obtained from various software and visual assessments by radiologists using full-field digital mammography (FFDM). Following the PRISMA 2020 guidelines, five databases (Pubmed, Google Scholar, Science Direct, Cochrane Library, and MEDLINE) were searched for studies correlating volumetric breast density with breast cancer risk. The Newcastle–Ottawa Scale and the Joanna Briggs Institute Checklist were used to assess the quality of the included studies. Meta-analysis of correlation was applied to aggregate correlation coefficients using a random-effects model using MedCalc Statistical Software version 19.2.6. The review included 22 studies with a total of 58,491 women. The pooled correlation coefficient for volumetric breast density amongst Volpara™ and Quantra™ was found to be 0.755 (95% CI 0.496–0.890, p < 0.001), indicating a high positive correlation, albeit with a significant heterogeneity (I2 = 99.89%, p < 0.0001). Subgroup analyses based on study origin, quality, and methodology were performed but did not reveal the heterogeneity cause. Egger's and Begg's tests showed no significant publication bias. Volumetric breast density is strongly correlated with breast cancer risk, underscoring the importance of accurate breast density assessment in screening programs. Automated volumetric measurement tools like Volpara™ and Quantra™ provide reliable assessments, potentially improving breast cancer risk prediction and management. Implementing fully automated breast density assessment tools could enhance consistency in clinical practice, minimizing observer variability and improving screening accuracy. These tools should be further validated against standardized criteria to ensure reliability in diverse clinical settings. [ABSTRACT FROM AUTHOR]

PremiseThe agar-based culture of Arabidopsis seedlings is widely used for quantifying root traits. Shoot traits are generally overlooked in these studies, probably because the rosettes are often askew. A technique to assess the shoot surface area of seedlings grown inside agar culture dishes would facilitate simultaneous root and shoot phenotyping.MethodsWe developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes. We validated this method by comparing its output with other metrics of seedling growth. As part of a larger study on genetic variation in plant responses to nitrogen form and concentration, we measured the rosette areas from more than 2000 plate images.ResultsThe rosette area measured from plate images was strongly correlated with the rosette area measured from directly overhead and moderately correlated with seedling mass. Rosette area in the large image set was significantly influenced by genotype and nitrogen treatment. The broad-sense heritability of leaf area measured using this method was 0.28.DiscussionThese results indicated that this approach for estimating rosette area produces accurate shoot phenotype data. It can be used with image sets for which other methods of leaf area quantification prove unsuitable.

Harmful Algal and Cyanobacterial Bloom (HACB) threaten aquatic ecosystems, human health, and the economy. Many factors influence these dynamic events, which are often difficult to detect until the late stages of growth. The inclusion of an Early Warning System (EWS) can be instrumental in identifying hazards and preventing or at least minimizing their impact. Traditional monitoring approaches often fail to provide the real-time, high-resolution data needed for effective early warnings. The integration of Internet of Things (IoT) technologies offers a promising avenue to address these limitations by creating a network of interconnected sensors capable of continuously collecting and transmitting data from various aquatic environments. In this paper, we present DEVS-BLOOM-WebUI, an advanced web application that extends the capabilities of the DEVS-BLOOM framework, providing a user-friendly interface that supports different user roles. The application includes an interface to manage users and permissions, dashboards to inspect data (from sensors, Unmanned Surface Vehicles (USVs), weather stations, etc.), train AI models, explore their predictions, and facilitate decision-making through notification of early warnings. A key feature of DEVS-BLOOM-WebUI is the Scenario Configuration Editor (SCE). This interactive tool allows for users to design and configure the deployment of monitoring infrastructure within a water body, enhancing the system's adaptability to user-defined simulation scenarios. This paper also investigates the practical implementation of an IoT-based EWS, discussing design considerations, sensor technologies, and communication protocols essential for seamless data integration and effective operation of the EWS. [ABSTRACT FROM AUTHOR]

From a field-management perspective, monitoring flower buds and fruits in blueberry cultivation is essential. However, blueberry cultivation in Japan is mainly small-scale and manual, which hinders real-time monitoring of the entire field. Here, with the ultimate aim of automating monitoring of blueberry fields, we examined the feasibility of estimating the number of flower buds and fruits per bush by using object detection through deep learning, specifically employing YOLOv5 and YOLOv8 models. First, we detected and classified normal and frost-damaged flower buds and unripe and ripe fruits at various resolutions using a DSLR camera. Then, we estimated the numbers of flower buds and fruits in the images by using a simple regression formula. The results showed high accuracy of the automatic counts of normal buds, ripe fruits, and unripe fruits in the images but low accuracy for frost-damaged buds. For flower-bud detection, the highest average precision (AP) index was 0.755 and the lowest regression error index mean absolute percentage error (MAPE) was 10.79%. For fruit detection, the highest AP was 0.815 and the lowest MAPE was 23.84%. Furthermore, we tested the possibility of estimating the total number of fruits, including those hidden behind leaves, from the number of fruits visible in the images. We found a positive correlation between the number of fruits in the images and the total number of fruits (r=0.96, p<0.001), with a simple regression error MAPE of 21.2%. Also, the degree of leaf coverage markedly affected the estimation error. However, we found that the imaging direction was inconsequential and image collection from both sides of the row was unnecessary. Finally, we examined potential field applications of this automatic flower bud and fruit counting and classification technology, such as early yield prediction (during the flower bud phase), understanding the spatial variation of pollination effects by bees, and determining the optimum harvest time and harvest sequence. [ABSTRACT FROM AUTHOR]

Study Design: Comparative study Objective: To compare manual and deep learning-based automated measurement of Cobb angle in adolescent idiopathic scoliosis. Methods: We proposed a fully automated framework to measure the Cobb angle of AIS patients. Whole-spine images of 500 AIS individuals were collected. 200 digital radiographic (DR) images were labeled manually as training set, and the remaining 300 images were used to validate by mean absolute error (MAE), Pearson or spearman correlation coefficients, and intra/interclass correlation coefficients (ICCs). The relationship between accuracy of vertebral boundary identification and the subjective image quality score was evaluated. Results: The PT, MT, and TL/L Cobb angles were measured by the automated framework within 300 milliseconds. Remarkable 2.92° MAE,.967 ICC, and high correlation coefficient (r =.972) were obtained for the major curve. The MAEs of PT, MT, and TL/L were 3.04°, 2.72°, and 2.53°, respectively. The ICCs of these 3 curves were.936,.977, and.964, respectively. 88.7% (266/300) of cases had a difference range of ±5°, with 84.3% (253/300) for PT, 89.7% (269/300) for MT, and 93.0% (279/300) for TL/L. The decreased bone/soft tissue contrast (2.94 vs 3.26; P=.039) and bone sharpness (2.97 vs 3.35; P=.029) were identified in the images with MAE exceeding 5°. Conclusion: The fully automated framework not only identifies the vertebral boundaries, vertebral sequences, the upper/lower end vertebras and apical vertebra, but also calculates the Cobb angle of PT, MT, and TL/L curves sequentially. The framework would shed new light on the assessment of AIS curvature. [ABSTRACT FROM AUTHOR]

