Manual Scoring Research Articles

Retracted] Design of Regression Model for MultiParameter Evaluation of English Pronunciation Quality

In order to better evaluate the quality of English pronunciation, this paper proposes a regression model design method based on multiparameter evaluation of English pronunciation quality. This method takes college students’ English pronunciation as the research object. Through the combination of various algorithms, a speech recognition model based on a machine learning neural network is constructed with characteristic parameters as input, and a speech quality evaluation model based on multiple regression is constructed with multiparameters such as intonation, speed, rhythm, and intonation as evaluation indicators. The experimental results show that log posterior probability and GOP are good measures of pronunciation standard. When used alone, a higher correlation with manual scores can be obtained, and the correlation of both exceeds 0.5. GOP has the best performance, with a correlation of 0.549. The combination of these two pronunciation standard evaluation features can further improve the evaluation performance, and the correlation degree reaches 0.574. Compared with the GOP algorithm with better performance, the evaluation performance is improved by 4.6%. Conclusion. The model provides a scientific basis for oral English speech recognition and objective evaluation of pronunciation quality.

A Deep Learning Approach to Automated Sleep Stages Classification Using Multi-Modal Signals

Sleep is one of the elements most vital to human life. However, the modern lifestyle continues to push people to neglect this critical requirement. With a vast majority of people falling victim to various sleep disorders, it has become increasingly essential to have a robust system for diagnosing and treating such ailments. Sleep stage classification is one of the primary steps for identifying sleep-related anomalies. Sleep stages are classified according to the frequency and nature of signals received during a polysomnography test. Since the early days, this has been performed manually with the help of trained technicians. However, manual scoring is often prone to error and subjectivity and requires tremendous time and effort. It is, therefore, essential to automatize this process. Several challenges from the correct selection of features remain to be faced in the machine learning-based sleep stage classification system. As an alternative, Deep Learning, capable of automatic feature extraction, proves far more reliable for this task. This experimental study analyses both techniques to compare and decide on a better approach. Three popular Machine Learning classifiers, namely Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), and a neural network comprising CNN and LSTM, have been trained on a vast base of diverse data. The proposed model reported an accuracy of 87.4% with CNN + LSTM.

An image-based technique for automated root disease severity assessment using PlantCV.

Plant disease severity assessments are used to quantify plant-pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis. Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot. Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (R 2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output. Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.

Sleep Staging Framework with Physiologically Harmonized Sub-Networks.

Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming gold-standard manual scoring procedure. Recently there have seen promising results on automatic stage scoring by extracting spatio-temporal features via deep neural networks from electroencephalogram (EEG). However, such methods fail to consistently yield good performance due to a missing piece in data representation: the medical criterion of the sleep scoring task on top of EEG features. We argue that capturing stage-specific features that satisfy the criterion of sleep medicine is non-trivial for automatic sleep scoring. This paper considers two criteria: Transient stage marker and Overall profile of EEG features, then we propose a physiologically meaningful framework for sleep stage scoring via mixed deep neural networks. The framework consists of two sub-networks: feature extraction networks, constructed in consideration of the physiological characteristics of sleep, and an attention-based scoring decision network. Moreover, we quantize the framework for potential use under an IoT setting. For proof-of-concept, the performance of the proposed framework is demonstrated by introducing multiple sleep datasets with the largest comprising 42,560h recorded from 5,793 subjects. From the experiment results, the proposed method achieves a competitive stage scoring performance, especially for Wake, N2, and N3, with higher F1 scores of 0.92, 0.86, and 0.88, respectively. Moreover, the feasibility analysis of framework quantization provides a potential for future implementation in the edge computing field and clinical settings.

