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Studies have demonstrated that high-speed jaw-opening exercises are effective in improving swallowing function. However, there has been no objective tool available for monitoring jaw-opening pace. This study aimed to develop an objective tool for monitoring and validating jaw-opening pace and compare it between young and old ages from different age groups. A load cell plug-in jaw pad connected to an automatic recording and analysis system was used to record jaw-opening motions for offline analysis. We recruited 58 healthy volunteers from different age groups (20–39 y/o; 40-59y/o; 60-79y/o). During a 2-min recording session, each participant was instructed to fully open and close their jaw as quickly as possible while wearing a sensor. Bland-Altman plot, paired t -test and Pearson's correlation test were used to compare the number of jaw-opening motions between manual counting and automatic software analysis. The number of jaw-opening motions during the 2-min recording was compared between the three age groups. Automated analysis of jaw-opening pace was efficient and equally comparable with the traditional manual counting method across the three age groups. A declining trend in jaw-opening pace among the old age group was found but with no statistically significant difference. A jaw-opening motion monitoring tool with reliable automatic pace analysis software was validated in young and old ages The jaw-opening pace demonstrated a tendency to decline with age. [ABSTRACT FROM AUTHOR]

Background: Studies have demonstrated that high-speed jaw-opening exercises are effective in improving swallowing function. However, there has been no objective tool available for monitoring jaw-opening pace. This study aimed to develop an objective tool for monitoring and validating jaw-opening pace and compare it between young and old ages from different age groups. Materials and methods: A load cell plug-in jaw pad connected to an automatic recording and analysis system was used to record jaw-opening motions for offline analysis. We recruited 58 healthy volunteers from different age groups (20–39 y/o; 40-59y/o; 60-79y/o). During a 2-min recording session, each participant was instructed to fully open and close their jaw as quickly as possible while wearing a sensor. Bland-Altman plot, paired t-test and Pearson's correlation test were used to compare the number of jaw-opening motions between manual counting and automatic software analysis. The number of jaw-opening motions during the 2-min recording was compared between the three age groups. Results: Automated analysis of jaw-opening pace was efficient and equally comparable with the traditional manual counting method across the three age groups. A declining trend in jaw-opening pace among the old age group was found but with no statistically significant difference. Conclusion: A jaw-opening motion monitoring tool with reliable automatic pace analysis software was validated in young and old ages The jaw-opening pace demonstrated a tendency to decline with age.

Copyright of e-Education Research is the property of Northwest Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, there has been a rapid growth of online learning in higher education. Apart from professional online course platforms, many online video sharing websites have also provided online learning opportunities for college students. One of the most popular websites among college students in China is Bilibili, a Shanghai-based Chinese video sharing website known for its danmaku commenting system. This system enables users to post scrolling comments synchronized with the video timeline while the video is playing. Which attracts young students due to the lively user interaction. As a result, an increasing number of Chinese students are utilizing online courses on Bilibili as a supplementary learning resource alongside traditional classroom learning. Despite its popularity, online learning faces the challenge of students' lack of participation more than traditional face-to-face learning does. To understand their learning involvement, we propose a novel danmaku-based automatic analysis model that extracts three dimensions of online learning engagement using the Text Mind software. This model enables us to understand the students' learning patterns both as clusters and as individuals. Based on the model results, we present corresponding intervention strategies for different types of students based on their individual engagement characteristics. [ABSTRACT FROM AUTHOR]

The text information processing technology of public health service is one of the hot research topics at present. To improve the defects of public health service texts, such as inaccurate word segmentation, spelling errors and professional vocabulary understanding, this study designed a character-level deep neural network model on the characteristics of public health service texts. In this model, the bidirectional short and short time memory and the attention pooling operation layer are introduced to make the model better classify the text according to the context. In addition, counter perturbation is introduced in this study to improve the robustness and generalization ability of the model, thus improving its classification effect. The performance verification results show that the proposed model has better classification performance on the public health service text data set. The anti-disturbance samples generated by the model are all in the range of 0–0.2 when WMD deviation degree is measured, while most of the other methods are in the range of 0.4–0.6. The experimental object of this study is ultrasonic examination data. The experimental results show that the automatic analysis model of public health service text based on character level convolutional neural network constructed in this study has excellent accuracy and convergence speed, and has excellent performance in the classification of public health service text in different subject areas. [ABSTRACT FROM AUTHOR]

