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Abstract Approximately 75% of stroke survivors have movement dysfunction. Rehabilitation exercises are capable of improving physical coordination. They are mostly conducted in the home environment without guidance from therapists. It is impossible to provide timely feedback on exercises without suitable devices or therapists. Human action quality assessment in the home setting is a challenging topic for current research. In this paper, a low-cost HREA system in which wearable sensors are used to collect upper limb exercise data and a multichannel 1D-CNN framework is used to automatically assess action quality. The proposed 1D-CNN model is first pretrained on the UCI-HAR dataset, and it achieves a performance of 91.96%. Then, five typical actions were selected from the Fugl-Meyer Assessment Scale for the experiment, wearable sensors were used to collect the participants’ exercise data, and experienced therapists were employed to assess participants’ exercise at the same time. Following the above process, a dataset was built based on the Fugl-Meyer scale. Based on the 1D-CNN model, a multichannel 1D-CNN model was built, and the model using the Naive Bayes fusion had the best performance (precision: 97.26%, recall: 97.22%, F1-score: 97.23%) on the dataset. This shows that the HREA system provides accurate and timely assessment, which can provide real-time feedback for stroke survivors’ home rehabilitation.

Automatic assessment of the operation ability of operators based on computers is an essential approach for improving the effectiveness of equipment operation training and enhancing equipment safety. Present methods primarily focus on the operation results but pay less attention to the operation procedure. One reason is that there is a lack of a model that has the ability to describe all probable paths to accomplish the same task. Therefore, an operations chain model is put forward for the first time to describe the standard operation procedure and relationships among operations based on the decomposition of operational tasks and the relationships among the various operations required to fulfill the task. A specific operation task corresponds to an operations chain, which will form one or multiple standard operation sequences that will allow trainees to complete the same task through different paths. The Needleman–Wunsch sequence alignment algorithm is introduced to match the trainees' operation sequence with all standard sequences. The maximum alignment result is the score of the trainees' operations. An example shows that the operations chain model can accurately describe the complex structure of the standard operating procedures. The Needleman–Wunsch sequence alignment algorithm can objectively evaluate the trainee's operation capabilities. The combination of the operations chain model and sequence alignment algorithm can form a complete operation procedure assessment method that is friendlier to trainees and has more objective evaluation results. The method will help to improve the effectiveness of the competency assessment of equipment operators. [ABSTRACT FROM AUTHOR]

To accurately and timely evaluate the long‐term performance of cables in cable‐stayed bridges, an automatic perception technology scheme by on‐line monitoring of cable vibrations is proposed for the fatigue damage evaluation of in‐service cables. In the fatigue‐stress amplitude of cables, the stress produced by tension changes caused by external actions like traffic, wind, and temperature is the main component. When cables vibrate significantly, the stress caused by changes in cable vibration‐induced additional stress should not be neglected. Besides axial stress, bending stress is also significant in cable fatigue damage analysis. To make cable fatigue life prediction closer to real engineering scenarios, this factor should be considered. First, based on the cable dynamic stiffness theory, a method is proposed for the automatic gathering of the actual full‐stress time history of a cable by on‐line vibration monitoring. Furthermore, based on Miner's linear fatigue damage accumulation theory, an automatic fatigue life assessment method is proposed and applied to the vibration monitoring data of cables on an operational bridge. The results indicate that the proposed technology realizes automatic on‐line monitoring of cable forces and fatigue assessment of cables. Through statistical analysis of cable fatigue stress amplitude, it was determined that in cable‐stayed bridges, compared to long cables, short cables are more sensitive to external variable loads, typically experiencing larger and more frequent tension changes, and are more prone to fatigue. Therefore, short cables should be given more attention when analyzing cable fatigue in cable‐stayed bridges. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes an algorithm for the automatic assessment of programming exercises. The algorithm assigns assessment scores based on the program dependency graph structure and the program semantic similarity, but does not actually need to run the student’s program. By calculating the node similarity between the student’s program and the teacher’s reference programs in terms of structure and program semantics, a similarity matrix is generated and the optimal similarity node path of this matrix is identified. The proposed algorithm achieves improved computational efficiency, with a time complexity of $$O(n^2)$$ O ( n 2 ) for a graph with n nodes. The experimental results show that the assessment algorithm proposed in this paper is more reliable and accurate than several comparison algorithms, and can be used for scoring programming exercises in C/C++, Java, Python, and other languages.