Validation of the iSperm for assessing rhinoceros Sperm

This study's objective was to determine the feasibility of using a portable computer-assisted sperm analyzer to standardize the evaluation of rhinoceros sperm motility and concentration. Assessments were performed opportunistically as part of other ongoing studies and included both fresh and cryopreserved samples from black (Diceros bicornis; n = 3), white (Ceratotherium simum; n = 7), and greater one-horned (Rhinoceros unicornis; n = 5) rhinoceroses. Accuracy of the iSperm Rhino 5 software in identifying sperm cells was evaluated through manual scoring of sperm tracks (n = 48 videos from 12 samples). The number of sperm identified by the observer and the software were significantly correlated (Pearson r = 0.9908, P < 0.0001). Moreover, the bias between the observer to iSperm values was small (Bland-Altman test; mean ratio = 0.97 observer/iSperm). A strong correlation (Pearson r = 0.9847, P < 0.0001) between hemocytometer counting and the iSperm for sperm concentration (n = 50) was also observed with a bias of just 1.03 (mean ratio hemocytometer to iSperm). Reliability analysis was performed by comparing ten consecutive evaluations on four different samples using intraclass correlation coefficients (ICC). In this study, the reliability (aka repeatability) of measuring the concentration or total motility via iSperm was deemed excellent (ICC > 0.96). Results of this work demonstrate that the iSperm offers a reliable and semi-automated alternative to manual evaluations for assessing sperm concentrations and motility in the rhinoceros. The ability to determine wildlife sperm concentrations rapidly and motility objectively while working under field conditions would be a great asset to conservation biologists in both zoological institutions and wildlife reserves.

A novel deep domain adaptation method for automated detection of sleep apnea/hypopnea events

Objective. Sleep apnea–hypopnea syndrome (SAHS) is a common sleep-related respiratory disorder that is generally assessed for severity using polysomnography (PSG); however, the diversity of sampling devices and patients makes this not only costly but may also degrade the performance of the algorithms. Approach. This paper proposes a novel deep domain adaptation module which uses a long short-term memory–convolutional neural network embedded with the channel attention mechanism to achieve autonomous extraction of high-quality features. Meanwhile, a domain adaptation module was built to achieve domain-invariant feature extraction for reducing the differences in data distribution caused by different devices and other factors. In addition, during the training process, the algorithm used the last second label as the label of the PSG segment, so that second-by-second evaluation of respiratory events could be achieved. Main results. The algorithm applied the two datasets provided by PhysioNet as the source and target domains. The accuracy, sensitivity and specificity of the algorithm on the source domain were 86.46%, 86.11% and 93.17%, respectively, and on the target domain were 83.63%, 82.52%, 91.62%, respectively. The proposed algorithm showed strong generalization ability and the classification results were comparable to the current advanced methods. Besides, the apnea–hypopnea index values estimated by the proposed algorithm showed a high correlation with the manual scoring values on both domains. Significance. The proposed algorithm can effectively perform SAHS detection and evaluation with certain generalization.

A Quantitative, Digital Method to Analyze Human Figure Drawings as a Tool to Assess Body Representations Distortions in Stroke Patients.

Human figure drawings are widely used in clinical practice as a qualitative indication of Body Representations (BRs) alterations in stroke patients. The objective of this study is to present and validate the use of a new app called QDraw for the quantitative analysis of drawings and to investigate whether this analysis can reveal distortions of BRs in chronic stroke patients. QDraw has proven to generate reliable data as compared to manual scoring and in terms of inter-rater reliability, as shown by the high correlation coefficients. Moreover, human figure drawings from chronic stroke patients demonstrated a distortion of upper limb perception, as shown by a significantly higher arm length asymmetry compared to legs, whereas no difference was found in healthy controls. The present study supports the use of quantitative, digital methods (the QDraw app) to analyze human figure drawings as a tool to evaluate BRs distortions in stroke patients.

A novel approach for fine-grained traffic risk characterization and evaluation of urban road intersections

An urban road intersection environment with low traffic risk is an essential component of sustainable cities. Various risk analysis and simulation techniques have been developed to measure and evaluate traffic risks. However, flexible assessment of intersection risk at a fine resolution remains a great challenge. This study proposes a novel approach for characterizing the field distribution of traffic risks at an urban road intersection and assessing traffic risks in multiple dimensions to increase the flexibility of risk assessment. First, we define a new traffic risk metric to quantify the risk of intersection traffic conflict points by combining their frequency and severity. Second, the plume diffusion model is introduced to model the non-linear relationship between traffic conflict points by considering the propagation of the traffic risk in the intersection. In this way, the entire intersection risk distribution map is derived for real-time risk analysis. Finally, a multidimensional hierarchical evaluation system of traffic risk at intersections is designed, including the traffic risk indicators at the intersection, path, and turn mode levels. The results of a case study in Wuhan, China, demonstrate the continuous characterization results of the risk field and provide multidimensional risk assessment index results, which are highly consistent with the cognitive results of manual risk scoring (up to 90% similarity). It also allows for risk assessment in a single path and turn modes through the risk field characterization and reveals the spatiotemporal dynamic risk distribution, thus providing an alternative tool for risk decision-making at urban road intersections.