Background: Recent evidence suggests that speech substantially changes in ageing. As a complex neurophysiological process, it can accurately reflect changes in the motor and cognitive systems underpinning human speech. Since healthy ageing is not always easily discriminable from early stages of dementia based on cognitive and behavioural hallmarks, speech is explored as a preclinical biomarker of pathological itineraries in old age. A greater and more specific impairment of neuromuscular activation, as well as a specific cognitive and linguistic impairment in dementia, unchain discriminating changes in speech. Yet, there is no consensus on such discriminatory speech parameters, neither on how they should be elicited and assessed. Aims: To provide a state‐of‐the‐art on speech parameters that allow for early discrimination between healthy and pathological ageing; the aetiology of these parameters; the effect of the type of experimental stimuli on speech elicitation and the predictive power of different speech parameters; and the most promising methods for speech analysis and their clinical implications. Methods & Procedures: A scoping review methodology is used in accordance with the PRISMA model. Following a systematic search of PubMed, PsycINFO and CINAHL, 24 studies are included and analysed in the review. Main Contribution: The results of this review yield three key questions for the clinical assessment of speech in ageing. First, acoustic and temporal parameters are more sensitive to changes in pathological ageing and, of these two, temporal variables are more affected by cognitive impairment. Second, different types of stimuli can trigger speech parameters with different degree of accuracy for the discrimination of clinical groups. Tasks with higher cognitive load are more precise in eliciting higher levels of accuracy. Finally, automatic speech analysis for the discrimination of healthy and pathological ageing should be improved for both research and clinical practice. Conclusions & Implications: Speech analysis is a promising non‐invasive tool for the preclinical screening of healthy and pathological ageing. The main current challenges of speech analysis in ageing are the automatization of its clinical assessment and the consideration of the speaker's cognitive background during evaluation. WHAT THIS PAPER ADDS: What is already known on the subject: Societal aging goes hand in hand with the rising incidence of ageing‐related neurodegenerations, mainly Alzheimer's disease (AD). This is particularly noteworthy in countries with longer life expectancies. Healthy ageing and early stages of AD share a set of cognitive and behavioural characteristics. Since there is no cure for dementias, developing methods for accurate discrimination of healthy ageing and early AD is currently a priority. Speech has been described as one of the most significantly impaired features in AD. Neuropathological alterations in motor and cognitive systems would underlie specific speech impairment in dementia. Since speech can be evaluated quickly, non‐invasively and inexpensively, its value for the clinical assessment of ageing itineraries may be particularly high. What this paper adds to existing knowledge: Theoretical and experimental advances in the assessment of speech as a marker of AD have developed rapidly over the last decade. Yet, they are not always known to clinicians. Furthermore, there is a need to provide an updated state‐of‐the‐art on which speech features are discriminatory to AD, how they can be assessed, what kind of results they can yield, and how such results should be interpreted. This article provides an updated overview of speech profiling, methods of speech measurement and analysis, and the clinical power of speech assessment for early discrimination of AD as the most common cause of dementia. What are the potential or actual clinical implications of this work?: This article provides an overview of the predictive potential of different speech parameters in relation to AD cognitive impairment. In addition, it discusses the effect that the cognitive state, the type of elicitation task and the type of assessment method may have on the results of the speech‐based analysis in ageing. [ABSTRACT FROM AUTHOR]

Automatic wolf pack hunting behavior analysis contributes to the identification of behavioral mechanism and the development of bio-swarm intelligence engineering. However, wolf pack hunting commonly involves a complicated background and high-speed motion, where the frequent interactions with each other lead to frequent visual occlusion of the individual wolves. These difficulties make the automatic analysis of group hunting behavior significantly more challenging. Hence, we develop an automatic wolf pack hunting behavior detection scheme appropriate for videos, comprising a four-level individual feature map (frame, video, space, and semantic) and a group feature map. We propose a temporal–spatial feature fusion-based motion state recognition algorithm appropriate for scale-varied and occlusion-frequent individuals to obtain a robust semantic-level feature. Based on this individual feature map, we propose a video-based wolf pack hunting automatic behavior detection method. The developed scheme is validated on our Wolf2022 dataset, while the proposed motion state recognition and group behavior detection algorithms are further tested through ablation experiments. The results revealed that the motion state recognition accuracy reaches 88%, correctly detecting 15 out of 17 group behavior video clips. Significance statement: It is difficult to fast extract quantitative analysis results of wolf pack hunting behavior from video data. Our research focused on the research of the automatic analysis method for wolf pack hunting. Using the proposed method, the statical individual and group behavior attributes can be automatically generated from video data, which contributes to the building of a conceptual bridge between the wolf pack hunting behavior and bio-swarm intelligence engineering. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Visual Analytics approach allows driving informed and effective decision-making. It assists decision-makers to visually interact with large amount of data and to computationally learn valuable hidden patterns in that data, which improve the decision quality. In this article, we introduce an enhanced visual analytics model combining cognitive-based visual analysis to data mining-based automatic analysis. As emotions are strongly related to human behaviour and society, emotion prediction is widely considered by decision making activities. Unlike speech and facial expressions modalities, EEG (electroencephalogram) has the advantage of being able to record information about the internal emotional state that is not always translated by perceptible external manifestations. For this reason, we applied the proposed cognitive approach on EEG data to demonstrate its efficiency for predicting emotional reaction to films. For automatic analysis, we developed the Echo State Network (ESN) technique considered as an efficient machine learning solution due to its straightforward training procedure and high modelling ability for handling time-series problems. Finally, utility and usability tests were performed to evaluate the developed prototype. [ABSTRACT FROM AUTHOR]