Objective: Validation of automated 2‐dimensional (2D) diameter measurements of vestibular schwannomas on magnetic resonance imaging (MRI). Study Design: Retrospective validation study using 2 data sets containing MRIs of vestibular schwannoma patients. Setting: University Hospital in The Netherlands. Methods: Two data sets were used, 1 containing 1 scan per patient (n = 134) and the other containing at least 3 consecutive MRIs of 51 patients, all with contrast‐enhanced T1 or high‐resolution T2 sequences. 2D measurements of the maximal extrameatal diameters in the axial plane were automatically derived from a 3D‐convolutional neural network compared to manual measurements by 2 human observers. Intra‐ and interobserver variabilities were calculated using the intraclass correlation coefficient (ICC), agreement on tumor progression using Cohen's kappa. Results: The human intra‐ and interobserver variability showed a high correlation (ICC: 0.98‐0.99) and limits of agreement of 1.7 to 2.1 mm. Comparing the automated to human measurements resulted in ICC of 0.98 (95% confidence interval [CI]: 0.974; 0.987) and 0.97 (95% CI: 0.968; 0.984), with limits of agreement of 2.2 and 2.1 mm for diameters parallel and perpendicular to the posterior side of the temporal bone, respectively. There was satisfactory agreement on tumor progression between automated measurements and human observers (Cohen's κ = 0.77), better than the agreement between the human observers (Cohen's κ = 0.74). Conclusion: Automated 2D diameter measurements and growth detection of vestibular schwannomas are at least as accurate as human 2D measurements. In clinical practice, measurements of the maximal extrameatal tumor (2D) diameters of vestibular schwannomas provide important complementary information to total tumor volume (3D) measurements. Combining both in an automated measurement algorithm facilitates clinical adoption. [ABSTRACT FROM AUTHOR]

The general-purpose powder diffractometer beamline (BL2-1) at the Stanford Synchrotron Radiation Lightsource (SSRL) is described. The evolution of design and performance of BL2-1 are presented, in addition to current operating specifications, applications and measurement capabilities. Recent developments involve a robotic sample changer enabling high-throughput X-ray diffraction measurements, applicable to mail-in and remote operations. In situ and operando capabilities to measure samples with different form factors (e.g. capillary, flat plate or thin film, and transmission) and under variable experimental conditions are discussed. Several example datasets and accompanying Rietveld refinements are presented. [ABSTRACT FROM AUTHOR]

Regular inspections during construction work ensure that the completed work aligns with the plans and specifications and that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision techniques for measuring as-built elements predominantly employ three-dimensional laser scanning or three-dimensional photogrammetry modeling to ascertain the geometric properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element's coordinates, making the deployment of these techniques on dynamic construction project sites challenging. This paper proposes a pipeline for automating the measurement of as-built components using artificial intelligence and computer vision techniques. The pipeline requires a single image obtained with a stereo camera system to measure the sizes of selected objects or as-built components. The results in this work were demonstrated by measuring the sizes of concrete walls and columns. The novelty of this work is attributed to the use of a single image and a single target for developing a fully automated computer vision-based method for measuring any given object. The proposed solution is suitable for use in measuring the sizes of as-built components in built assets. It has the potential to be further developed and integrated with building information modelling applications for use on construction projects for progress monitoring. [ABSTRACT FROM AUTHOR]

Discrete element numerical simulations can help researchers find potential problems in the design phase, shortening the development cycle and reducing costs. In the field of agricultural engineering, more and more researchers are using discrete element methods (DEM) to assist in designing and optimising equipment parameters. Model parameters calibration is a prerequisite for discrete element numerical calculations, and the angle of repose (AoR) is commonly used to calibrate the parameters. However, the measurement of AoR in DEM was not seriously considered in industrial or academic fields. In practice, AoR is measured manually, using 2D digital image processing or using a 3D scan. However, reliable and consistent measurements of AoR in DEM are rarely mentioned. This study suggests an accurate and consistent way to measure AoR in DEM using a novel method to read particle coordinate information directly from the data file; then, the AoR is calculated by linearly fitting the centre coordinates of the outermost particles. Influences of input variables on AoR acquisition are discussed through several examples using customised templates with known angles. Then a comparative study of the accuracy of the measurement of AoR in DEM and the reliability of the parameter calibration results by the manual measurement, 2D digital image processing, and algorithm proposed in this paper was conducted. In case studies with four seed materials, this method prevented the subjective selection of AoR, improved the identification accuracy, and increased the precision and accuracy of DEM calibration. In addition, the time consumption for obtaining AoR using the novel method for measurement is much less than that of 2D.

Abstract Background The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few investigations on spinal motion analysis. The purpose of this study is to develop a deep learning-based model for fully automated measurement of SIRM based on flexion–neutral–extension cervical lateral radiographs and to evaluate its applicability for the flexion–extension (F/E), flexion–neutral (F/N), and neutral–extension (N/E) motion analysis. Methods A total of 2796 flexion, neutral, and extension cervical lateral radiographs from 932 patients were analyzed. Radiographs from 100 patients were randomly selected as the test set, and those from the remaining 832 patients were used for training and validation. Landmarks were annotated for measuring SIRM at five segments from C2/3 to C6/7 on F/E, F/N, and N/E motion. High-Resolution Net (HRNet) was used as the main structure to train the landmark detection network. Landmark performance was assessed according to the percentage of correct key points (PCK) and mean of the percentage of correct key points (MPCK). Measurement performance was evaluated by intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots. Results At a 2-mm distance threshold, the PCK for the model ranged from 94 to 100%. Compared with the reference standards, the model showed high accuracy for SIRM measurements for all segments on F/E and F/N motion. On N/E motion, the model provided reliable measurements from C3/4 to C6/7, but not C2/3. Compared with the radiologists’ measurements, the model showed similar performance to the radiologists. Conclusions The developed model can automatically measure SIRM on flexion–neutral–extension cervical lateral radiographs and showed comparable performance with radiologists. It may provide rapid, accurate, and comprehensive information for cervical motion analysis.

Background: A novel method using a moving threshold (r‐PLTmt) to determine canine r‐PLTs (reticulated platelets) has been introduced for ADVIA 2120 software v6.11.7. Objectives: We aimed to evaluate absolute (ar‐PLTmt) and percent (%r‐PLTmt) prior to and after visual inspection of scattergrams (ar‐PLTmtv, %rPLTmtv) compared with flow cytometry (flow) and to determine reference intervals (RIs) in 120 dogs. Methods: For method comparison, 42 blood samples of healthy and thrombocytopenic dogs were included. Calculation of Spearman's rho, Bland–Altman, and Passing‐Bablok analysis was performed. Coefficients of variation (CVs) were determined for three concentration levels. Results: Moderate correlations between %r‐PLTmt and %r‐PLTmtv (rs 0.75–0.76) were seen compared with flow cytometry. The CV for medium %r‐PLTs counts assessed with flow cytometry was 12.9%. Comparable CVs were obtained for ar‐PLTmt (14.4%) and %r‐PLTmt (15.7%), and ar‐PLTmtv and %r‐PLTmtv (10.9% and 12.9%, respectively). At low and high concentration levels, CVs for % and absolute r‐PLTmt/rPLTmtv ranged between 23%–30% and 15%–20%. In patients with microcytic hypochromic erythrocytes, CVs for ar‐PLTmt and %r‐PLTmt were 36%–66%. Visual inspection of scattergrams resulted in a marked decrease in CV ranging between 15% and 20%. A proportional bias of 10.8% between %r‐PLTmt and flow cytometry became lower (9.7%) after visual validation of scattergrams. Passing‐Bablok analysis showed proportional and constant error. RIs for r‐PLTmt and r‐PLTmtv were 0.2%–3.8% and 0.6–10.2 × 109/L and 0.3%–4.5% and 1.1–10.3 × 109/L, respectively. Median values for %r‐PLTmtv were higher in young adults (≤2 years) than in older dogs (P = 0.03). Conclusions: r‐PLTmt and r‐PLTmtv were moderately correlated with flow cytometry. Visual inspection of scattergrams is recommended. [ABSTRACT FROM AUTHOR]