Binocular amblyopia treatment improves manual dexterity

To determine whether deficits in manual dexterity in children with amblyopia improve after binocular amblyopia treatment and whether improvements are related to age at treatment, baseline sensory status, or amount of improvement in sensory status with treatment. Manual dexterity (Movement Assessment Battery for Children-2), visual acuity, fusion, suppression, and stereoacuity were measured at baseline and after 4-8 weeks of binocular amblyopia in 134 children with amblyopia, including 75 children in the "younger group" (aged 3 to <7 years) and 59 in the "older group" (aged 7-10 years), and in 40 age-similar control children. Baseline manual dexterity standard scores of amblyopic children were significantly below those of controls in both the younger (8.81 ± 0.33 vs 11.80 ± 0.60 [P < 0.0001]) and older groups (7.19 ± 0.34 vs 9.75 ± 0.57 [P = 0.00013]). After 4-8 weeks of binocular amblyopia treatment, the younger group standard score improved to 9.85 ± 0.35 and the older group improved to 8.08 ± 0.39, but both groups remained significantly lower than controls (P = 0.03 and P = 0.01, resp.). Improvement in manual dexterity standard score was not associated with any baseline factors but was weakly correlated with the amount of visual acuity improvement (rs = 0.26; 95% CI, 0.09-0.41) CONCLUSIONS: Manual dexterity impairments are common among children with amblyopia. In our study cohort, binocular amblyopia treatment improved visual acuity and manual dexterity.

Efficacy of portable sleep monitoring device in diagnosing central sleep apnea in patients with congestive heart failure.

Central sleep apnea (CSA) is a common and serious comorbidity mainly occurring in patients with heart failure (HF), which tends to be underdiagnosed and has not been widely studied. Overnight polysomnography (PSG) is the gold standard for diagnosing CSA; however, the time and expense of the procedure limit its applicability. Portable monitoring (PM) devices are convenient and easy to use; however, they have not been widely studied as to their effectiveness in detecting CSA in patients with HF. In the current study, we examined the diagnostic value of PM as a screening tool to identify instances of CSA among patients with HF. A total of 22 patients under stable heart failure conditions with an ejection fraction of <50% were enrolled. All patients underwent PM and overnight PSG within a narrow time frame. The measurements of the apnea-hypopnea index (AHI), hypopnea index (HI), central apnea index (CAI), and obstructive apnea index (OAI) obtained from PSG, automatic scoring, and manual scoring of PM were recorded. The results obtained from PSG and those from PM (automatic and manual scoring) were compared to assess the accuracy of PM. Among the patients, CSA in 11 patients was found by PSG. The AHI measurements performed using manual scoring of PM showed a significant correlation with those performed using PSG (r = 0.69; P = 0.01). Nonetheless, mean AHI measurements showed statistically significant differences between PSG and automatic scoring of PM (40.0 vs. 23.7 events/hour, respectively; P < 0.001), as well as between automatic and manual scoring of PM (23.7 vs. 29.5 events/hour; P < 0.001). Central sleep apnea was detected by PM; however, the results were easily misread as obstructive apnea, particularly in automatic scoring. PM devices could be used to identify instances of central sleep apnea among patients with HF. The results from PM were well-correlated with standard PSG results, and manual scoring was preferable to automated scoring.

WSVAS: A YOLOv4 -based phenotyping platform for automatically detecting the salt tolerance of wheat based on seed germination vigour.