Background. Digital technologies are increasingly used in agriculture to solve a variety of problems. However, in horticulture and industrial production of fruit and berry crops, qualitative evaluation and scoring of the most important morphological indicators of fruits are still common, and measurements are carried out manually. The aim of this study was to develop an algorithm for using the ImageJ package for quick and accurate measurements of the size and shape of berries.Materials and methods. The material included 190 berries of 3 blue honeysuckle (Lonicera caerulea L.) cultivars: ‘Amazonka’, ‘Lazurit’, and ‘Lenita’. The berries were laid out on a white sheet of paper with a ruler on top of the glass and photographed with additional lighting from below. The analysis of the obtained images was carried out using the public domain package ImageJ (v. 1.51k) and included automatic search for objects and their measurement by 7 indicators: area, perimeter, maximum and minimum Feret diameters, aspect ratio, circularity, and surface roundness (1-Solidity). Statistical analysis included the calculation of the minimum, maximum and mean values with a nonparametric 95% CI (bootstrap, percentile method), comparison of cultivars using the Kruskal–Wallis test, and search for the most typical objects based on the results of a between-group PCA.Results. It was shown how the size and shape indicators from the ImageJ package related to classical measurements in pomology, including length, diameter, and berry shape index. For all indicators, the differences between cultivars were highly statistically significant (p < 0.001). The prospects of using the surface roughness index for quantitative characterization of the degree of deviation of fruits from their natural shape due to mechanical and other deformations are discussed.Conclusion. The results of the automatic image analysis in the ImageJ package can be used in horticulture, breeding, and production of fruits and berries.

Current methods to detect eating behavior events (i.e., bites, chews, and swallows) lack objective measurements, standard procedures, and automation. The video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we reviewed the current methods to automatically detect eating behavior events from video recordings. According to PRISMA guidelines, publications from 2010–2021 in PubMed, Scopus, ScienceDirect, and Google Scholar were screened through title and abstract, leading to the identification of 277 publications. We screened the full text of 52 publications and included 13 for analysis. We classified the methods in five distinct categories based on their similarities and analyzed their accuracy. Facial landmarks can count bites, chews, and food liking automatically (accuracy: 90%, 60%, 25%). Deep neural networks can detect bites and gesture intake (accuracy: 91%, 86%). The active appearance model can detect chewing (accuracy: 93%), and optical flow can count chews (accuracy: 88%). Video fluoroscopy can track swallows but is currently not suitable beyond clinical settings. The optimal method for automated counts of bites and chews is facial landmarks, although further improvements are required. Future methods should accurately predict bites, chews, and swallows using inexpensive hardware and limited computational capacity. Automatic eating behavior analysis will allow the study of eating behavior and real-time interventions to promote healthy eating behaviors. [ABSTRACT FROM AUTHOR]

The assessment of the knee alignment using standing weight-bearing full-leg radiographs (FLR) is a standardized method. Determining the load-bearing axis of the leg requires time-consuming manual measurements. The aim of this study is to develop and validate a novel algorithm based on artificial intelligence (AI) for the automated assessment of lower limb alignment. In the first stage, a customized mask-RCNN model was trained to automatically detect and segment anatomical structures and implants in FLR. In the second stage, four region-specific neural network models (adaptations of UNet) were trained to automatically place anatomical landmarks. In the final stage, this information was used to automatically determine five key lower limb alignment angles. For the validation dataset, weight-bearing, antero-posterior FLR were captured preoperatively and 3 months postoperatively. Preoperative images were measured by the operating orthopedic surgeon and an independent physician. Postoperative images were measured by the second rater only. The final validation dataset consisted of 95 preoperative and 105 postoperative FLR. The detection rate for the different angles ranged between 92.4% and 98.9%. Human vs. human inter-(ICCs: 0.85–0.99) and intra-rater (ICCs: 0.95–1.0) reliability analysis achieved significant agreement. The ICC-values of human vs. AI inter-rater reliability analysis ranged between 0.8 and 1.0 preoperatively and between 0.83 and 0.99 postoperatively (all p < 0.001). An independent and external validation of the proposed algorithm on pre- and postoperative FLR, with excellent reliability for human measurements, could be demonstrated. Hence, the algorithm might allow for the objective and time saving analysis of large datasets and support physicians in daily routine. [ABSTRACT FROM AUTHOR]

Clean steel non-metallic inclusions have a significant effect on the physic and mechanical as well as technological properties of the metal and consequently on the quality of the final product. There are many methods for detecting and evaluating nonmetallic inclusions. Traditional methods for assessing the contamination of metal with microinclusions are metallographic methods, which are divided into methods for comparing the contamination of the section with inclusions with reference scales and counting methods. The classification of inclusions by traditional methods on a polished section is based on morphological features: shape, color, aspect ratio, etc. In addition to traditional optical microscopy, an electron microscope is used for studies that are more detailed. Equipping an electron microscope with an attachment for energy-dispersive microanalysis allows classifying nonmetallic inclusions by chemical classes. As a result of the implementation of a set of organizational and technical measures, BMZ, the management company of BMK Holding, installed and put into operation research equipment in December 2020 – a scanning electron microscope with an energy-dispersive microanalyzer (SEM with EMF). Thanks to the software module that the microanalyzer is equipped with, the possibility of automatic analysis of non-metallic inclusions is implemented. The software settings include the search for non-metallic inclusions and their classification according to widely used international standards. The flexible configuration of the software allows you to create models for separating inclusions into different user classes.