The plankton metabolism determines fluctuations in the oxygen concentrations of a water body, its ecological condition, and the exchange of СО2 with the atmosphere. We used an automated device that allowed us to make mass measurements of intraday values of the gross primary production (GPP) and respiration (R) of plankton. During our studies of the Mozhaisk Reservoir (Russia) in the summer of 2017, we established an important fact: the maximum values of GPP were observed essentially earlier in the daytime than those of R. This is explained by the fact that GPP is related to solar radiation (the maximum values are observed at noon), whereas R is related to the temperature of water, which reaches its maximum several hours after in the afternoon. As a result, the maximum value of dissolved oxygen was observed in the second half of the day, when GPP and R become even. The device we have designed may be used for continuously monitoring the metabolism of plankton and its responses to various hydrometeorological, hydrochemical, and anthropogenic impacts. [ABSTRACT FROM AUTHOR]

The measurement and characterization of luminaires and lighting scenes is an important part of their design and evaluation process. These measurements often rely on special equipment and generally require manual labor. Our study investigates the possibility of automated lighting measurement using drones, a solution that is gaining popularity in various industrial applications. More precisely, we investigate the possibility of automating illuminance mapping and directional luminous intensity measurement. For this purpose, we present simple models to evaluate the effect of positioning errors on the measurements' performance. Based on these models, we outline the limitations of similar systems, why the lower hollow cone (~10–45°) is ideal for the measurement, and that major uncertainties arise at the height of the luminaire and directly underneath (or above) it. We compare the results to a proof‐of‐concept prototype device built using commercially available components, experimentally demonstrating our findings. Finally, based on the experiments, we provide guidelines and recommendations for the development of a device capable of in‐situ photometric measurement of luminaires. [ABSTRACT FROM AUTHOR]

Background: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few investigations on spinal motion analysis. The purpose of this study is to develop a deep learning-based model for fully automated measurement of SIRM based on flexion-neutral-extension cervical lateral radiographs and to evaluate its applicability for the flexion-extension (F/E), flexion-neutral (F/N), and neutral-extension (N/E) motion analysis.Methods: A total of 2796 flexion, neutral, and extension cervical lateral radiographs from 932 patients were analyzed. Radiographs from 100 patients were randomly selected as the test set, and those from the remaining 832 patients were used for training and validation. Landmarks were annotated for measuring SIRM at five segments from C2/3 to C6/7 on F/E, F/N, and N/E motion. High-Resolution Net (HRNet) was used as the main structure to train the landmark detection network. Landmark performance was assessed according to the percentage of correct key points (PCK) and mean of the percentage of correct key points (MPCK). Measurement performance was evaluated by intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots.Results: At a 2-mm distance threshold, the PCK for the model ranged from 94 to 100%. Compared with the reference standards, the model showed high accuracy for SIRM measurements for all segments on F/E and F/N motion. On N/E motion, the model provided reliable measurements from C3/4 to C6/7, but not C2/3. Compared with the radiologists' measurements, the model showed similar performance to the radiologists.Conclusions: The developed model can automatically measure SIRM on flexion-neutral-extension cervical lateral radiographs and showed comparable performance with radiologists. It may provide rapid, accurate, and comprehensive information for cervical motion analysis. [ABSTRACT FROM AUTHOR]

Regular inspections during construction work ensure that the completed work aligns with the plans and specifications and that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision techniques for measuring as-built elements predominantly employ three-dimensional laser scanning or three-dimensional photogrammetry modeling to ascertain the geometric properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element’s coordinates, making the deployment of these techniques on dynamic construction project sites challenging. This paper proposes a pipeline for automating the measurement of as-built components using artificial intelligence and computer vision techniques. The pipeline requires a single image obtained with a stereo camera system to measure the sizes of selected objects or as-built components. The results in this work were demonstrated by measuring the sizes of concrete walls and columns. The novelty of this work is attributed to the use of a single image and a single target for developing a fully automated computer vision-based method for measuring any given object. The proposed solution is suitable for use in measuring the sizes of as-built components in built assets. It has the potential to be further developed and integrated with building information modelling applications for use on construction projects for progress monitoring.

Abstract Premise The agar‐based culture of Arabidopsis seedlings is widely used for quantifying root traits. Shoot traits are generally overlooked in these studies, probably because the rosettes are often askew. A technique to assess the shoot surface area of seedlings grown inside agar culture dishes would facilitate simultaneous root and shoot phenotyping. Methods We developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes. We validated this method by comparing its output with other metrics of seedling growth. As part of a larger study on genetic variation in plant responses to nitrogen form and concentration, we measured the rosette areas from more than 2000 plate images. Results The rosette area measured from plate images was strongly correlated with the rosette area measured from directly overhead and moderately correlated with seedling mass. Rosette area in the large image set was significantly influenced by genotype and nitrogen treatment. The broad‐sense heritability of leaf area measured using this method was 0.28. Discussion These results indicated that this approach for estimating rosette area produces accurate shoot phenotype data. It can be used with image sets for which other methods of leaf area quantification prove unsuitable.