Salt stress is one of the major environmental stress factors that affect and limit wheat production worldwide. Therefore, properly evaluating wheat genotypes during the germination stage could be one of the effective ways to improve yield. Currently, phenotypic identification platforms are widely used in the seed breeding process, which can improve the speed of detection compared with traditional methods. We developed the Wheat Seed Vigour Assessment System (WSVAS), which enables rapid and accurate detection of wheat seed germination using the lightweight convolutional neural network YOLOv4. The WSVAS system can automatically acquire, process and analyse image data of wheat varieties to evaluate the response of wheat seeds to salt stress under controlled environments. The WSVAS image acquisition system was set up to continuously acquire images of seeds of four wheat varieties under three types of salt stress. In this paper, we verified the accuracy of WSVAS by comparing manual scoring. The cumulative germination curves of wheat seeds of four genotypes under three salt stresses were also investigated. In this study, we compared three models, VGG16 + Faster R-CNN, ResNet50 + Faster R-CNN and YOLOv4. We found that YOLOv4 was the best model for wheat seed germination target detection, and the results showed that the model achieved an average detection accuracy (mAP) of 97.59%, a recall rate (Recall) of 97.35% and the detection speed was up to 6.82 FPS. This proved that the model could effectively detect the number of germinating seeds in wheat. In addition, the germination rate and germination index of the two indicators were highly correlated with germination vigour, indicating significant differences in salt tolerance amongst wheat varieties. WSVAS can quantify plant stress caused by salt stress and provides a powerful tool for salt-tolerant wheat breeding.

1029: IMPROVING THE INTENSIVE CARE UNIT ROTATION EXPERIENCE: THE USE OF A MANUAL FOR TRAINEE ORIENTATION

Introduction: Clinical rotations for residents in the intensive care unit (ICU) could be challenging due to the complexity of cases, acuity of care, and lack of prior experience. An effective and timely rotation orientation may facilitate the need for rapid adjustment of trainees to their rotation roles and acknowledgement of expectations while increasing trainee rotation satisfaction (Gaetke-Udager 2015, Singer 2013). The aim of this study is to assess the utilization of an ICU orientation manual to facilitate the learning experience. Methods: Trainees scheduled for an ICU rotation during the academic year were provided with the manual prior to their first rotation. A prospective study was conducted in a community based medical ICU. All medical trainees assessed on their knowledge in workflow, clinical care, documentation, equipment and procedures prior to and after their ICU rotations. The level of satisfaction with the manual as an orientation tool and overall experience satisfaction were also assessed. Results: A total of 95 and 61 trainees completed the pre and post-rotation test respectively. There was a significant decrease in the median score, among paired samples, between the pre and post-test (41 [37-44] vs. 38 [34-41], p <.001). Pre-test scores were higher than post-test scores for emergency medicine (39.5 [36-43.5 vs. 33 [30-36.75], p< .01) and pulmonary and critical care (44 [39-46] vs. (39 [36-40], p<.03) trainees but not for internal medicine trainees. ICU manual training satisfaction score significantly correlated with the post-test score (r[59]=.320, p=.01) and strongly correlated with ICU rotation trainee satisfaction level (r[59] = .909, p<.001). Conclusions: Our study revealed lower ICU manual content retention scores in emergency medicine residents and pulmonary and critical care fellows, yet consistent scores among internal medicine residents. Trainees with higher ICU manual satisfaction levels as an orientation tool exhibited higher content retention and overall rotation satisfaction. Educators should consider a tailored orientation process among trainees based on their training program and consider reinforcing orientation after each academic year. Trainee satisfaction with ICU orientation material could be indicative of their overall ICU rotation satisfaction.

Modeling gait score of broiler chicken via production and behavioral data

Lameness in broilers may be associated with pain and is considered a major broiler production and welfare concern. Manual gait score assessment in commercial broiler houses is discrete, time-consuming, and laborious. As such, automatic methods for broiler gait score assessment are urgently needed. The objective of this study was to identify the relation of broiler gait score with several productions and behavioral metrics (bird BW, age, activity, and distribution), and establish three gait score prediction models for automatic gait score estimations in broiler farms with automatic weighing systems, camera systems, or both. Sixteen pens were used to rear Cobb 500 and Ross 708 broilers for eight and nine weeks, respectively (eight pens/strain, 12 birds/pen). The gait scores of all birds were assessed weekly by trained assessors following a six-point (0–5) scoring protocol from the third week. The pen’s average BW was measured weekly. Top-view cameras were installed to continuously record videos of broilers in all 16 pens. Images were extracted from video clips (10 min/hour) during a 16-hour light period to determine the activity index and distribution index through image processing. The gait score was positively correlated with BW (R2 = 0.97 for Cobb and R2 = 0.96 for Ross), while negatively correlated with activity (R2 = 0.78 for Cobb and R2 = 0.73 for Ross). The three models showed high accuracies in predicting broiler gait score based on variables of BW, age, activity index, and distribution index (R2 = 0.90–0.91, RMSE = 0.38–0.41). The findings of this study demonstrated the potential of estimating broiler gait score using bird BW, age, activity index, and distribution index. This information will assist in the development of automated gait score assessment systems in broiler production.