The article presents approaches to the automatic detection of sound repetitions and the measurement of sound coherence in Russian poetic text basing on the syllabocentric concept of the sound texture of the verse, which distinguishes the phonosyllabeme as its main operational unit and determines the main types of relations between the elements of repetition - equiphony and metaphony - and also allows describe the text in its unity of sound-segmental and rhythmic structure. The paper considers the linguistic prerequisites, main algorithms and methods, which form the basis of the PHONOTEXT computer program (web application), which automatically analyzes and visually forms the picture of the sound composition of the text presenting its quantitative characteristics. The basic operations of the program are described in detail, as well as the mathematical formulas used to identify the structure and carry out mathematical measurement of sound repetitions in Russian poetic speech. The article discusses the results of the programs use for the comparative study of poetry and prose, individual works and individual styles of Russian poets. In particular, the data obtained using the Phonotext program allows us to speak of a tendency towards syntagmatic consolidation of the phonosyllabeme and intensive granulation of the sound texture of the text in the individual style of Osip Mandelstam as the evidence of his striving for poetic morphologization of the elements of sound repetition, it also shows a tendency towards the use of the dominant sound theme as a means for cohesion of a complex syntactic and compositional whole, in contrast to Vladimir Nabokovs idiostyle, which is characterized by its orientation towards splitting up the elements of repetition, with a relatively chaotic interaction and spreading of consonances over the text and their low activity as a means of syntagmatic segmentation and consolidation of the text. The prospects for the development of the Phonotext program are described for identifying the relations of segment-sound and prosodic parallelism in speech (relations of equirhythmy and equiphony, including rhyme), which will provide an automatic presentation of a complete picture of the sound composition, the interaction of its main forms in the text and its further functional description.

In the field of open-pit mining, the key to ingoptimizing the blasting design is to automatically and accurately obtain the ore fragmentation information of the blasting pile, because of the problem that the current fragmentation analysis system cannot automatically acquire and automatically process images in batches, this paper proposes an automatic fragmentation analysis system of ore based on deep learning. The system is mainly composed of an automatic acquisition subsystem based on the MobileNet classification model and an automatic analysis subsystem based on U-Net semantic segmentation model. The system takes pictures of an electric dump truck unloading at the crushing station automatically and continuously and uploads them to the cloud platform by 4G network for automatic analysis of fragmentation information. Kvaluating the classification model and segmentation model qualitatively and quantitatively, the accuracy of the classification model on the test set reaches 98. 08%, and the ore category IoU of the ore segmentation model reaches 78.43%. The system has been running at the cycle station in the open-pit mining area of a mine for more than a year. The results show that the system meets the design requirements, and realizes the automation and intellectualization of the whole process from collection to analysis and information display, which can further provide data support for intelligent blasting. [ABSTRACT FROM AUTHOR]

Background: The right ventricle (RV) plays a pivotal role in cardiovascular diseases and 3-dimensional echocardiography (3DE) has gained acceptance for the evaluation of RV volumes and function. Recently, a new artificial intelligence (AI)–based automated 3DE software for RV evaluation has been proposed and validated against cardiac magnetic resonance. The aims of this study were three-fold: (i) feasibility of the AI-based 3DE RV quantification, (ii) comparison with the semi-automatic 3DE method and (iii) assessment of 2-dimensional echocardiography (2DE) and strain measurements obtained automatically. Methods: A total of 203 subject (122 normal and 81 patients) underwent a 2DE and both the semi-automatic and automatic 3DE methods for Doppler standard, RV volumes and ejection fraction (RVEF) measurements. Results: The automatic 3DE method was highly feasible, faster than 2DE and semi-automatic 3DE and data obtained were comparable with traditional measurements. Both in normal subjects and patients, the RVEF was similar to the two 3DE methods and 2DE and strain measurements obtained by the automated system correlated very well with the standard 2DE and strain ones. Conclusions: results showed that rapid analysis and excellent reproducibility of AI-based 3DE RV analysis supported the routine adoption of this automated method in the daily clinical workflow. [ABSTRACT FROM AUTHOR]

Purpose: Sagittal balance (SB) plays an important role in the surgical treatment of spinal disorders. The aim of this research study is to provide a detailed evaluation of a new, fully automated algorithm based on artificial intelligence (AI) for the determination of SB parameters on a large number of patients with and without instrumentation.Methods: Pre- and postoperative sagittal full body radiographs of 170 patients were measured by two human raters, twice by one rater and by the AI algorithm which determined: pelvic incidence, pelvic tilt, sacral slope, L1-S1 lordosis, T4-T12 thoracic kyphosis (TK) and the spino-sacral angle (SSA). To evaluate the agreement between human raters and AI, the mean error (95% confidence interval (CI)), standard deviation and an intra- and inter-rater reliability was conducted using intra-class correlation (ICC) coefficients.Results: ICC values for the assessment of the intra- (range: 0.88-0.97) and inter-rater (0.86-0.97) reliability of human raters are excellent. The algorithm is able to determine all parameters in 95% of all pre- and in 91% of all postoperative images with excellent ICC values (PreOP-range: 0.83-0.91, PostOP: 0.72-0.89). Mean errors are smallest for the SSA (PreOP: -0.1° (95%-CI: -0.9°-0.6°); PostOP: -0.5° (-1.4°-0.4°)) and largest for TK (7.0° (6.1°-7.8°); 7.1° (6.1°-8.1°)).Conclusion: A new, fully automated algorithm that determines SB parameters has excellent reliability and agreement with human raters, particularly on preoperative full spine images. The presented solution will relieve physicians from time-consuming routine work of measuring SB parameters and allow the analysis of large databases efficiently. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CIRCULO de Linguistica Aplicada a la Comunicacion is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problem of revealing the “Declaration of Love” discourse in works of art is considered. The author’s development on the automatic detection of the situation of declaration of love, tested on the material of the work of A. P. Chekhov, is presented. The search was carried out on the basis of the Russian National Corpus. In total, more than 200 texts have been identified containing textual representations of the situation of declaration of love. 40 out of 200 texts are identified by the authors of the article as the most fully representative of the desired situation. The set of textual passages describing it is viewed as a cognitive-discursive set of declarations of love. The development of the algorithm was carried out based on the identification of the cognitive schemes of the writer and the statistical analysis of the lexical composition of the situation of declaration of love. Among the frequency components of the cognitive model, which A. P. Chekhov follows in describing the process of declaring love, an open space filled with plants, birds, etc. was revealed. In the general cognitive-discursive set of declarations of love, the nuclear and satellite zones were identified and analyzed, the lexical components of which belong to certain functional-semantic classes and functional-semantic groups.

