How useful are genomic data for predicting maladaptation to future climate?

Methods using genomic information to forecast potential population maladaptation to climate change or new environments are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods-Gradient Forests (GFoffset) and the risk of non-adaptedness (RONA)-using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against 2- and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.

Information literacy in the digital age: information sources, evaluation strategies, and perceived teaching competences of pre-service teachers.

Introduction: Information literacy has become indispensable in navigating today's fast-paced media environment, with teachers playing a pivotal role in fostering reflective and critical digital citizenship. Positioned as future gatekeepers, pre-service teachers are the key to teaching media skills and especially information literacy to future generations of pupils. Given the particular challenges facing educators today compared to previous generations, it is important to determine whether the next generation of teachers feel adequately prepared and perceive themselves as competent to pass on these skills to their future pupils. However, previous research has highlighted deficiencies in formal learning opportunities at universities, underscoring the need for further investigation into pre-service teachers' information acquisition, evaluation practices as well as their perceived relevance to teaching, and person-related factors associated with their perceived competence in teaching information literacy. Method: An online questionnaire was presented to participants, employing a mixed-method approach. We qualitatively examined the sources of information used by pre-service teachers and the evaluation strategies they employ, while quantitatively analyzing relationships between pre-service teachers' person-related factors and their perceived teaching competence. Participants assessed their perceived teaching competence, perceived learning opportunities, self-efficacy (general and related to information assessment), perceived informedness, selective exposure, need for cognition, need for cognitive closure, and mistrust in media coverage. Results: Data from 371 participants revealed digital media dominance in information acquisition over traditional sources, albeit with a prevalence of surface-level evaluation strategies over reflective approaches. Two distinct dimensions of perceived competence in teaching information literacy emerged: one focusing on information assessment while the other centers on the understanding of news creation processes. Perceived competence in teaching information literacy was significantly associated with self-efficacy in information assessment, perceived informedness, selective exposure to information as well as perceived learning opportunities focusing on information evaluation. Moreover, pre-service teachers employing diverse information evaluation strategies demonstrated a heightened sense of perceived competence in teaching information assessment. Discussion: Our results provide valuable insights into the multifaceted nature of pre-service teachers' perceived competence in teaching information literacy. Theoretical implications for future research as well as practical implications for teacher education and the structure of future curricula are discussed.

Effect of Consultation Number on the Assessment and Treatment of Polycystic Ovary Syndrome.

Background: The basic medical education stage is not enough to support physicians to fully diagnose and evaluate polycystic ovary syndrome (PCOS). The study aims to discover the difference in treatment choice between participants with different annual consultation number of PCOS, to promote lifelong learning, and drive balanced development within healthcare. Methods: This is a multicenter cross-sectional survey. Participants' basic information, knowledge of PCOS and treatment options were collected online. According to the annual consultation number of patients with PCOS, physicians were divided into three groups: 0-50 people/yr, 50-200 people/yr, and >200 people/yr, and the results were derived from χ2 test, Fisher exact test, and multivariate logistic regression analysis. Results: The study analyzed 1689 questionnaires, and 1206 physicians (71.4%) received less than 50 women per year, 388 physicians (30.0%) with an annual number of 50-200 women, and 95 physicians (5.6%) with patient turnover for more than 200 people. Reproductive endocrinologists generally have higher access to the clinic. As the number of visits increases, more and more physicians would perceive patients as more likely to have abnormal blood glucose and heavy weight. Physicians with large numbers of consultations are more likely to use Asian or Chinese standards to assess obesity. The multivariate analysis involved variables such as age, hospital level, specialty, and patient turnover annually, and more young doctors actively assessed lipid profile (odds ratio (OR) 1.56, 95% confidence interval (CI) (1.16, 2.16)), and primary hospitals (OR 0.65 CI (0.44, 0.89)) chose OGTT for blood glucose assessment less than tertiary hospitals. Physicians in secondary hospitals are more aggressive in evaluating androgens. Conclusion: Our survey found differences in endocrine assessment, metabolic screening, and treatment in PCOS women in terms of the number of obstetrician-gynecologists who received different patient consultation numbers. The importance of continuing education for physicians is emphasized, to promote lifelong learning.

Virtual reality in experiential pharmacy education: A quasi-experimental study.