STAGETOOL, a Novel Automated Approach for Mouse Testis Histological Analysis.

Spermatogenesis is a complex differentiation process that takes place in the seminiferous tubules. A specific organization of spermatogenic cells within the seminiferous epithelium enables a synchronous progress of germ cells at certain steps of differentiation on the spermatogenic pathway. This can be observed in testis cross-sections where seminiferous tubules can be classified into distinct stages of constant cellular composition (12 stages in the mouse). For a detailed analysis of spermatogenesis, these stages have to be individually observed from testis cross-sections. However, the recognition of stages requires special training and expertise. Furthermore, the manual scoring is laborious considering the high number of tubule cross-sections that have to be analyzed. To facilitate the analysis of spermatogenesis, we have developed a convolutional deep neural network-based approach named "STAGETOOL." STAGETOOL analyses histological images of 4',6-diamidine-2'-phenylindole dihydrochloride (DAPI)-stained mouse testis cross-sections at ×400 magnification, and very accurately classifies tubule cross-sections into 5 stage classes and cells into 9 categories. STAGETOOL classification accuracy for stage classes of seminiferous tubules of a whole-testis cross-section is 99.1%. For cellular level analysis the F1 score for 9 seminiferous epithelial cell types ranges from 0.80 to 0.98. Furthermore, we show that STAGETOOL can be applied for the analysis of knockout mouse models with spermatogenic defects, as well as for automated profiling of protein expression patterns. STAGETOOL is the first fluorescent labeling-based automated method for mouse testis histological analysis that enables both stage and cell-type recognition. While STAGETOOL qualitatively parallels an experienced human histologist, it outperforms humans time-wise, therefore representing a major advancement in male reproductive biology research.

Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning

Judging the efficiency of agricultural machinery operations is the basis for evaluating the utilization rate of agricultural machinery, the driving abilities of operators, and the effectiveness of agricultural machinery management. A range of evaluative factors—including operational efficiency, oil consumption, operation quality, repetitive operation rate, and the proportion of effective operation time—must be considered for a comprehensive evaluation of the quality of a given operation, an analysis of the causes of impact, the improvement of agricultural machinery management and an increase in operational efficiency. In this study, the main factors affecting the evaluation of agricultural machinery operations are extracted, and information about the daily operations of particular items of agricultural machinery is taken as a data source. As regards modeling, a subset of data can be scored manually, and the remaining data is predicted after the training of the relevant model. With a large quantity of data, manual scoring is not only time-consuming and labor-intensive, but also produces sample errors due to subjective factors. However, a small number of samples cannot support an accurate evaluation model, and so in this study a semi-supervised learning method was used to increase the number of training samples and improve the accuracy of the least-squares support vector machine (LSSVM) training model. The experiment used 33,000 deep subsoiling operation data, 500 of which were used as training samples and 500 as test samples. The accuracy rate of the model obtained using 500 training samples was 94.43%, and the accuracy rate achieved with this method with an increased number of training samples was 96.83%. An optimal combination of agricultural machinery and tools is recommended owing to their operational benefits in terms of reduced costs and improved operating capacity.

Differentiation of central disorders of hypersomnolence with manual and artificial-intelligence-derived polysomnographic measures.