Speech training apps are being developed that provide automatic feedback concerning children’s production of known target words, as a score on a 1–5 scale. However, this ‘goodness’ scale is still poorly understood. We investigated listeners’ ratings of ‘how many stars the app should provide as feedback’ on children’s utterances, and whether listener agreement is affected by clinical experience and/or access to anchor stimuli. In addition, we explored the association between goodness ratings and clinical measures of speech accuracy; the Percentage of Consonants Correct (PCC) and the Percentage of Phonemes Correct (PPC). Twenty speech-language pathologists and 20 non-expert listeners participated; half of the listeners in each group had access to anchor stimuli. The listeners rated 120 words, collected from children with and without speech sound disorder. Concerning reliability, intra-rater agreement was generally high, whereas inter-rater agreement was moderate. Access to anchor stimuli was associated with higher agreement, but only for non-expert listeners. Concerning the association between goodness ratings and the PCC/PPC, correlations were moderate for both listener groups, under both conditions. The results indicate that the task of rating goodness is difficult, regardless of clinical experience, and that access to anchor stimuli is insufficient for achieving reliable ratings. This raises concerns regarding the 1–5 rating scale as the means of feedback in speech training apps. More specific listener instructions, particularly regarding the intended context for the app, are suggested in collection of human ratings underlying the development of speech training apps. Until then, alternative means of feedback should be preferred. [ABSTRACT FROM AUTHOR]

Speech and language pathologists (SLPs) often rely on judgements of speech fluency for diagnosing or monitoring patients with aphasia. However, such subjective methods have been criticised for their lack of reliability and their clinical cost in terms of time. This study aims at assessing the relevance of a signal-processing algorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency in people with aphasia (PWA). Twenty-nine PWA and five control participants were recruited via non-profit organizations and SLP networks. All participants were recorded while reading out loud a set of sentences taken from the French version of the Boston Diagnostic Aphasia Examination. Three trained SLPs assessed the fluency of each sentence on a five-point qualitative scale. A forward-backward divergence segmentation and a clustering algorithm were used to compute, for each sentence, four automatic predictors of speech fluency: pseudo-syllable rate, speech ratio, rate of silent breaks, and standard deviation of pseudo-syllable length. The four predictors were finally combined into multivariate regression models (a multiple linear regression — MLR, and two non-linear models) to predict the average SLP ratings of speech fluency, using a leave-one-speaker-out validation scheme. All models achieved accurate predictions of speech fluency ratings, with average root-mean-square errors as low as 0.5. The MLR yielded a correlation coefficient of 0.87 with reference ratings at the sentence level, and of 0.93 when aggregating the data for each participant. The inclusion of an additional predictor sensitive to repetitions improved further the predictions with a correlation coefficient of 0.91 at the sentence level, and of 0.96 at the participant level. The algorithms used in this study can constitute a cost-effective and reliable tool for the assessment of the speech fluency of patients with aphasia in read-aloud tasks. Perspectives for the assessment of spontaneous speech are discussed. [ABSTRACT FROM AUTHOR]

Surgical experience is often reflected by efficient, fluid, and well-calculated movements. For a new trainee, learning these characteristics is possible only by observation as there is no quantification system to define these factors. We analyzed surgeons' hand movements with different experience levels to characterize their movements according to experience. Hand motions were recorded by an inertial measurement unit (IMU) mounted on the hands of the surgeons during a simulated surgical procedure. IMU data provided acceleration and Eulerian angles: yaw, roll, and pitch corresponding to hand motions as radial/ulnar deviation, pronation/supination, and extension/flexion, respectively. These variables were graphically depicted and compared between three surgeons. Participants were assigned to three groups based on years of surgical experience: group 1: >15 y; group 2: 3-10 y; and group 3: 0-1 y. Visualization of the roll motion, being the main motion during suturing, showed the clear difference in fluidity and regularity of the movements between the groups, showing minimal wasted movements for group 1. The angle of the roll motion, measured at the minimum, midpoint, and maximum points was significantly different between the groups. As expected, the experienced group completed the procedure first; however, the acceleration was not different between the groups. Surgeons' hand movements can be easily characterized and quantified by an IMU device for automatic assessment of surgical skills. These characteristics graphically visualize a surgeon's regularity, fluidity, economy, and efficiency. The characteristics of an experienced surgeon can serve as a training model and as a reference tool for trainees. [ABSTRACT FROM AUTHOR]