Purpose: A radiologic assessment method to measure position change of screw-rod constructs over time by superposing the 3‑dimensional images assists in quantitative evaluation of screw loosening. We investigated the association between position change and radiolucent zone that was commonly used for diagnosing screw loosening. Methods: In this study 101 patients who underwent lumbar fusion were reviewed. Patient characteristics included age, sex, indications for surgery, number of fused levels, surgical procedures, and timing of follow-up computed tomography (CT, 1–5 months, 6–11 months, and ≥ 12 months). The Hounsfield unit values of L1 vertebra on preoperative CT were measured, and the radiolucent zone on each follow-up CT was evaluated. Using baseline CT on the day after surgery and follow-up CT, 3‑dimensional images of screw-rod constructs were generated and superposed. Position change was assessed by the median of the distances between the 3‑dimensional images at baseline and follow-up using the automated measurement method. Patient characteristics, the Hounsfield unit values of L1, and the amount of position change were categorized into the radiolucent zone presence and absence groups and compared. Results: The medians of position change were 0.281 mm and 0.136 mm in the radiolucent zone presence and absence groups, respectively (P < 0.001 by Mann-Whitney U-test). The area under the curve for position change in identifying radiolucent zone was 0.846; the cut-off value was 1.76 mm. In multivariable analysis, position change was independently associated with radiolucent zone (adjusted odds ratio per 0.1 mm, 2.80, 95% confidence interval 1.70–4.61). Conclusion: Radiolucent zone was associated with position change of screw-rod constructs. [ABSTRACT FROM AUTHOR]

Mammalian lungs are comprised of large numbers of tracheobronchial airways that transition from the trachea to alveoli. Studies as wide ranging as pollutant deposition and lung development rely on accurate characterization of these airways. Advancements in CT imaging and the value of computational approaches in eliminating the burden of manual measurement are providing increased efficiency in obtaining this geometric data. In this study, we compare an automated method to a manual one for the first six generations of three Balb/c mouse lungs. We find good agreement between manual and automated methods and that much of the disagreement can be attributed to method precision. Using the automated method, we then provide anatomical data for the entire tracheobronchial airway tree from three Balb/C mice. Anat Rec, 2017. © 2017 Wiley Periodicals, Inc. Anat Rec, 300:2046-2057, 2017. © 2017 Wiley Periodicals, Inc.

Purpose: The aim of this study was to conduct a systematic review and meta-analysis on the reliability and applicability of artificial intelligence (AI)-based analysis of leg axis parameters. We hypothesized that AI-based leg axis measurements would be less time-consuming and as accurate as those performed by human raters., Methods: The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO). PubMed, Epistemonikos, and Web of Science were searched up to 24 February 2024, using a BOOLEAN search strategy. Titles and abstracts of identified records were screened through a stepwise process. Data extraction and quality assessment of the included papers were followed by a frequentist meta-analysis employing a common effect/random effects model with inverse variance and the Sidik-Jonkman heterogeneity estimator., Results: A total of 13 studies encompassing 3192 patients were included in this meta-analysis. All studies compared AI-based leg axis measurements on long-leg radiographs (LLR) with those performed by human raters. The parameters hip knee ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), and joint-line convergence angle (JLCA) showed excellent agreement between AI and human raters. The AI system was approximately 3 min faster in reading standing long-leg anteroposterior radiographs (LLRs) compared with human raters., Conclusion: AI-based assessment of leg axis parameters is an efficient, accurate, and time-saving procedure. The quality of AI-based assessment of the investigated parameters does not appear to be affected by the presence of implants or pathological conditions., Level of Evidence: Level I., (© 2024 The Author(s). Knee Surgery, Sports Traumatology, Arthroscopy published by John Wiley & Sons Ltd on behalf of European Society of Sports Traumatology, Knee Surgery and Arthroscopy.)

Objective: To evaluate the clinical value of automated measurements by AutoEF (GE EchoPAC system, version 113) in left ventricular (LV) volumes and ejection fraction (EF) estimation based on the biplane Simpson’s method (manual method) in different clinical subsets. Methods: A total of 322 subjects participated in this study (the common group). In the common group, 112 patients with coronary heart disease (CHD) were divided into the CHD group, and 34 CHD patients with LV wall motion abnormalities (WMA) comprising the CHD group, renamed the WMA group. LV volumes and EF were assessed using both manual tracing and automated estimation. Time spent on each method was documented. The agreements in echocardiographic measurements by different methods were assessed by intraclass correlation coefficients (ICC) and Bland-Altman analysis. Results: The average analysis time of the automated method was 12 ± 1 s/patient with excellent repeatability. ICC revealed good consistency between manual and automated EF in all groups, especially in the CHD and WMA groups, although Bland-Altman analysis showed non-negligible bias in EF estimation between the two methods. ICC analysis showed a good correlation between automated and manual EF in all the good and poor image quality subgroups. Conclusion: Automated method by AutoEF was a time-saving, excellent reproducible, and resistant to image interference approach, with a strong potential in left ventricular function measurements, especially for patients with CHD and/or WMA.

Abstract Self-direction skill is considered a vital skill for twenty-first-century learners in both the learning context and physical activity context. Analysis skill for self-directed activities requires the students to analyze their own activity data for understanding their status in that activity. It is an important phase that determines whether an appropriate plan can be set or not. This research presents a framework designed to foster students’ analysis skill in self-directed activities, which aims (1) to build a technology-enabled learning system allowing students to practice analyzing data from their own daily contexts, (2) to propose an approach to model student’s analysis skill acquisition level and process, and (3) to provide automated support and feedback for analysis skill development tasks. The analysis module based on the proposed framework was implemented in the GOAL system which synchronized data from learners’ physical and reading activities. A study was conducted with 51 undergraduate students to find reliable indicators for the model to then measure students’ analysis skills. By further analyzing students’ actual usage of the GOAL system, we found the actual activity levels and their preferences regarding analysis varied for the chosen contexts (learning and physical activity). The different context preference groups were almost equal, highlighting the utility of a system that integrates data from multiple contexts. Such a system can potentially respond to students’ individual preferences to execute and acquire self-direction skill.

This article presents a dictionary-based measurement of populist communication that reaches citizens directly through social media in German. The studied populist messages reflect ideational definitions of populism. Thus, populist messages appeal to the people, dismiss the elites as appalling, or highlight the people's right to unfettered rule. Despite German-speaking countries offering a variety of populist parties, existing automated approaches are rarely applicable to German texts. Furthermore, they are not tailored to social media and often focus only on anti-elitism. I vastly improved existing dictionaries by analyzing populist ideology in its entirety, including people-centrism and the demand for people's sovereignty. The article showed how known populist parties in Austria, Germany, and Switzerland spread these messages to a great extent on social media. Furthermore, I was able to highlight intra-party differences. Finally, the article discusses different aspects of validity and shows that the proposed approach offers high convergent validity and split-half reliability. [ABSTRACT FROM AUTHOR]