DICOM X-rays are not easily accessible for telemedicine, and existing learning-based automated Cobb angle (CA) predictions are not accurate on suboptimal X-ray images. To develop an automated CA prediction system irrespective of image quality, with no restrictions on curve patterns, 367 consecutive patients attending our scoliosis clinic were recruited and their coronal X-rays were re-captured using mobile phones. Five-fold cross-validation was conducted (each with 294 randomly selected images for training a neural network SpineHRNet to detect endplate landmarks and end-vertebrae, and the remaining 73 images for testing). The predicted heatmaps of vertebral landmarks were visualized to enhance interpretability of the SpineHRNet. Per-landmark Euclidean distance (L2) errors and recall of landmark detection were calculated to assess the accuracy of the predicted landmarks. Further computed CAs were quantitatively compared with spine-specialists measured ground truth (GT). The average L2 error and the recall of the detected endplates landmarks were 2.8 pixels and 0.99 respectively. The predicted CAs were all significantly correlated with GT ($\text{p}

Immunohistochemistry (IHC) plays an important role in evaluating the status of ER, PR, Ki-67 and human epidermal growth factor receptor 2 (HER-2) during diagnosis of breast cancer. Although some existing automated approaches can solve the high time-consumption and inter-/intra-observer variability drawbacks to a certain extent, most of them are can’t analyze both nuclear staining and cell membrane staining using the same method. This is attributed to the difference in localization of the positive signal of immunohistochemical staining in different biological markers. The present study proposes a novel automated image analysis model for scoring and grading of ER, PR, Ki-67 and HER-2 immunohistochemical images based on whole tissue sections in breast cancer. The scoring results of the trained model and manual interpretation of ER, PR, Ki-67 and HER-2 were then finally analyzed and compared. Experimental results show that the F1-measure was 0.8450, 0.8533 and 0.7962 for nuclear recognition of Ki-67, ER/PR and HER-2 respectively. For stain grading of Ki-67, ER/PR and HER-2, the F1-measure was 0.9776, 0.8306 and 0.9573 respectively. The scoring consistency of ER/PR, Ki-67 and HER-2 between our model and expert interpretation was 0.9279, 0.9712 and 0.8046 respectively. Our results demonstrate that artificial intelligence technology is a feasible and accurate method for accurate quantitative immunohistochemical analysis that can solve the drawbacks of low repeatability and time consumption brought by manual counting. The main contribution of our proposed model is that it can recognize both nuclear staining and cell membrane staining and grade the staining intensity as a sequential learning task.

Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis. [ABSTRACT FROM AUTHOR]

Assessment of swallowing function is often invasive or involves irradiation. Analysis of swallowing sounds is a noninvasive method for assessment of swallowing but is not used in daily medical practice. Dysphagia could be the first symptom that occurs in head and neck cancer. This study evaluated a method for the automatic detection and analysis of swallowing sounds in healthy subjects and in patients with pharyngolaryngeal cancer. A smartphone application, developed for automatic detection and analysis of swallowing sounds was developed and tested in 12 healthy volunteers and in 26 patients with pharyngolaryngeal cancer. Swallowing sounds were recorded with a laryngophone during a standardized meal (100 mL mashed potatoes, 100 mL water, and 100 mL yogurt). Swallowing number and duration were noted; the results were compared to a standard swallowing sound analysis using the software AUDACITY®. There were no statistically significant differences in swallowing number or duration between the two analysis methods for the three types of foods in healthy volunteers and only for water in patients. In healthy volunteers, the results of our automatic analysis were comparable with those obtained with the standard analysis. However, a better discrimination of swallowing sounds is necessary for the algorithm to obtain reliable results with thicker food in patients with head and neck cancer. [ABSTRACT FROM AUTHOR]