The trade-off between speed and accuracy in process-oriented tests of fundamental motor skills development has always been a challenge in motor development screening plans. Thus, this study was designed to evaluate the feasibility of using wearable inertial sensors (IMUs) based on artificial intelligence algorithms to assess kicking skill. Thirteen children aged 4 to 10 years (age = 8±1.37) (boys = 58%) participated in this study. The subjects were asked to do at least ten repetitions of the kicking skill according to the TGMD-3. Trials were captured with video recording and three wearable inertial sensors installed on the ankles and lower back. K-Nearest Neighbor artificial intelligence algorithms automatically classified the linear acceleration and angular velocity signals. The intraclass correlation coefficient (ICC) was calculated between expert scores and the artificial intelligence algorithm. All tests were done at a 95% confidence interval. The classification accuracy of the KNN algorithm (k=7) for kicking was 95%, ICC =0.90 (CI=0.86-0.95). The scoring time was reduced from 5 minutes per trial (in an expert-oriented way) to less than 30 seconds (using artificial intelligence). As a result, this method was a reliable and practical way to assess the fundamental motor skills. Also, by maintaining relative accuracy, it was possible to reduce test time for research, clinical, sports, and educational purposes. [ABSTRACT FROM AUTHOR]

Text-based open-ended questions in academic formative and summative assessments help students become deep learners and prepare them to understand concepts for a subsequent conceptual assessment. However, grading text-based questions, especially in large (>50 enrolled students) courses, is tedious and time-consuming for instructors. Text processing models continue progressing with the rapid development of Artificial Intelligence (AI) tools and Natural Language Processing (NLP) algorithms. Especially after breakthroughs in Large Language Models (LLM), there is immense potential to automate rapid assessment and feedback of text-based responses in education. This systematic review adopts a scientific and reproducible literature search strategy based on the PRISMA process using explicit inclusion and exclusion criteria to study text-based automatic assessment systems in post-secondary education, screening 838 papers and synthesizing 93 studies. To understand how text-based automatic assessment systems have been developed and applied in education in recent years, three research questions are considered: 1) What types of automated assessment systems can be identified using input, output, and processing framework? 2) What are the educational focus and research motivations of studies with automated assessment systems? 3) What are the reported research outcomes in automated assessment systems and the next steps for educational applications? All included studies are summarized and categorized according to a proposed comprehensive framework, including the input and output of the system, research motivation, and research outcomes, aiming to answer the research questions accordingly. Additionally, the typical studies of automated assessment systems, research methods, and application domains in these studies are investigated and summarized. This systematic review provides an overview of recent educational applications of text-based assessment systems for understanding the latest AI/NLP developments assisting in text-based assessments in higher education. Findings will particularly benefit researchers and educators incorporating LLMs such as ChatGPT into their educational activities.

Ensuring equity in education is a goal for sustainable development. Among the factors that hinder equity, socioeconomic status (SES) has the highest impact on learning Mathematics. This paper addresses the issue of equity at the secondary school level by proposing an approach based on adopting automatic formative assessment (AFA). Carefully designed mathematical activities with interactive feedback were experimented with a sample of 299 students of grade 8 for a school year. A control group of 257 students learned the same topics using traditional methodologies. Part of the sample belonged to low SES. The learning achievement was assessed through pre-and post-tests to understand if the adoption of AFA impacted learning and whether the results depended on the students’ SES. The results show a positive effect of the experimentation (effect size: 0.42). Moreover, the effect size of the experimentation restricted to the low-SES group is high (0.77). In the treatment group, the results do not depend on SES, while in the control group, they do, suggesting that AFA is an equitable approach while traditional instruction risks perpetuating inequalities.