Introduction: Extended reality (XR) technologies are an umbrella term for simulated-based learning tools that cover 3-dimensional technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR). At King Saud University, first-year pharmacy students are required to experience hospital observational training during the Introductory Pharmacy Practice Experience (IPPE). We aimed to measure the effectiveness and satisfaction of the VR learning experience among IPPE students. Methods: A Quasi-Experimental study was conducted. The experimental arm included first-year PharmD students. VR headset was used to watch three narrated videos capturing 360° views of the outpatient, inpatient pharmacy, and counseling clinic. A test measuring students' general knowledge was required prior to and post the experience, followed by a satisfaction survey. The control arm included second-year PharmD students who had traditional hospital visits and were administered a knowledge test and satisfaction survey. Results: A total of 336 students were enrolled, 174 in the experimental arm and 162 in the control arm. The results showed improvement in the knowledge scores average among the experimental arm, 1.9 vs 3.5 in the pre-test and post-test. The control arm had a comparable score with an average of 3.7. Regarding self-assessment using four 5-likert scales assessing pharmacist role, skills, and responsibilities, 31.8 % and 42 % in the experimental arm compared to 28.9 % and 28.9 % in the control group answered strongly agree and agree, respectively. Regarding satisfaction, using five 5-Likert scales assessing the experience time, quality, and content, 53 % and 25 % in the experimental group compared to 34 % and 23 % in the control group answered strongly agree and agree, respectively. Conclusion: VR provides pharmacy students with a standardized and effective learning and training experience. The experimental arm reported higher satisfaction rates and self-reported outcomes. Thus, implementing VR experiences within the pharmacy curriculum will provide students with an advanced educational advantage.

Accurate prediction of myopic progression and high myopia by machine learning.

Background: Myopia is a leading cause of visual impairment in Asia and worldwide. However, accurately predicting the progression of myopia and the high risk of myopia remains a challenge. This study aims to develop a predictive model for the development of myopia. Methods: We first retrospectively gathered 612 530 medical records from five independent cohorts, encompassing 227 543 patients ranging from infants to young adults. Subsequently, we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia. Result: The model to predict the progression of myopia achieved an R2 value of 0.964 vs a mean absolute error (MAE) of 0.119D [95% confidence interval (CI): 0.119, 1.146] in the internal validation set. It demonstrated strong generalizability, maintaining consistent performance across external validation sets: R2 = 0.950 vs MAE = 0.119D (95% CI: 0.119, 1.136) in validation study 1, R2 = 0.950 vs MAE = 0.121D (95% CI: 0.121, 1.144) in validation study 2, and R2 = 0.806 vs MAE = -0.066D (95% CI: -0.066, 0.569) in the Shanghai Children Myopia Study. In the Beijing Children Eye Study, the model achieved an R2 of 0.749 vs a MAE of 0.178D (95% CI: 0.178, 1.557). The model to predict the risk of high myopia achieved an area under the curve (AUC) of 0.99 in the internal validation set and consistently high area under the curve values of 0.99, 0.99, 0.96 and 0.99 in the respective external validation sets. Conclusion: Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.

How to create a successful mobile learning strategy for medical education during lockdowns?

BACKGROUND: Mobile learning has played an important role during the COVID-19 pandemic and medical schools now consider it as an effective educational method in current and future crises. In this qualitative study, an attempt was made to demonstrate the principles of designing a mobile learning strategy in medical education from the perspective of experts. MATERIALS AND METHODS: The study was conducted by the qualitative content analysis method. The data were collected from July 2022 to Feb 2023. Twelve participants were included in this study from Iran's medical universities, consisting of two members of the Higher Council of Virtual Education, three educational directors, three clinical faculty members, two faculty members specializing in e-learning and medical education, an educational vice, and a dean. Data were collected using semi-structured interviews and analyzed by Granheim and Lundman's (2004) method. RESULTS: Out of twelve participants in the study, eight (66%) were males and four (44%) females. Data were classified into eight categories and one theme. Based on the participants' experiences, the main theme, that is, "the principles of medical education design in mobile learning," included pedagogical component, interactive design, effective and comprehensive analysis, achieving objectives with the mobile learning platform, generating micro- and interactive e-content, teaching-learning interactive methods, course implementation and interactive evaluation at both micro- and macro-levels. CONCLUSION: Data analysis revealed that in addition to the eight principles in the medical education design in mobile learning, the participants prioritized the two principles of pedagogical component and interactive design over other principles in educational design. Using a successful mobile learning strategy in situations of restrictions limiting physical presence may improve the quality of medical education.

Human skill knowledge guided global trajectory policy reinforcement learning method.