The capability to obtain detailed motility information of model organisms is fundamental to reveal their functional and social behavior characteristics. Zebrafish is a powerful vertebrate model organism. Despite recent success in the automatic quantification of adult zebrafish movement, it remains a laborious task for group zebrafish larval tracking due to their similar appearance, frequent occlusions, and highly discontinuous kinematics. This article presents DanioSense (DS), an automatic tracker for group larval zebrafish, to overcome these tracking challenges. The integration of a light convolutional neural network and a centerline extraction algorithm enables the tracker to localize individuals even in occlusion cases where objects’ identities are prone to switch. With reliable detections, an adaptive Kalman filter is designed to optimally estimate locomotive parameters, which is also used for object reidentification accomplished by a two-stage data association protocol. Experimental results demonstrated a tracking accuracy of over 97%, median errors of $102~{\mathrm{\mu m}}$ , and 8.8° for the position and orientation measurement, and a processing speed of over 30 frames/s with a normal computer configuration. DS provides detailed quantitative data for a large-scale larvae group in nearly real time, highly boosting the efficiency of characterizing individual phenotypes and analyzing social interactions. Note to Practitioners—This article aimed to tackle the problem of automated tracking groups of zebrafish larvae, an ideal vertebrate model organism for large-scale chemical and genetic screens. The task of group tracking is to record each individual’s movement and calculate their position, velocity, direction, and other parameters for further analysis, where the correct identity of each individual must be maintained. Existing algorithms either switch larvae’ identities easily or are unable to achieve online tracking due to the limitations of their methods to address individuals’ intersections. DanioSense (DS) adopts a convolutional neural network to identify larval heads whenever they intersect and uses an adaptive Kalman filter to calculate the movement parameters optimally. Besides, a range of visualization options is designed to bring insight into underlying patterns through massive amounts of data. Theoretically, this algorithm’s approach to solving intersections and calculating movement statistics can also apply to other fish-like animals. Its visualization options are applicable to other tracking systems. The key advantage of Daniosense over existing trackers is the capability to track each larva within a group and output detailed quantitative data in nearly real time. The tracking performance of DS is based on the quality of image segmentation and the success rate of classifying samples. Many state-of-the-art image segmentation and classification neural networks can be adopted to extend this system’s applications to more complex environments but at a higher computation and time cost, which is a tradeoff between efficiency and capability. Some applications require a higher video sampling rate, so the system’s processing speed needs to be further improved with better hardware and software framework optimization. The next steps include improving the processing efficiency, providing more tracking modules and visualization options, and extending its application fields. [ABSTRACT FROM AUTHOR]

• A multi-sensor line scanning profiler was constructed. • A surface recognition method was proposed for the automated measurement of complex or unknown surfaces. • A point cloud extraction algorithm was proposed, which can extract target point clouds with a high signal-to-noise ratio. • An automated measurement path design algorithm was proposed, which can be applied to complex surfaces and exhibits high operational efficiency. Existing high-precision measurement devices face numerous challenges when dealing with complex surfaces, including high manual involvement, low measurement efficiency, and high costs. It is even more difficult to obtain complete surface data, particularly when confronted with unknown surfaces. This paper combines a fringe projection sensor with a high-precision line-structured light sensor to construct a line scan profiler. It proposes a surface recognition measurement method to overcome these challenges. This method fully leverages the fringe projection sensor's wide-field advantage to identify the object's surface shape and pose quickly. Subsequently, through the point cloud extraction and path planning algorithms proposed in this paper, efficient and complete line scan measurement paths are automatically generated, thus achieving the automation of the measurement process. Experimental measurements of aspherical lenses, freeform reflective mirrors, and artificial knee joint models validate the advantages of this method over existing methods in terms of measurement efficiency, automation capability, and applicability. [ABSTRACT FROM AUTHOR]

• An automated method for measuring the propagation of visually thin fatigue crack is introduced. • The introduced method utilizes homography mapping error and DIC for extracting the propagation path of fatigue crack. • The introduced method is validated through a model experiment using an orthotropic steel deck. Fatigue testing poses significant challenges due to its time-consuming and labor-intensive nature, especially when manually observing visually thin fatigue cracks. To address this issue, this study introduces a novel approach that utilizes homography mapping error (HME) digital image correlation (DIC) for automated measurement of thin fatigue crack, including their width, length, and morphology. The HME is employed to differentiate between the two flanks of a fatigue crack. By leveraging HME, the method accurately extracts the crack propagation path. Validation through numerical simulations demonstrates sub-pixel level accuracy in measuring crack width and length, with precision at approximately 0.01 mm and 0.2 mm, respectively. Additionally, morphology characterization achieves an accuracy of approximately 0.42 mm. Results from physical experiment further confirm the practical feasibility of this method in fatigue testing. [ABSTRACT FROM AUTHOR]

The Nuclear and Energy Research Institute (IPEN-CNEN/SP) currently employs manual external u-shape frame micrometers with non-rotating spindles, and chamfered measuring anvils at 21 pre-defined points to control the thickness of its fuel plates. However, it is acknowledged that this method introduces the human element into the measurement process, potentially compromising result accuracy and the integrity of the fuel plates' surfaces. This research introduces a novel thickness measurement method and an accompanying automated device featuring precise movement mechanisms, data capture, transcription, and processing. Our findings emphasize the effectiveness of this new measurement system and the structural integrity of the equipment, highlighting its potential to significantly enhance both the speed and metrological reliability of dimensional control processes for nuclear fuel plates manufactured at IPEN-CNEN/SP. [Display omitted] • Automated thickness measurement method for nuclear fuel plates. • Significant enhancement of both the speed and metrological reliability of dimensional control processes. • An advanced approach for measuring the thickness of fuel plates. • A new precision technique for measuring fuel plate thickness. • Precise movement mechanisms and data capture, transcription, and processing. [ABSTRACT FROM AUTHOR]

Keywords: allergy; automated measurement; clinical trial; diagnosis; prick test EN allergy automated measurement clinical trial diagnosis prick test 299 301 3 12/30/22 20230101 NES 230101 Skin prick testing (SPT) is the cornerstone of IgE-mediated allergy diagnosis,[1] due to its high sensitivity and specificity.[2] However, a uniform method for wheal measurement does not exist. Allergy, automated measurement, clinical trial, diagnosis, prick test IgE allergy diagnostics and other relevant tests in allergy, a World Allergy Organization position paper. [Extracted from the article]

AbstractBackground and aim Eyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the management of blepharoptosis. We aimed to proposed a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery.Methods This study included 135 ptotic eyes of 103 patients who underwent blepharoptosis surgery. Facial photographs were taken preoperatively and postoperatively. Margin reflex distance (MRD) 1 and 2 of the operated eyes were manually measured by a senior surgeon. Multiple eyelid morphological parameters, such as MRD1, MRD2, upper eyelid length and corneal area, were automatically measured by our deep learning-based image analysis. Agreement between manual and automated measurements, as well as two repeated automated measurements of MRDs were analysed. Preoperative and postoperative eyelid morphological parameters were compared. Postoperative eyelid contour symmetry was evaluated using multiple mid-pupil lid distances (MPLDs).Results The intraclass correlation coefficients (ICCs) between manual and automated measurements of MRDs ranged from 0.934 to 0.971 (p

The measurement of beef cattle's body size is the key problem to be solved in beef cattle breeding. Automatic body size measurement were still influenced due to the light, distance, and other factors. To achieve measurement of beef cattle's oblique length, body height, chest depth, and hoof diameter under different distances and lighting conditions, an automatic measurement method based on key point detection and monocular depth estimation was proposed. Firstly, the cattle were detected by the YOLOv5s network, then, the key points were detected through the Lite-HRNet network and the pixel distance of the body size was calculated. Secondly, the monocular depth information was extracted through the Global-Local Path networks to obtain the relative depth value of the images. The relationship between the actual distance and the relative depth was obtained by using the actual distance calibration. Finally, the ratio between the pixel length and the actual length was obtained through the imaging principle of the RGB camera, and the beef cattle images in different scenarios were measured. It was concluded that the proposed system could complete the measurement work of beef cattle straight side images under no sky or small sky background. Thirty beef cattle images were measured and the average relative error of body height, body oblique length, chest depth and hoof diameter were 6.75 %, 7.55 %, 8.00 %, 8.97 %. The results showed that the proposed method could measure the body size of beef cattle at different distances and light conditions. [ABSTRACT FROM AUTHOR]