Automatic assessment of the operation ability of operators based on computers is an essential approach for improving the effectiveness of equipment operation training and enhancing equipment safety. Present methods primarily focus on the operation results but pay less attention to the operation procedure. One reason is that there is a lack of a model that has the ability to describe all probable paths to accomplish the same task. Therefore, an operations chain model is put forward for the first time to describe the standard operation procedure and relationships among operations based on the decomposition of operational tasks and the relationships among the various operations required to fulfill the task. A specific operation task corresponds to an operations chain, which will form one or multiple standard operation sequences that will allow trainees to complete the same task through different paths. The Needleman–Wunsch sequence alignment algorithm is introduced to match the trainees’ operation sequence with all standard sequences. The maximum alignment result is the score of the trainees’ operations. An example shows that the operations chain model can accurately describe the complex structure of the standard operating procedures. The Needleman–Wunsch sequence alignment algorithm can objectively evaluate the trainee’s operation capabilities. The combination of the operations chain model and sequence alignment algorithm can form a complete operation procedure assessment method that is friendlier to trainees and has more objective evaluation results. The method will help to improve the effectiveness of the competency assessment of equipment operators.

BackgroundThe diagnostic performance and clinical validity of automatic intracoronary imaging (ICI) tools for atherosclerotic plaque assessment have not been systematically investigated so far.MethodsWe performed a scoping review including studies on automatic tools for automatic plaque components assessment by means of optical coherence tomography (OCT) or intravascular imaging (IVUS). We summarized study characteristics and reported the specifics and diagnostic performance of developed tools.ResultsOverall, 42 OCT and 26 IVUS studies fulfilling the eligibility criteria were found, with the majority published in the last 5 years (86% of the OCT and 73% of the IVUS studies). A convolutional neural network deep-learning method was applied in 71% of OCT- and 34% of IVUS-studies. Calcium was the most frequent plaque feature analyzed (26/42 of OCT and 12/26 of IVUS studies), and both modalities showed high discriminatory performance in testing sets [range of area under the curve (AUC): 0.91–0.99 for OCT and 0.89–0.98 for IVUS]. Lipid component was investigated only in OCT studies (n = 26, AUC: 0.82–0.86). Fibrous cap thickness or thin-cap fibroatheroma were mainly investigated in OCT studies (n = 8, AUC: 0.82–0.94). Plaque burden was mainly assessed in IVUS studies (n = 15, testing set AUC reported in one study: 0.70).ConclusionA limited number of automatic machine learning-derived tools for ICI analysis is currently available. The majority have been developed for calcium detection for either OCT or IVUS images. The reporting of the development and validation process of automated intracoronary imaging analyses is heterogeneous and lacks critical information.Systematic Review RegistrationOpen Science Framework (OSF), https://osf.io/nps2b/.Graphical AbstractCentral Illustration.

This study investigated the automatic segmentation and classification of mitral regurgitation (MR) and tricuspid regurgitation (TR) using a deep learning-based method, aiming to improve the efficiency and accuracy of diagnosis of valvular regurgitations. A VABC-UNet model was proposed consisting of VGG16 encoder, U-Net decoder, batch normalization, attention block and deepened convolution layer based on the U-Net backbone. Then, a VABC-UNet-based assessment framework was established for automatic segmentation, classification, and evaluation of valvular regurgitations. A total of 315 color Doppler echocardiography images of MR and/or TR in an apical four-chamber view were collected, including 35 images in the test dataset and 280 images in the training dataset. In comparison with the classic U-Net and VGG16-UNet models, the segmentation performance of the VABC-UNet model was evaluated via four metrics: Dice, Jaccard, Precision, and Recall. According to the features of regurgitation jet and atrium, the regurgitation could automatically be classified into MR or TR, and evaluated to mild, moderate, moderate–severe, or severe grade by the framework. The results show that the VABC-UNet model has a superior performance in the segmentation of valvular regurgitation jets and atria to the other two models and consequently a higher accuracy of classification and evaluation. There were fewer pseudo- and over-segmentations by the VABC-UNet model and the values of the metrics significantly improved (p < 0.05). The proposed VABC-UNet-based framework achieves automatic segmentation, classification, and evaluation of MR and TR, having potential to assist radiologists in clinical decision making of the regurgitations in valvular heart diseases. [ABSTRACT FROM AUTHOR]