Purpose: To propose a deep-learning-based approach to automatically and objectively evaluate morphologic eyelid features using two-dimensional(2D) digital photographs and to assess the agreement between automatic and manual measurements. Materials and Methods: The 2D photographs of 1378 normal Asian participants (2756 eyes) were included for training, validating and testing the cornea and eyelid segmentation network. Margin reflex distance 1 (MRD1) and margin reflex distance 2 (MRD2) of 406 eyes from 203 participants were manually evaluated by 3 ophthalmologists and the photographs of 406 eyes were measured automatically for 8 morphologic eyelid features. The Spearman's correlation coefficient, intra-class correlation coefficient (ICC) and Bland-Altman analyses were used to determine the agreement between manual and automatic MRDs. Results: The dice coefficient was 0.922 and 0.974 for eyelid and cornea segmentation, respectively. A strong correlation was shown between manually and automatically measured MRD1 (r = 0.993, ICC = 0.996) and MRD2 (r = 0.950, ICC = 0.974). Bland-Altman analyses also showed excellent reliability with bias being 0.04 mmbetween automated and manual MRD1 measurements and 0.06 mm for MRD2. Automatically measured 8 features (MRD1, MRD2, palpebral fissure, medial area, lateral area, cornea area, upper and lower eyelid lengths) were found to be increased with age and peaked around the age range of 21 to 30 years. Conclusions: The proposed novel integrative analysis scheme was comparable with human performance. The approach with excellent reliability and reproductivity showed great potential for automated diagnosis and remote monitoring of eyelid-related diseases. [ABSTRACT FROM AUTHOR]

Patients with schizophrenia often display impairments in the expression of emotion and speech and those are observed in their facial behaviour. Automatic analysis of patients’ facial expressions that is aimed at estimating symptoms of schizophrenia has received attention recently. However, the datasets that are typically used for training and evaluating the developed methods, contain only a small number of patients (4-34) and are recorded while the subjects were performing controlled tasks such as listening to life vignettes, or answering emotional questions. In this paper, we use videos of professional-patient interviews, in which symptoms were assessed in a standardised way as they should/may be assessed in practice, and which were recorded in realistic conditions (i.e., varying illumination levels and camera viewpoints) at the patients’ homes or at mental health services. We automatically analyse the facial behaviour of 91 out-patients – this is almost 3 times the number of patients in other studies – and propose SchiNet, a novel neural network architecture that estimates expression-related symptoms in two different assessment interviews. We evaluate the proposed SchiNet for patient-independent prediction of symptoms of schizophrenia. Experimental results show that some automatically detected facial expressions are significantly correlated to symptoms of schizophrenia, and that the proposed network for estimating symptom severity delivers promising results. [ABSTRACT FROM AUTHOR]

Abstract Background Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T1 mapping in particular has shown promise as a useful biomarker to support diagnostic, therapeutic and prognostic decision-making in ischaemic and non-ischaemic cardiomyopathies. Methods Convolutional neural networks (CNNs) with Bayesian inference are a category of artificial neural networks which model the uncertainty of the network output. This study presents an automated framework for tissue characterisation from native shortened modified Look-Locker inversion recovery ShMOLLI T1 mapping at 1.5 T using a Probabilistic Hierarchical Segmentation (PHiSeg) network (PHCUMIS 119–127, 2019). In addition, we use the uncertainty information provided by the PHiSeg network in a novel automated quality control (QC) step to identify uncertain T1 values. The PHiSeg network and QC were validated against manual analysis on a cohort of the UK Biobank containing healthy subjects and chronic cardiomyopathy patients (N=100 for the PHiSeg network and N=700 for the QC). We used the proposed method to obtain reference T1 ranges for the left ventricular (LV) myocardium in healthy subjects as well as common clinical cardiac conditions. Results T1 values computed from automatic and manual segmentations were highly correlated (r=0.97). Bland-Altman analysis showed good agreement between the automated and manual measurements. The average Dice metric was 0.84 for the LV myocardium. The sensitivity of detection of erroneous outputs was 91%. Finally, T1 values were automatically derived from 11,882 CMR exams from the UK Biobank. For the healthy cohort, the mean (SD) corrected T1 values were 926.61 (45.26), 934.39 (43.25) and 927.56 (50.36) for global, interventricular septum and free-wall respectively. Conclusions The proposed pipeline allows for automatic analysis of myocardial native T1 mapping and includes a QC process to detect potentially erroneous results. T1 reference values were presented for healthy subjects and common clinical cardiac conditions from the largest cohort to date using T1-mapping images.

The increasing availability of devices to capture the position of moving objects (and other environmental information) leads to a very large amount and variety of mobility data. In order to obtain important information about the objects, their behavior or the environment of the objects, an automatic analysis is required. This article highlights current research questions in the context of the analysis of mobility data and presents them on the basis of work carried out at the Institute of Cartography and Geoinformatics (ikg) at Leibniz University of Hannover, Germany. A focus is put on the analysis and exploitation of information from Mobile Mapping vehicles.

Current methods to detect eating behavior events (i.e., bites, chews, and swallows) lack objective measurements, standard procedures, and automation. The video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we reviewed the current methods to automatically detect eating behavior events from video recordings. According to PRISMA guidelines, publications from 2010–2021 in PubMed, Scopus, ScienceDirect, and Google Scholar were screened through title and abstract, leading to the identification of 277 publications. We screened the full text of 52 publications and included 13 for analysis. We classified the methods in five distinct categories based on their similarities and analyzed their accuracy. Facial landmarks can count bites, chews, and food liking automatically (accuracy: 90%, 60%, 25%). Deep neural networks can detect bites and gesture intake (accuracy: 91%, 86%). The active appearance model can detect chewing (accuracy: 93%), and optical flow can count chews (accuracy: 88%). Video fluoroscopy can track swallows but is currently not suitable beyond clinical settings. The optimal method for automated counts of bites and chews is facial landmarks, although further improvements are required. Future methods should accurately predict bites, chews, and swallows using inexpensive hardware and limited computational capacity. Automatic eating behavior analysis will allow the study of eating behavior and real-time interventions to promote healthy eating behaviors.