• An automated method for the measurement the propagation of fatigue crack's width, length, and morphology is introduced. • The method utilizes decorrelated digital image correlation to extract the propagation path of fatigue crack. • The method is validated through a model experiment using an orthotropic steel deck. Measurements of fatigue crack propagation are crucial for fatigue tests. Nowadays, fatigue tests still necessitate experimenters to manually observe and document the propagation of fatigue cracks, a process that proves to be extremely time-consuming and labor-intensive. This study introduces a novel approach to fatigue crack measurement by employing the decorrelated digital image correlation (DIC) technique, which stands in contrast to the conventional DIC method. The proposed method utilizes decorrelation field to extract crack propagation path. The proposed method enables the automated measurement of fatigue crack parameters such as width, length, and morphology. Both numerical simulations and physical model experiments were conducted to validate the effectiveness of the proposed method. The numerical experiments demonstrate that the method achieves sub-pixel level accuracy in measuring crack width, length, and morphology. The results from physical experiment further affirm the feasibility of the method for practical application. [ABSTRACT FROM AUTHOR]

The three-dimensional carpal alignment was measured in the neutral wrist position in 121 asymptomatic volunteers using computer-based cone-beam CT analysis. Normal values and the reliability of the automated analysis are reported. An analysis software based on segmentation of CT images and mathematical modelling was used to determine several axis variants based on different landmarks and to calculate the intercarpal angles automatically. Twenty wrists were imaged twice to determine intra-observer reliability. Mean values using the preferred axis variants were: scapholunate angle 57° (SD 9°), radiolunate angle 2° (SD 10°), lunocapitate angle –14° (SD 9°). Using alternate axis variants notably changed the angles produced. The intra-observer reliability of the analysis was excellent (mean intraclass correlation coefficient 0.97, SD 0.03). Computer-based CT analysis enables highly reproducible and automated assessment of carpal alignment. This study provides a reference database for measurement techniques and normal carpal angle values in three-dimensional imaging. [ABSTRACT FROM AUTHOR]

Eyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the management of blepharoptosis. We aimed to proposed a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery. This study included 135 ptotic eyes of 103 patients who underwent blepharoptosis surgery. Facial photographs were taken preoperatively and postoperatively. Margin reflex distance (MRD) 1 and 2 of the operated eyes were manually measured by a senior surgeon. Multiple eyelid morphological parameters, such as MRD1, MRD2, upper eyelid length and corneal area, were automatically measured by our deep learning-based image analysis. Agreement between manual and automated measurements, as well as two repeated automated measurements of MRDs were analysed. Preoperative and postoperative eyelid morphological parameters were compared. Postoperative eyelid contour symmetry was evaluated using multiple mid-pupil lid distances (MPLDs). The intraclass correlation coefficients (ICCs) between manual and automated measurements of MRDs ranged from 0.934 to 0.971 (p <.001), and the bias ranged from 0.09 mm to 0.15 mm. The ICCs between two repeated automated measurements were up to 0.999 (p <.001), and the bias was no more than 0.002 mm. After surgery, MRD1 increased significantly from 0.31 ± 1.17 mm to 2.89 ± 1.06 mm, upper eyelid length from 19.94 ± 3.61 mm to 21.40 ± 2.40 mm, and corneal area from 52.72 ± 15.97 mm2 to 76.31 ± 11.31mm2 (all p <.001). Postoperative binocular MPLDs at different angles (from 0° to 180°) showed no significant differences in the patients. This technique had high accuracy and repeatability for automatically measuring eyelid morphology, which allows objective assessment of blepharoptosis surgical outcomes. Using only patients' photographs, this technique has great potential in diagnosis and management of other eyelid-related diseases. [ABSTRACT FROM AUTHOR]

Background: The World Trade Center (WTC) attack exposed thousands of workers to toxic chemicals that have been linked to liver diseases and cancers. This study examined the relationship between the intensity of WTC dust exposure and the risk of hepatic steatosis in the WTC General Responders Cohort (GRC). Methods: All low‐dose computed tomography (CT) scans of the chest performed on the WTC GRC between September 11, 2001 and December 31, 2018, collected as part of the World Trade Center Health Program, were reviewed. WTC dust exposure was categorized into five groups based on WTC arrival time. CT liver density was estimated using an automated algorithm, statistics‐based liver density estimation from imaging. The relationship between the intensity of WTC dust exposure and the risk of hepatic steatosis was examined using univariate and multivariable regression analyses. Results: Of the 1788 WTC responders, 258 (14.4%) had liver attenuation less than 40 Hounsfield units (HU < 40) on their earliest CT. Median time after September 11, 2001 and the earliest available CT was 11.3 years (interquartile range: 8.0–14.9 years). Prevalence of liver attenuation less than 40 HU was 17.0% for arrivals on September 11, 2001, 16.0% for arrivals on (September 12, 2001 or September 13, 2001), 10.9% for arrivals on September 14–30, 2001, and 9.0% for arrivals on January 10, 2001 or later (p = 0.0015). A statistically significant trend of increasing liver steatosis was observed with earlier arrival times (p < 0.0001). WTC arrival time remained a significant independent factor for decreased liver attenuation after controlling for other covariates. Conclusions: Early arrival at the WTC site was significantly associated with increasing hepatic steatosis. [ABSTRACT FROM AUTHOR]

Abstract Background Mammographic breast density is an important predictor of breast cancer, but its measurement has limitations related to subjectivity of visual evaluation or to difficult access for automatic volumetric measurement methods. Herein, we describe the design and clinical validation of Aguida, a software program for automated quantification of breast density from flat mammography images. Materials and methods The software program was developed in MatLab. After image segmentation separating the background from the breast image, the operator positions a cursor defining a region of interest on the pectoralis major muscle from the mediolateral oblique view. Then, in the craniocaudal view, the threshold for separation of the dense tissue is based on the optical density of the pectoral muscle, and the proportion of dense tissue is calculated by the program. Mammograms obtained from 2 different occasions in 291 women were used for clinical evaluation. Results The intraclass correlation coefficient (ICC) between breast density measurements by the software and by a radiologist was 0.96, with a bias of only 0.67 percentage points and a 95% limit of agreement of 13.5 percentage points; the ICC was 0.94 in the interobserver reliability assessment by two radiologists with different experience; and the ICC was 0.98 in the intraobserver reliability assessment. The distribution among the density classes was close to the values obtained with the volumetric software. Conclusions Measurement of breast density with the Aguida program from flat mammography images showed high agreement with the visual determination by radiologists, and high inter- and intra-observer reliability.