Differentiation of central disorders of hypersomnolence (DOH) is challenging but important for patient care. This study aimed to investigate whether biomarkers derived from sleep structure evaluated both by manual scoring as well as with artificial intelligence (AI) algorithms allow distinction of patients with different DOH. We included video-polysomnography data of 40 narcolepsy type 1 (NT1), 26 narcolepsy type 2 (NT2), 23 patients with idiopathic hypersomnia (IH) and 54 participants with subjective excessive daytime sleepiness (sEDS). Sleep experts manually scored sleep stages. A previously validated AI algorithm was employed to obtain automatic hypnograms and hypnodensity graphs (where each epoch is represented as a mixture of sleep stage probabilities). One-thousand-three features describing sleep architecture and instability were extracted from manual/automatic hypnogram and hypnodensity graphs. After feature selection, random forest classifiers were trained and tested in a 5-fold-cross-validation scheme to distinguish groups pairwise (NT1-vs-NT2, NT1-vs-IH, …) and single groups from the pooled remaining ones (NT1-vs-rest, NT2-vs-rest,…). The accuracy/F1-score values obtained in the test sets were: 0.74 ± 0.04/0.79 ± 0.05 (NT1-vs-NT2), 0.89 ± 0.09/0.91 ± 0.08 (NT1-vs-IH), 0.93 ± 0.06/0.91 ± 0.07 (NT1-vs-sEDS), 0.88 ± 0.04/0.80 ± 0.07 (NT1-vs-rest), 0.65 ± 0.10/0.70 ± 0.09 (NT2-vs-IH), 0.72 ± 0.12/0.60 ± 0.10 (NT2-vs-sEDS), 0.54 ± 0.19/0.38 ± 0.13 (NT2-vs-rest), 0.57 ± 0.11/0.35 ± 0.18 (IH-vs-sEDS), 0.71 ± 0.08/0.35 ± 0.10 (IH-vs-rest) and 0.76 ± 0.08/0.71 ± 0.13 (sEDS-vs-rest). The results confirm previous findings on sleep instability in patients with NT1 and show that combining manual and automatic AI-based sleep analysis could be useful for better distinction of NT2 from IH, but no precise sleep biomarker of NT2 or IH could be identified. Validation in a larger and multi-centric cohort is needed to confirm these findings.

Establishing the Correlation Between Complexity and Performance for Arrival Operations

Air traffic complexity indicators play an essential role in measuring operational performance and controller workload. However, current studies mainly depend on the manual scoring method to scale performance or workload. This paper focuses on arrival operations and presents a data-driven strategy to establish the correlation between complexity and performance to avoid the subjectivity of the currently used manual scoring method. Firstly, we present twenty-six indicators for describing air traffic complexity and two indicators for arrival operational performance. Secondly, the clustering method distinguishes peak and off-peak situations for arrival operation. Moreover, clustering results are compared to investigate the correlation between complexity and performance initially. Thirdly, the classification method is adopted to determine such correlation further. In addition, we also identify the affecting factors which could influence operational performance. Finally, trajectories of arrival aircraft landing at Guangzhou Baiyun International Airport (ZGGG) are used for case validation. The results indicate that there is a strong correlation between complexity and performance. The accuracy and precision of classification are approximately 90%. Furthermore, the number of aircraft significantly impacts the arrival operational performance within TMA.

Automating the verbal paired associates test to assess memory remotely and at scale

AbstractBackgroundVerbal neuropsychological tests are sensitive to the early signs of neurodegeneration in older adults. However, their dependence on in‐person testing and manual scoring make them unsuitable for large‐scale screening and home‐based monitoring. To address this, we have developed a fully automated verbal paired associate learning (VPA) test. Here we describe the program of work to refine and validate this task, and ensure suitability of the test for remote, repeat administration.MethodsTwo phases of data collection were carried out. In the first, data was collected from 3,264 participants aged 17–86 years (M = 34.5, SD = 12.32) at home and on their own devices. The automated VPA task comprises eight word‐pairs, learned over three attempts. Automatic speech recognition (ASR) scores responses in real‐time, using the Neurovocalix platform. Following this phase, the task was refined to better enable repeat testing. This included producing a large corpus of algorithmically generated pairs, controlling for important linguistic features (e.g. frequency and concreteness), and adding brief familiarisation to the first visit to increase reliability and stability. We assessed: 1) the accuracy of automated scoring 2) the impact of participant characteristics and task design on performance and consistency 3) participant feedback.ResultsData from the first phase showed excellent correspondence between human and automated scoring of VPA, with a correlation .93 (p &lt; 0.001), and the expected sensitivity to ageing number of task performance. Participant demographics and technology choice did not impact correspondence between ASR scoring and manual scoring. In the second phase data collection, we compared VPA performance in participants with (N = 190) and without (N = 186) prior familiarisation. Familiarisation dramatically improved consistency of participant performance, eliminating the significant effect of task order on performance (F &gt; 100, p &lt; 0.001). Participants reported that they found the task engaging and easy to understand, and repeated testing over five sessions in older adults was well tolerated.ConclusionsTogether, these results demonstrate that remote, automated, voice‐based, cognitive assessments are feasible and acceptable. These methods may therefore be useful in the context of home‐based screening or monitoring in remote clinical trials, enabling longitudinal assessment of memory in AD.