Abstract Background Conventional approach to myocardial strain analysis relies on a software designed for the left ventricle (LV) which is complex and time-consuming and is not specific for right ventricular (RV) and left atrial (LA) assessment. This study compared this conventional manual approach to strain evaluation with a novel semi-automatic analysis of myocardial strain, which is also chamber-specific. Methods Two experienced observers used the AutoStrain software and manual QLab analysis to measure the LV, RV and LA strains in 152 healthy volunteers. Fifty cases were randomly selected for timing evaluation. Results No significant differences in LV global longitudinal strain (LVGLS) were observed between the two methods (-21.0% ± 2.5% vs. -20.8% ± 2.4%, p = 0.230). Conversely, RV longitudinal free wall strain (RVFWS) and LA longitudinal strain during the reservoir phase (LASr) measured by the semi-automatic software differed from the manual analysis (RVFWS: -26.4% ± 4.8% vs. -31.3% ± 5.8%, p

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In this paper, we focus on developing automatic assessment (AA) for a topic that has some difficulties in its practical assessment: object oriented programming (OOP). For evaluating that the OOP principles have been correctly applied to a real application, we use unit testing. In this paper, we focus on prioritizing that the students understand and apply correctly complex OOP principles and that they design properly the classes (including their relationships). In addition, we focus on the Python programming language rather than the typical previous works' focus in this area. Thus, we present a real case study of a practical assignment, in which the students have to implement characters for a video game. This assignment has the particularities and advantages that it is incremental and that it applies all four OOP principles within a single assignment. We also present its solution with the UML class diagram description. Furthermore, we provide unit testing for this case study and give general advice for generalizing the unit tests to other real case scenarios. Finally, we corroborate the effectiveness of our approach with positive student evaluations. [ABSTRACT FROM AUTHOR]

This study aims to develop an automatic assessment of after‐stroke dyskinesias degree by combining machine learning and near‐infrared spectroscopy (NIRS). Thirty‐five subjects were divided into five stages (healthy, patient: Brunnstrom stages 3, 4, 5, 6). NIRS was used to record the muscular hemodynamic responses from bilateral femoris (biceps brachii) muscles during passive and active upper (lower) limbs circular exercise. We used the D‐S evidence theory to conduct feature information fusion and established a Gradient Boosting DD‐MLP Net model, combining the dendrite network and multilayer perceptron, to realize automatic dyskinesias degree evaluation. Our model classified the upper limb dyskinesias with high accuracy: 98.91% under the passive mode and 98.69% under the active mode, and classified the lower limb dyskinesias with high accuracy: 99.45% and 99.63% under the passive and active modes, respectively. Our model combined with NIRS has great potential in monitoring the after‐stroke dyskinesias degree and guiding rehabilitation training. [ABSTRACT FROM AUTHOR]

Gross motor skills such as sitting, jumping, and running are activities that involve the large muscles of the human body. The Test of Gross Motor Development, or TGMD, is widely used by researchers, pediatricians, physiotherapists, and educators from different countries to assess these skills in children aged 3 to 11 years. An important part of the test is that the movement, performed by the children, needs to be recorded and assessed by two or more professionals. The assessment process is laborious and takes time, and its automation is one of the main points to be developed. In recent years, methods have been proposed to automate the assessment according to the TGMD. The hypothesis investigated in this work is that it is possible to induce a machine learning model to identify whether the movement executed by the child is correct, considering only the first criterion of the TGMD-3 jumping skill. The skeleton of the children was extracted using PoseNet. A dataset of 350 images of Brazilian children between 3 and 11 years old performing the preparatory movement for the jump was used. The experimental results show an accuracy of 84%.