The assessment of the knee alignment using standing weight-bearing full-leg radiographs (FLR) is a standardized method. Determining the load-bearing axis of the leg requires time-consuming manual measurements. The aim of this study is to develop and validate a novel algorithm based on artificial intelligence (AI) for the automated assessment of lower limb alignment. In the first stage, a customized mask-RCNN model was trained to automatically detect and segment anatomical structures and implants in FLR. In the second stage, four region-specific neural network models (adaptations of UNet) were trained to automatically place anatomical landmarks. In the final stage, this information was used to automatically determine five key lower limb alignment angles. For the validation dataset, weight-bearing, antero-posterior FLR were captured preoperatively and 3 months postoperatively. Preoperative images were measured by the operating orthopedic surgeon and an independent physician. Postoperative images were measured by the second rater only. The final validation dataset consisted of 95 preoperative and 105 postoperative FLR. The detection rate for the different angles ranged between 92.4% and 98.9%. Human vs. human inter-(ICCs: 0.85–0.99) and intra-rater (ICCs: 0.95–1.0) reliability analysis achieved significant agreement. The ICC-values of human vs. AI inter-rater reliability analysis ranged between 0.8 and 1.0 preoperatively and between 0.83 and 0.99 postoperatively (all p < 0.001). An independent and external validation of the proposed algorithm on pre- and postoperative FLR, with excellent reliability for human measurements, could be demonstrated. Hence, the algorithm might allow for the objective and time saving analysis of large datasets and support physicians in daily routine.

Background: The right ventricle (RV) plays a pivotal role in cardiovascular diseases and 3-dimensional echocardiography (3DE) has gained acceptance for the evaluation of RV volumes and function. Recently, a new artificial intelligence (AI)–based automated 3DE software for RV evaluation has been proposed and validated against cardiac magnetic resonance. The aims of this study were three-fold: (i) feasibility of the AI-based 3DE RV quantification, (ii) comparison with the semi-automatic 3DE method and (iii) assessment of 2-dimensional echocardiography (2DE) and strain measurements obtained automatically. Methods: A total of 203 subject (122 normal and 81 patients) underwent a 2DE and both the semi-automatic and automatic 3DE methods for Doppler standard, RV volumes and ejection fraction (RVEF) measurements. Results: The automatic 3DE method was highly feasible, faster than 2DE and semi-automatic 3DE and data obtained were comparable with traditional measurements. Both in normal subjects and patients, the RVEF was similar to the two 3DE methods and 2DE and strain measurements obtained by the automated system correlated very well with the standard 2DE and strain ones. Conclusions: results showed that rapid analysis and excellent reproducibility of AI-based 3DE RV analysis supported the routine adoption of this automated method in the daily clinical workflow.

Background: Cardiovascular magnetic resonance (CMR) sequences are commonly used to obtain a complete description of the function and structure of the heart, provided that accurate measurements are extracted from images. New methods of extraction of information are being developed, among them, deep neural networks are powerful tools that showed the ability to perform fast and accurate segmentation. Iq1n order to reduce the time spent by reading physicians to process data and minimize intra- and inter-observer variability, we propose a fully automatic multi-scan CMR image analysis pipeline. Methods: Sequence specific U-Net 2D models were trained to perform the segmentation of the left ventricle (LV), right ventricle (RV) and aorta in cine short-axis, late gadolinium enhancement (LGE), native T1 map, post-contrast T1, native T2 map and aortic flow sequences depending on the need. The models were trained and tested on a set of data manually segmented by experts using semi-automatic and manual tools. A set of parameters were computed from the resulting segmentations such as the left ventricular and right ventricular ejection fraction (EF), LGE scar percentage, the mean T1, T1 post, T2 values within the myocardium, and aortic flow. The Dice similarity coefficient, Hausdorff distance, mean surface distance, and Pearson correlation coefficient R were used to assess and compare the results of the U-Net based pipeline with intra-observer variability. Additionally, the pipeline was validated on two clinical studies. Results: The sequence specific U-Net 2D models trained achieved fast (≤ 0.2 s/image on GPU) and precise segmentation over all the targeted region of interest with high Dice scores (= 0.91 for LV, = 0.92 for RV, = 0.93 for Aorta in average) comparable to intra-observer Dice scores (= 0.86 for LV, = 0.87 for RV, = 0.95 for aorta flow in average). The automatically and manually computed parameters were highly correlated (R = 0.91 in average) showing results superior to the intra-observer variability (R = 0.85 in average) for every sequence presented here. Conclusion: The proposed pipeline allows for fast and robust analysis of large CMR studies while guaranteeing reproducibility, hence potentially improving patient's diagnosis as well as clinical studies outcome. [ABSTRACT FROM AUTHOR]