Background: Weight-bearing CT (WBCT) 3D volumetric measurement has shown promising accuracy for the diagnosis of syndesmotic instability. However, these measurements are rather complex and time-consuming, rendering them a clinically unfavorable option. We hypothesized that automatized measurements would be more accurate and time-efficient than manual ones., Methods: Thirty cases of intraoperatively confirmed syndesmotic instability along with thirty individuals with no injuries to the ankle joint were recruited as cases and controls, retrospectively. Two observers conducted the manual volumetric measurements two times, at a one-week interval. An automated algorithm for 3D WBCT measurements was developed to conduct the measurements on the axial images. The time spent on each method was recorded. Mann-Whitney U test was used to compare the values between human raters and computers. Inter- and intra-class reliability were calculated., Results: The intra-class correlation coefficient was found to be "excellent" for the automated measurements (0.97) and "good" for the observers (0.75). Similarly, the Cronbach's alpha was shown to be higher for the computer (0.88) than the observers (0.60 and 0.62). The mean time spent on the measurements was different between human raters and the computer-assisted method (p < 0.001)., Conclusion: Automated volumetric assessment of syndesmosis seems to be a faster and more reliable option than the manual one. We suggest future larger-scale prospective studies conducted under actual clinical circumstances for more definitive conclusions., Level of Evidence: Retrospective case-control study - Level 3., Competing Interests: Declaration of Competing Interest The authors declare that there are no conflicts of interest regarding the publication of this paper. No direct or indirect financial incentives have been received from any entities that could influence the objectivity, integrity, and interpretation of this research. We understand the importance of disclosing potential conflicts of interest and hereby affirm our commitment to transparency in our research., (Copyright © 2024. Published by Elsevier Ltd.)

The reliable particle parameters of biomass for discrete element method (DEM) pose a challenge in reproducing actual conditions in a simulation environment. However, the calibration of simulation parameters was not seriously considered in industrial or academic fields. In order to adjust the main material parameters and contact parameters of biomass materials in DEM simulation, a calibration procedure based on the comparison between the experimental angle of repose (AoR) by using 3D scan in laboratory and the automated measurement of the numerical AoR (aMAoR) is proposed in this study. A novel data processing algorithm is developed for aMAoR and integrated in a self-developed graphic user interface (GUI). Influences of GUI input variables on AoR acquisition are discussed through several examples. In case studies with four biomass materials, this method prevented the subjective selection of AoR, improved the identification accuracy and therefore decreased minimum amount of suitable parameter combinations for DEM calibration. [Display omitted] • The first study to obtain simulated AoR without influence of subjective selection. • The whole conical surface of heap is taken into consideration for more precise AoR. • Case studies on four representative biomass were used to verify the applicability. • The method is less time-consuming and provides valid result for DEM calibration. [ABSTRACT FROM AUTHOR]

Self-direction skill is considered a vital skill for twenty-first-century learners in both the learning context and physical activity context. Analysis skill for self-directed activities requires the students to analyze their own activity data for understanding their status in that activity. It is an important phase that determines whether an appropriate plan can be set or not. This research presents a framework designed to foster students' analysis skill in self-directed activities, which aims (1) to build a technology-enabled learning system allowing students to practice analyzing data from their own daily contexts, (2) to propose an approach to model student's analysis skill acquisition level and process, and (3) to provide automated support and feedback for analysis skill development tasks. The analysis module based on the proposed framework was implemented in the GOAL system which synchronized data from learners' physical and reading activities. A study was conducted with 51 undergraduate students to find reliable indicators for the model to then measure students' analysis skills. By further analyzing students' actual usage of the GOAL system, we found the actual activity levels and their preferences regarding analysis varied for the chosen contexts (learning and physical activity). The different context preference groups were almost equal, highlighting the utility of a system that integrates data from multiple contexts. Such a system can potentially respond to students' individual preferences to execute and acquire self-direction skill. [ABSTRACT FROM AUTHOR]

Children's own language production has a role in structuring the language of their conversation partners and influences their own development. Children's active participation in their own language development is most apparent in the rich body of work investigating language in natural environments. The advent of automated measures of vocalizations and movement have made such in situ research increasingly feasible. In this chapter, we review recent research on children's language development in context with a particular focus on research employing automated methods in preschool classrooms for children between ages 2 and 5 years. These automated methods indicate that the speech directed to preschool children from specific peers predicts the child's speech to those peers on a subsequent observation occasion. Similar patterns are seen in the influence of peer and teacher phonemic diversity on the phonemic diversity of children's speech to those partners. In both cases, children's own speech to partners was the best predictor of their language abilities, suggesting their active role in their own development. Finally, new research suggests the potential of machine learning to predict children's speech in group contexts, and to transcribe classroom speech to better understand the content of children's conversations and how they change with development., (Copyright © 2024. Published by Elsevier Inc.)

The Microsoft Kinect is a motion‐sensing device that enables users to interact with a computer through body movements. The Kinect was initially developed for video game users (e.g., Microsoft Xbox) although another important use may be as an automated system for measuring human behavior. To demonstrate the potential utility of the Kinect for behavioral measurement, we asked adults to perform various repetitive behaviors while in view of the Kinect sensor. Data collected from Kinect was analyzed via the Matrix Laboratory (MATLAB) program generating frequency of occurrences of repetitive behavior. To assess validity of the automated recording measurement, Kinect data were compared to frequency data obtained via direct human observation. Overall, there was a high‐level of agreement (92%) between measurement procedures (automated vs. human), although the automated system tended to result in slightly lower frequencies (false negatives) than was captured via human observation. Based on these findings, the Kinect technology showed solid potential as a tool for the automatic measurement of behavior. Suggestions for methodical refinement and future evaluation are discussed. [ABSTRACT FROM AUTHOR]

This article reports an automated optically-induced dielectrophoresis (ODEP) system for characterizing the specific membrane capacitance (SMC) of individual cells. The simulation of cell motion is conducted to analyze the electrokinetic forces acting on the cell. A self-developed visual tracking algorithm for multicells is used to realize an automated process for determining the frequency-sweeping range, crossover frequencies, and cell radii. The SMC values of malignant bladder cancer cells (T24 and RT4) and normal urothelial cells (SV-HUC-1) were quantified using the automated system, demonstrating that the system has a measurement speed of ~1 cell/s, an accuracy of 1 kHz for the crossover frequency determination, and an accuracy of $0.2~\mu \text{m}$ for the cell radius measurement. Note to Practitioners—The current manual optically-induced dielectrophoresis (ODEP) quantification of the specific membrane capacitance (SMC) values of the cells requires a tedious and time-consuming procedure for measuring the cell size and crossover frequency. The automated ODEP approach presented in this article was developed to identify multiple cells and trigger alternating current (ac) bias potential for measuring the radius and crossover frequency of multiple cells. Compared to the manual method, the automated system significantly improves the efficiency of the SMC measurement. [ABSTRACT FROM AUTHOR]

Background: Mammographic breast density is an important predictor of breast cancer, but its measurement has limitations related to subjectivity of visual evaluation or to difficult access for automatic volumetric measurement methods. Herein, we describe the design and clinical validation of Aguida, a software program for automated quantification of breast density from flat mammography images.Materials and Methods: The software program was developed in MatLab. After image segmentation separating the background from the breast image, the operator positions a cursor defining a region of interest on the pectoralis major muscle from the mediolateral oblique view. Then, in the craniocaudal view, the threshold for separation of the dense tissue is based on the optical density of the pectoral muscle, and the proportion of dense tissue is calculated by the program. Mammograms obtained from 2 different occasions in 291 women were used for clinical evaluation.Results: The intraclass correlation coefficient (ICC) between breast density measurements by the software and by a radiologist was 0.96, with a bias of only 0.67 percentage points and a 95% limit of agreement of 13.5 percentage points; the ICC was 0.94 in the interobserver reliability assessment by two radiologists with different experience; and the ICC was 0.98 in the intraobserver reliability assessment. The distribution among the density classes was close to the values obtained with the volumetric software.Conclusions: Measurement of breast density with the Aguida program from flat mammography images showed high agreement with the visual determination by radiologists, and high inter- and intra-observer reliability. [ABSTRACT FROM AUTHOR]