Background: Conventional approach to myocardial strain analysis relies on a software designed for the left ventricle (LV) which is complex and time-consuming and is not specific for right ventricular (RV) and left atrial (LA) assessment. This study compared this conventional manual approach to strain evaluation with a novel semi-automatic analysis of myocardial strain, which is also chamber-specific. Methods: Two experienced observers used the AutoStrain software and manual QLab analysis to measure the LV, RV and LA strains in 152 healthy volunteers. Fifty cases were randomly selected for timing evaluation. Results: No significant differences in LV global longitudinal strain (LVGLS) were observed between the two methods (-21.0% ± 2.5% vs. -20.8% ± 2.4%, p = 0.230). Conversely, RV longitudinal free wall strain (RVFWS) and LA longitudinal strain during the reservoir phase (LASr) measured by the semi-automatic software differed from the manual analysis (RVFWS: -26.4% ± 4.8% vs. -31.3% ± 5.8%, p < 0.001; LAS: 48.0% ± 10.0% vs. 37.6% ± 9.9%, p < 0.001). Bland–Altman analysis showed a mean error of 0.1%, 4.9%, and 10.5% for LVGLS, RVFWS, and LASr, respectively, with limits of agreement of -2.9,2.6%, -8.1,17.9%, and -12.3,33.3%, respectively. The semi-automatic method had a significantly shorter strain analysis time compared with the manual method. Conclusions: The novel semi-automatic strain analysis has the potential to improve efficiency in measurement of longitudinal myocardial strain. It shows good agreement with manual analysis for LV strain measurement. [ABSTRACT FROM AUTHOR]

Introductory programming is a basic course compulsory for students majoring in computer studies. This course is considered a difficult course to learn since time immemorial. Starting from 2020, measures taken by the Malaysian government in dealing with the COVID-19 pandemic has resulted in the educational institutions to be closed to students and face-to-face lessons replaced with online classes. Therefore, the process of learning programming becomes increasingly difficult since the instructors are unable to have face-to-face interaction with neither their local nor their international students during online classes. This paper aims to implement CodeZinger, used as an initiative to replace physical laboratory classes, and is used in monitoring the students' achievement. In this study, the application of CodeZinger was made on two programming courses involving 266 students of Year 1, namely students taking the Computer Programming course (semester 1), and Data Structure course (semester 2). The diverse test data provided by the instructors made the students more skilful and critical in doing programming, and easier for students due to the automatic assessment function provided in CodeZinger. This study's findings greatly influence students' motivation in learning programming, considering that CodeZinger allows prompt feedback and automatic assessment. Moreover, for the instructors' view of point, CodeZinger allows instructors to manage and identify students who need extra assistance in programming. In conclusion, CodeZinger assisted the students in optimizing the management of learning programming where CodeZinger provides the solution for problems and obstacles in face-to-face learning, facilitated the students in learning at their own pace, and facilitated the instructors in monitoring the students' tasks. [ABSTRACT FROM AUTHOR]

Abstract Hand function assessment is an essential component of the process of stroke rehabilitation because of the high incidence of hand motor dysfunction. In terms of the manual evaluation of hand function, the Fugl‐Meyer scale is a recommended scale with high reliability and validity. However, the need for accurate assessments and increasing developments in technology has led to the promotion of automatic quantitative assessment systems for the hand. In this study, we collected quantitative data on hand function with an automatic system and the upper limb Fugl‐Meyer assessment (FMA) from 79 people with stroke. We developed decision tree (DT) and gradient‐boosted decision tree (GBDT) predictive models for the Fugl‐Meyer score using features extracted from the Hand Automatic Quantitative Assessment System (HAQAS). Predictive performances were compared between these models regarding the predictive accuracy and Cohen's kappa. There were high correlations between features automatically collected by the HAQAS and the Fugl‐Meyer scale in all the sub‐items, with the maximal correlations all being over 0.5, indicating the high validity of the HAQAS in automatic FMA prediction. Hand functions were more highly correlated (average correlation coefficient 0.90) with HAQAS features than wrist functions (average correlation coefficient 0.54), and the GBDT achieved higher predictive accuracies and agreement than the DT algorithm. We conclude that the HAQAS is feasible for stroke patients with hand dysfunction and convenient for clinicians and therapists. This study was registered in the Chinese Clinical Trial Registry (ChiCTR1800019098).