Purpose: Cardiac relaxometry techniques, particularly T1 mapping, have recently gained clinical importance in various cardiac pathologies. Myocardial T1 and extracellular volume are usually calculated from manual identification of left ventricular epicardial and endocardial regions. This is a laborious process, particularly for large volume studies. Here we present a fully automated relaxometry framework (FASTR) for segmental analysis of T1 maps (both native and postcontrast) and partition coefficient (λ). Methods: Patients (N = 11) were imaged postacute myocardial infarction on a 1.5T clinical scanner. The scan protocol involved CINE‐SSFP imaging, native, and post‐contrast T1 mapping using the Modified Look‐Locker Inversion (MOLLI) recovery sequence. FASTR consisted of automatic myocardial segmentation of spatio‐temporally coregistered CINE images as an initial guess, followed by refinement of the contours on the T1 maps to derive segmental T1 and λ. T1 and λ were then compared to those obtained from two trained expert observers. Results: Robust endocardial and epicardial contours were achieved on T1 maps despite the presence of infarcted tissue. Relative to experts, FASTR resulted in myocardial Dice coefficients (native T1: 0.752 ± 0.041; postcontrast T1: 0.751 ± 0.057) that were comparable to interobserver Dice (native T1: 0.803 ± 0.045; postcontrast T1: 0.799 ± 0.054). There were strong correlations observed for T1 and λ derived from experts and FASTR (native T1: r = 0.83; postcontrast T1: r = 0.87; λ: r = 0.78; P < 0.0001), which were comparable to inter‐expert correlation coefficients (native T1: r = 0.90; postcontrast T1: r = 0.93; λ: r = 0.80; P < 0.0001). Conclusions: Our fully automated framework, FASTR, can generate accurate myocardial segmentations for native and postcontrast MOLLI T1 analysis without the need for manual intervention. Such a design is appealing for high volume clinical protocols. [ABSTRACT FROM AUTHOR]

Abstract Background Cardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility. In this study, we propose an automated method for combined myocardium segmentation, alignment, and T1 calculation for myocardial T1 mapping. Methods A deep fully convolutional neural network (FCN) was used for myocardium segmentation in T1 weighted images. The segmented myocardium was then resampled on a polar grid, whose origin is located at the center-of-mass of the segmented myocardium. Myocardium T1 maps were reconstructed from the resampled T1 weighted images using curve fitting. The FCN was trained and tested using manually segmented images for 210 patients (5 slices, 11 inversion times per patient). An additional image dataset for 455 patients (5 slices and 11 inversion times per patient), analyzed by an expert reader using a semi-automatic tool, was used to validate the automatically calculated global and regional T1 values. Bland-Altman analysis, Pearson correlation coefficient, r, and the Dice similarity coefficient (DSC) were used to evaluate the performance of the FCN-based analysis on per-patient and per-slice basis. Inter-observer variability was assessed using intraclass correlation coefficient (ICC) of the T1 values calculated by the FCN-based automatic method and two readers. Results The FCN achieved fast segmentation (

In recent years, major capability improvements at synchrotron beamlines have given researchers the ability to capture more complex structures at a higher resolution within a very short time. This opens up the possibility of studying dynamic processes and observing resulting structural changes over time. However, such studies can create a huge quantity of 3D image data, which presents a major challenge for segmentation and analysis. Here tomography experiments at the Australian synchrotron source are examined, which were used to study bread dough formulations during rising and baking, resulting in over 460 individual 3D datasets. The current pipeline for segmentation and analysis involves semi‐automated methods using commercial software that require a large amount of user input. This paper focuses on exploring machine learning methods to automate this process. The main challenge to be faced is in generating adequate training datasets to train the machine learning model. Creating training data by manually segmenting real images is very labour‐intensive, so instead methods of automatically creating synthetic training datasets which have the same attributes of the original images have been tested. The generated synthetic images are used to train a U‐Net model, which is then used to segment the original bread dough images. The trained U‐Net outperformed the previously used segmentation techniques while taking less manual effort. This automated model for data segmentation would alleviate the time‐consuming aspects of experimental workflow and would open the door to perform 4D characterization experiments with smaller time steps. [ABSTRACT FROM AUTHOR]

Circulating tumor cells (CTCs) indicate the diagnosis and prognosis of cancer patients, together with benefiting individual treatment and anticancer drug development. However, their large‐scale application in general population still requires systematically multifaceted modifications for currently proprietary new technologies based on filtration. We primitively utilized a cell size‐based platform to evaluate the recovery efficiency of spiked abnormal cell lines and analyzed circulating abnormal cells (CACs). To dissect the subpopulations of CACs, we conducted immunofluorescent (IF) staining with a combination of unique biomarkers of CTCs and circulating endothelial cells (CECs). Furthermore, we improved the CTC screening system by assessing the feasibility of transferring CTCs for automatic IF analysis, together with simulating and optimizing the circumstances for long‐term CTC storage and transportation. We detected CACs in 15 HD candidates with CTC characteristics such as abnormally large cytomorphology, high nuclear−cytoplasmic ratio, and positive for panCK or VIM staining. Thereafter, we improved accuracy of the platform by distinguishing CTCs from CECs, which satisfied the elementary requirement for small‐scale CTC screening in HD candidates. Finally, large‐scale CTC screening in general population was available after multifaceted modifications including automatic analysis by transferring CTCs on slides, choosing the appropriate blood‐collecting tube, optimizing the conditions for long‐term CTC storage and transportation, and evaluating the potential effect on the CTC phenotype. Hence, we systematically modified the scope of technique parameters, improved the accuracy of early cancer detection, and made it realizable for large‐scale CTC or CEC screening in general population. [ABSTRACT FROM AUTHOR]

Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.