Traditional trajectory learning methods based on Imitation Learning (IL) only learn the existing trajectory knowledge from human demonstration. In this way, it can not adapt the trajectory knowledge to the task environment by interacting with the environment and fine-tuning the policy. To address this problem, a global trajectory learning method which combinines IL with Reinforcement Learning (RL) to adapt the knowledge policy to the environment is proposed. In this paper, IL is proposed to acquire basic trajectory skills, and then learns the agent will explore and exploit more policy which is applicable to the current environment by RL. The basic trajectory skills include the knowledge policy and the time stage information in the whole task space to help learn the time series of the trajectory, and are used to guide the subsequent RL process. Notably, neural networks are not used to model the action policy and the Q value of RL during the RL process. Instead, they are sampled and updated in the whole task space and then transferred to the networks after the RL process through Behavior Cloning (BC) to get continuous and smooth global trajectory policy. The feasibility and the effectiveness of the method was validated in a custom Gym environment of a flower drawing task. And then, we executed the learned policy in the real-world robot drawing experiment.

Preoperative prediction for early recurrence of hepatocellular carcinoma using machine learning-based radiomics.

Objective: To develop a contrast-enhanced computed tomography (CECT) based radiomics model using machine learning method and assess its ability of preoperative prediction for the early recurrence of hepatocellular carcinoma (HCC). Methods: A total of 297 patients confirmed with HCC were assigned to the training dataset and test dataset based on the 8:2 ratio, and the follow-up period of the patients was from May 2012 to July 2017. The lesion sites were manually segmented using ITK-SNAP, and the pyradiomics platform was applied to extract radiomic features. We established the machine learning model to predict the early recurrence of HCC. The accuracy, AUC, standard deviation, specificity, and sensitivity were applied to evaluate the model performance. Results: 1,688 features were extracted from the arterial phase and venous phase images, respectively. When arterial phase and venous phase images were employed correlated with clinical factors to train a prediction model, it achieved the best performance (AUC with 95% CI 0.8300(0.7560-0.9040), sensitivity 89.45%, specificity 79.07%, accuracy 82.67%, p value 0.0064). Conclusion: The CECT-based radiomics may be helpful to non-invasively reveal the potential connection between CECT images and early recurrence of HCC. The combination of radiomics and clinical factors could boost model performance.

Predicting early gastric cancer risk using machine learning: A population-based retrospective study.

Background: Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple indicators. Methods: Based on the XGBoost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker results. Results: The XHGC20 model constructed using the XGBoost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed diagnoses. Conclusions: The XHGC20 model established by the XGBoost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.

Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study.

Objectives: Metabolic bariatric surgery is a critical intervention for patients living with obesity and related health issues. Accurate classification and prediction of patient outcomes are vital for optimizing treatment strategies. This study presents a novel machine learning approach to classify patients in the context of metabolic bariatric surgery, providing insights into the efficacy of different models and variable types. Methods: Various machine learning models, including Gaussian Naive Bayes, Complement Naive Bayes, K-nearest neighbour, Decision Tree, K-nearest neighbour with RandomOverSampler, and K-nearest neighbour with SMOTE, were applied to a dataset of 73 patients. The dataset, comprising psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study also explored the impact of different variable groupings and oversampling techniques. Results: Experimental results indicate average accuracy values as high as 66.7% for the best model. Enhanced versions of K-nearest neighbour and Decision Tree, along with variations of K-nearest neighbour such as RandomOverSampler and SMOTE, yielded the best results. Conclusions: The study unveils a promising avenue for classifying patients in the realm of metabolic bariatric surgery. The results underscore the importance of selecting appropriate variables and employing diverse approaches to achieve optimal performance. The developed system holds potential as a tool to assist healthcare professionals in decision-making, thereby enhancing metabolic bariatric surgery outcomes. These findings lay the groundwork for future collaboration between hospitals and healthcare entities to improve patient care through the utilization of machine learning algorithms. Moreover, the findings suggest room for improvement, potentially achievable with a larger dataset and careful parameter tuning.

Detection of arousal and valence from facial expressions and physiological responses evoked by different types of stressors.

Automatically detecting mental state such as stress from video images of the face could support evaluating stress responses in applicants for high risk jobs or contribute to timely stress detection in challenging operational settings (e.g., aircrew, command center operators). Challenges in automatically estimating mental state include the generalization of models across contexts and across participants. We here aim to create robust models by training them using data from different contexts and including physiological features. Fifty-one participants were exposed to different types of stressors (cognitive, social evaluative and startle) and baseline variants of the stressors. Video, electrocardiogram (ECG), electrodermal activity (EDA) and self-reports (arousal and valence) were recorded. Logistic regression models aimed to classify between high and low arousal and valence across participants, where "high" and "low" were defined relative to the center of the rating scale. Accuracy scores of different models were evaluated: models trained and tested within a specific context (either a baseline or stressor variant of a task), intermediate context (baseline and stressor variant of a task), or general context (all conditions together). Furthermore, for these different model variants, only the video data was included, only the physiological data, or both video and physiological data. We found that all (video, physiological and video-physio) models could successfully distinguish between high- and low-rated arousal and valence, though performance tended to be better for (1) arousal than valence, (2) specific context than intermediate and general contexts, (3) video-physio data than video or physiological data alone. Automatic feature selection resulted in inclusion of 3-20 features, where the models based on video-physio data usually included features from video, ECG and EDA. Still, performance of video-only models approached the performance of video-physio models. Arousal and valence ratings by three experienced human observers scores based on part of the video data did not match with self-reports. In sum, we showed that it is possible to automatically monitor arousal and valence even in relatively general contexts and better than humans can (in the given circumstances), and that non-contact video images of faces capture an important part of the information, which has practical advantages.