Background: Left ventricular (LV) apical sparing by transthoracic echocardiography (TTE) has not been widely accepted to diagnose transthyretin amyloid cardiomyopathy (ATTR-CM), because it is time consuming and requires a level of expertise. We hypothesized that automatic assessment may be the solution for these problems. Methods-and-Results: We enrolled 63 patients aged ≥70 years who underwent 99mTc-labeled pyrophosphate (99mTc-PYP) scintigraphy on suspicion of ATTR-CM and performed TTE by EPIQ7G, and had enough information for two-dimensional speckle tracking echocardiography at Kumamoto University Hospital from January 2016 to December 2019. LV apical sparing was described as a high relative apical longitudinal strain (LS) index (RapLSI). Measurement of LS was repeated using the same apical images with three different measurement packages as follows: (1) full-automatic assessment, (2) semi-automatic assessment, and (3) manual assessment. The calculation time for full-automatic assessment (14.7 ± 1.4 sec/patient) and semi-automatic assessment (66.7 ± 14.4 sec/patient) were significantly shorter than that for manual assessment (171.2 ± 59.7 sec/patient) (p

Enhancement the methodology for assessing the digital accessibility of e-learning is an important condition for improving the quality of modern educational services. In order to develop a basic algorithm for auditing digital accessibility, the expert data from 173 e-learning resources (56 Massive Open Online Courses (MOOCs) in mathematics, 65 MOOCs in computer science and programming, 22 intra-university online courses in mathematics, computer science and programming, 30 MOOCs in cardiopulmonary resuscitation) were systematized and analyzed. The paper considers: methods and process of collecting expert data, the content of data sets, the procedure for empirical analysis of the results of testing the e-learning content accessibility. The structure of the proposed algorithm of auditing the digital accessibility includes the following stages: preparation for examination; automatic and expert testing of digital accessibility and generation of data sets; data analysis; formulating a final conclusion and recommendations for improving digital accessibility. Further use of the basic algorithm and data sets of audits can be useful for the development of accessible education, the training of e-learning specialists, and the strengthening of regulatory and control mechanisms in the field of education.

Background: Right heart catheterization is the gold standard for evaluating hemodynamic parameters of pulmonary circulation, especially pulmonary artery pressure (PAP) for diagnosis of pulmonary hypertension (PH). However, the invasive and costly nature of RHC limits its widespread application in daily practice. Purpose: To develop a fully automatic framework for PAP assessment via machine learning based on computed tomography pulmonary angiography (CTPA). Materials and Methods: A machine learning model was developed to automatically extract morphological features of pulmonary artery and the heart on CTPA cases collected between June 2017 and July 2021 based on a single center experience. Patients with PH received CTPA and RHC examinations within 1 week. The eight substructures of pulmonary artery and heart were automatically segmented through our proposed segmentation framework. Eighty percent of patients were used for the training data set and twenty percent for the independent testing data set. PAP parameters, including mPAP, sPAP, dPAP, and TPR, were defined as ground-truth. A regression model was built to predict PAP parameters and a classification model to separate patients through mPAP and sPAP with cut-off values of 40 mm Hg and 55 mm Hg in PH patients, respectively. The performances of the regression model and the classification model were evaluated by analyzing the intraclass correlation coefficient (ICC) and the area under the receiver operating characteristic curve (AUC). Results: Study participants included 55 patients with PH (men 13; age 47.75 ± 14.87 years). The average dice score for segmentation increased from 87.3% ± 2.9 to 88.2% ± 2.9 through proposed segmentation framework. After features extraction, some of the AI automatic extractions (AAd, RVd, LAd, and RPAd) achieved good consistency with the manual measurements. The differences between them were not statistically significant (t = 1.222, p = 0.227; t = −0.347, p = 0.730; t = 0.484, p = 0.630; t = −0.320, p = 0.750, respectively). The Spearman test was used to find key features which are highly correlated with PAP parameters. Correlations between pulmonary artery pressure and CTPA features show a high correlation between mPAP and LAd, LVd, LAa (r = 0.333, p = 0.012; r = −0.400, p = 0.002; r = −0.208, p = 0.123; r = −0.470, p = 0.000; respectively). The ICC between the output of the regression model and the ground-truth from RHC of mPAP, sPAP, and dPAP were 0.934, 0.903, and 0.981, respectively. The AUC of the receiver operating characteristic curve of the classification model of mPAP and sPAP were 0.911 and 0.833. Conclusions: The proposed machine learning framework on CTPA enables accurate segmentation of pulmonary artery and heart and automatic assessment of the PAP parameters and has the ability to accurately distinguish different PH patients with mPAP and sPAP. Results of this study may provide additional risk stratification indicators in the future with non-invasive CTPA data. [ABSTRACT FROM AUTHOR]