Automated Analysis of Written Language in the Three Variants of Primary Progressive Aphasia.

Despite the important role of written language in everyday life, abnormalities in functional written communication have been sparsely investigated in PPA. We developed a Python program which uses a language parser and quantifies content units (CU) and total utterances in language samples. The program was used to analyze written descriptions of the WAB Picnic scene, based on a pre-defined CU corpus, from a cohort with 95 participants (20 controls, 28 nonfluent variant Primary Progressive Aphasia (nfvPPA), 30 logopenic variant (lvPPA), 17 semantic variant (svPPA)). We then compared written functional communication between the three PPA variants and controls. We analyzed the measurements in relation to aphasia severity (Progressive Aphasia Severity Scale Sum of Boxes (PASS SoB) and Clinical Dementia Rating Supplemental Language (CDR-L) scores), and dementia severity (Clinical Dementia Rating Sum of Boxes (CDR SoB)). The reliability of the automated analysis was high as compared to manual scoring of 239 samples (99.6%). One-way ANOVAs revealed patients with lvPPA and svPPA wrote fewer CU than controls (mean+/-SD 11.97+/-7.87 and 10+/-4.5, vs 18.5+/-9.32) (F(3, 91)=[4.897], p=0.003). There was a trend towards a decrease in CU in nfvPPA (13.89+/-6.33) (p=0.144). Patients with all PPA subtypes (nfvPPA mean+/-SD 34.64+/-18.89; lvPPA 38+/-24.54; svPPA 28.88+/-20.36) wrote fewer total utterances than controls (61.7+/-27.86) (F(3, 91)=[7.778], p<0.001). The CU/utterance ratio was higher in nfvPPA and svPPA than in controls and lvPPA (0.42+/-0.14 and 0.43+/0.22 vs 0.3+/-0.05 and 0.32+/-0.12 respectively) (F(3, 91)=[5.592], p=0.001). Considering all PPA samples, regression analysis showed a decrease in CU relative to an increase in PASS SoB (R=0.24, p=0.04), CDR-L (R=0.22, p=0.06), and CDR SoB (R=0.34, p=0.004). As expected, we found impairments in informative written language in patients with PPA. Automated CU analysis is a tool that can improve assessment of functional written communication, and may be an objective tool to track aphasia progression. More studies are required to assess individual changes over time and whether these findings could be extrapolated to other forms of cognitive impairment.

Validation of a Three-Dimensional Computed Tomography Reconstruction Tool for Aortic Valve Calcium Quantification

BackgroundAortic valve calcification correlates with the severity of aortic valve stenosis and a high calcium score is associated with conduction disturbances and paravalvular leakage after transcatheter aortic valve replacement. The 3mensio Structural Heart is a semiautomated software package to facilitate aortic root analysis by multislice computed tomography.The aim of the contemporary study is to validate a semiautomated calcium quantification scoring tool with a conventional manual calcium quantification tool. MethodsFifty randomly selected patients who underwent multislice computed tomography for preprocedural planning were retrospectively selected to compare the semiautomated aortic valve Agatston calcium score by 3mensio with the manually obtained score using IntelliSpace Portal as standard reference. ResultsPatients had a mean age of 76.7 ± 7.4 years and 60% were male. The median Agatston score was 3390 [interquartile range 1877-4509] with 3mensio and 3434 [interquartile range 1839-4620] with IntelliSpace.The mean difference was -0.18 [95% confidence interval (CI) −53.8 to 53.4]. The intraclass correlation coefficient between the Agatston scores using IntelliSpace and 3mensio showed an excellent correlation of 0.995 [95% CI 0.992-0.997], p ≤ 0.001. The interobserver and intraobserver variability was 0.993 ([95% CI 0.961-0.998], p ≤ 0.001) and 0.995([95%CI 0.981-0.999], p = <0.001), respectively. ConclusionsThe semiautomated calcium quantification module in 3mensio Structural Heart highly correlated with a conventional manual calcium scoring tool.