To evaluate the accuracy, reproducibility, and transcatheter heart valve (THV) sizing efficiency of an automated 3-dimensional transesophageal echocardiographic (3D-TEE) post-processing software in the assessments of aortic roots, intra-individually compared with multidetector computed tomography (MDCT). We prospectively studied 67 patients with normal aortic roots. We measured diameters of aortic annulus (AA), sinus of Valsalva (SOV), and sino-tubular junction (STJ) by full-automated and semi-automated methods using 3D-TEE datasets, then compared them to corresponding transthoracic echocardiography and MDCT values. THV sizes were chosen based on echocardiography and MDCT measurements according to recommended criterion. Taking MDCT planimetered diameters as reference, the full-automated (r: 0.4745-0.8792) and semi-automated (r: 0.6647-0.8805) 3D-TEE measurements were linearly correlated (p < 0.0001). The average differences between semi-automated or full-automated measurements and reference were 0.3 mm or 1.3 mm for AA, - 1.9 mm or - 0.5 mm for SOV, and - 0.1 mm or 1.9 mm for STJ, respectively. The intra-class correlation coefficients of semi-automated method were 0.79-0.96 (intra-observer) and 0.75-0.92 (inter-observer). THV sizing by semi-automated measurements using echocardiographic criteria was larger than that by MDCT measurements using MDCT criteria (p < 0.0001) but equivalent (p > 0.05) if both using MDCT standards. The new automated 3D-TEE software allows modeling and quantifying aortic roots with high reproducibility. Measurements by the semi-automated method closely approximate and well correlate with the corresponding MDCT, thus THV sizing by this modeled 3D-TEE measurements should adopt recommended MDCT criteria but not echocardiographic criteria. The full-automated 3D-TEE segmentations are yet immature. (Semi-automated assessMent of Aortic Roots by Three-dimensional transEsophageal echocaRdiography [SMARTER], NCT02724709). [ABSTRACT FROM AUTHOR]

Purpose: To propose a novel approach for automated measurements of margin reflex distance (MRD) using digital image analysis and to evaluate the agreement between automated and manual measurements of MRD. Materials and Methods: This observational study included 132 eyes of 66 volunteers referred to an oculoplastic clinic for blepharoptosis. Preoperative facial photographs of participants were taken. MRD1 and MRD2 were measured manually by a senior surgeon and automatically by our image-based algorithm. Correlation analyses and Bland–Altman analyses were performed to evaluate the agreement between the two measurements. Results: A strong positive correlation was observed between automated and manual MRD1 measurements, with Spearman's r = 0.968 (95% confidence interval [CI] = 0.949–0.976; P <.001) and intraclass correlation coefficient (ICC) = 0.966 (95% CI = 0.953–0.976; P <.001). The bias between automated and manual MRD1 measurements was 0.02 mm (95% CI = −0.06–0.10 mm), with 95% limits of agreement (LoA) = −0.88 to 0.92 mm. Agreement also existed in MRD2 measurements, with Spearman's r = 0.803 (95% CI = 0.716–0.865; P <.001), ICC = 0.833 (95% CI = 0.772–0.879; P <.001), and bias = 0.34 mm (95% CI = 0.26–0.42 mm), 95% LoA = −0.54 to 1.22 mm. Conclusions: Automated eyelid measurements in blepharoptosis using the image-based approach compare favorably with clinical manual measurements. This novel approach allows an objective assessment of ptosis with high accuracy. [ABSTRACT FROM AUTHOR]

Gravity may influence multiple aspects of legged locomotion, from the periods of limbs moving as pendulums to the muscle forces required to support the body. We present a system for exposing mice to hypergravity using a centrifuge and studying their locomotion and activity during exposure. Centrifuge-induced hypergravity has the advantages that it both allows animals to move freely, and it affects both body and limbs. The centrifuge can impose two levels of hypergravity concurrently, using two sets of arms of different lengths, each carrying a mouse cage outfitted with a force and speed measuring exercise wheel and an infrared high-speed camera; both triggered automatically when a mouse begins running on the wheel. Welfare is monitored using infrared cameras. As well as detailing the design of the centrifuge and instrumentation, we present example data from mice exposed to multiple levels of hypergravity and details of how they acclimatized to hypergravity.

Aims: Machine-learning (ML)-based automated measurement of echocardiography images emerges as an option to reduce observer variability. The objective of the study is to improve the accuracy of a pre-existing automated reading tool ('original detector') by federated ML-based re-training., Methods and Results: Automatisierte Vermessung der Echokardiographie was based on the echocardiography images of n = 4965 participants of the population-based Characteristics and Course of Heart Failure Stages A-B and Determinants of Progression Cohort Study. We implemented federated ML: echocardiography images were read by the Academic Core Lab Ultrasound-based Cardiovascular Imaging at the University Hospital Würzburg (UKW). A random algorithm selected 3226 participants for re-training of the original detector. According to data protection rules, the generation of ground truth and ML training cycles took place within the UKW network. Only non-personal training weights were exchanged with the external cooperation partner for the refinement of ML algorithms. Both the original detectors as the re-trained detector were then applied to the echocardiograms of n = 563 participants not used for training. With regard to the human referent, the re-trained detector revealed (i) superior accuracy when contrasted with the original detector's performance as it arrived at significantly smaller mean differences in all but one parameter, and a (ii) smaller absolute difference between measurements when compared with a group of different human observers., Conclusion: Population data-based ML in a federated ML set-up was feasible. The re-trained detector exhibited a much lower measurement variability than human readers. This gain in accuracy and precision strengthens the confidence in automated echocardiographic readings, which carries large potential for applications in various settings., Competing Interests: Conflict of interest: C.M. reports research cooperation with the University of Würzburg and TOMTEC Imaging Systems GmbH funded by a research grant from the Bavarian Ministry of Economic Affairs, Regional Development and Energy, Germany. G.G. reports research cooperation with the University Hospital of Würzburg and TOMTEC Imaging Systems GmbH funded by a research grant from the Bavarian Ministry of Economic Affairs, Regional Development and Energy, Germany. M.S. is a full-time employee of TOMTEC imaging systems. He further reports research cooperation with the University and University Hospital of Würzburg funded by a research grant from the Bavarian Ministry of Economic Affairs, Regional Development and Energy, Germany. N.H., H.W., and D.S. are full-time employees of TOMTEC imaging systems. H.W. and S.S. are supported by the CHFC Würzburg and by the German Federal Ministry of Education and Research (BMBF). All other authors report no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)