This study investigated the automatic segmentation and classification of mitral regurgitation (MR) and tricuspid regurgitation (TR) using a deep learning-based method, aiming to improve the efficiency and accuracy of diagnosis of valvular regurgitations. A VABC-UNet model was proposed consisting of VGG16 encoder, U-Net decoder, batch normalization, attention block and deepened convolution layer based on the U-Net backbone. Then, a VABC-UNet-based assessment framework was established for automatic segmentation, classification, and evaluation of valvular regurgitations. A total of 315 color Doppler echocardiography images of MR and/or TR in an apical four-chamber view were collected, including 35 images in the test dataset and 280 images in the training dataset. In comparison with the classic U-Net and VGG16-UNet models, the segmentation performance of the VABC-UNet model was evaluated via four metrics: Dice, Jaccard, Precision, and Recall. According to the features of regurgitation jet and atrium, the regurgitation could automatically be classified into MR or TR, and evaluated to mild, moderate, moderate–severe, or severe grade by the framework. The results show that the VABC-UNet model has a superior performance in the segmentation of valvular regurgitation jets and atria to the other two models and consequently a higher accuracy of classification and evaluation. There were fewer pseudo- and over-segmentations by the VABC-UNet model and the values of the metrics significantly improved (p < 0.05). The proposed VABC-UNet-based framework achieves automatic segmentation, classification, and evaluation of MR and TR, having potential to assist radiologists in clinical decision making of the regurgitations in valvular heart diseases.

In the context of an aging population, tools to help elderly to live independently must be developed. The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavioral data to automatically detect one of the most common consequences of aging: functional health decline. After gathering the longitudinal smart home data of 29 older adults for an average of >2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing 10 behavioral features. Using this data, we created regression models to predict absolute and standardized functional health scores, as well as classification models to detect reliable absolute change and positive and negative fluctuations in everyday functioning. Functional health was assessed every six months by means of the Instrumental Activities of Daily Living-Compensation (IADL-C) scale. Results show that total IADL-C score and subscores can be predicted by means of activity-aware smart home data, as well as a reliable change in these scores. Positive and negative fluctuations in everyday functioning are harder to detect using in-home behavioral data, yet changes in social skills have shown to be predictable. Future work must focus on improving the sensitivity of the presented models and performing an in-depth feature selection to improve overall accuracy.

Automated metrics for machine translation (MT) such as BLEU are customarily used because they are quick to compute and sufficiently valid to be useful in MT assessment. Whereas the instantaneity and reliability of such metrics are made possible by automatic computation based on predetermined algorithms, their validity is primarily dependent on a strong correlation with human assessments. Despite the popularity of such metrics in MT, little research has been conducted to explore their usefulness in the automatic assessment of human translation or interpreting. In the present study, we therefore seek to provide an initial insight into the way MT metrics would function in assessing spoken-language interpreting by human interpreters. Specifically, we selected five representative metrics – BLEU, NIST, METEOR, TER and BERT – to evaluate 56 bidirectional consecutive English–Chinese interpretations produced by 28 student interpreters of varying abilities. We correlated the automated metric scores with the scores assigned by different types of raters using different scoring methods (i.e., multiple assessment scenarios). The major finding is that BLEU, NIST, and METEOR had moderate-to-strong correlations with the human-assigned scores across the assessment scenarios, especially for the English-to-Chinese direction. Finally, we discuss the possibility and caveats of using MT metrics in assessing human interpreting. [ABSTRACT FROM AUTHOR]

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