Federated learning for medical image analysis: A survey.

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.

Objective: To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods. Methods: Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models. Results: In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study. Conclusion: In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Using Claims Data to Predict Pre-Operative BMI Among Bariatric Surgery Patients: Development of the BMI Before Bariatric Surgery Scoring System (B3S3).

Background: Lack of body mass index (BMI) measurements limits the utility of claims data for bariatric surgery research, but pre-operative BMI may be imputed due to existence of weight-related diagnosis codes and BMI-related reimbursement requirements. We used a machine learning pipeline to create a claims-based scoring system to predict pre-operative BMI, as documented in the electronic health record (EHR), among patients undergoing a new bariatric surgery. Methods: Using the Optum Labs Data Warehouse, containing linked de-identified claims and EHR data for commercial or Medicare Advantage enrollees, we identified adults undergoing a new bariatric surgery between January 2011 and June 2018 with a BMI measurement in linked EHR data ≤30 days before the index surgery (n=3226). We constructed predictors from claims data and applied a machine learning pipeline to create a scoring system for pre-operative BMI, the B3S3. We evaluated the B3S3 and a simple linear regression model (benchmark) in test patients whose index surgery occurred concurrent (2011-2017) or prospective (2018) to the training data. Results: The machine learning pipeline yielded a final scoring system that included weight-related diagnosis codes, age, and number of days hospitalized and distinct drugs dispensed in the past 6 months. In concurrent test data, the B3S3 had excellent performance (R2 0.862, 95% confidence interval [CI] 0.815-0.898) and calibration. The benchmark algorithm had good performance (R2 0.750, 95% CI 0.686-0.799) and calibration but both aspects were inferior to the B3S3. Findings in prospective test data were similar. Conclusion: The B3S3 is an accessible tool that researchers can use with claims data to obtain granular and accurate predicted values of pre-operative BMI, which may enhance confounding control and investigation of effect modification by baseline obesity levels in bariatric surgery studies utilizing claims data.

Pre-operative BMI is an important potential confounder in comparative effectiveness studies of bariatric surgeries.Claims data lack clinical measurements, but insurance reimbursement requirements for bariatric surgery often result in pre-operative BMI being coded in claims data.We used a machine learning pipeline to create a model, the B3S3, to predict pre-operative BMI, as documented in the EHR, among bariatric surgery patients based on the presence of certain weight-related diagnosis codes and other patient characteristics derived from claims data.Researchers can easily use the B3S3 with claims data to obtain granular and accurate predicted values of pre-operative BMI among bariatric surgery patients.

Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis.

Objective: Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs). Methods: EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single center at the time of first (T1), second (T2), and third (T3) visits. The radiographic progression of the (n+1)th visit (Pn+1=(mSASSSn+1-mSASSSn)/(Tn+1-Tn)≥1 unit per year) was predicted using follow-up visit datasets from T1 to Tn. We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation. Results: The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase. Conclusion: Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset when predicting radiographic progression.

Predictors of compulsive cyberporn use: A machine learning analysis.

Introduction: Compulsive cyberporn use (CCU) has previously been reported among people who use cyberporn. However, most of the previous studies included convenience samples of students or samples of the general adult population. Research examining the factors that predict or are associated with CCU are still scarce.In this study, we aimed to (a) assess compulsive cyberporn consumption in a broad sample of people who had used cyberporn and (b) determine, among a diverse range of predictor variables, which are most important in CCU scores, as assessed with the eight-item Compulsive Internet Use Scale adapted for cyberporn. Materials and Methods: Overall, 1584 adult English speakers (age: 18-75 years, M = 33.18; sex: 63.1 % male, 35.2 % female, 1.7 % nonbinary) who used cyberporn during the last 6 months responded to an online questionnaire that assessed sociodemographic, sexual, psychological, and psychosocial variables. Their responses were subjected to correlation analysis, analysis of variance, and machine learning analysis. Results: Among the participants, 21.96% (in the higher quartile) presented CCU symptoms in accordance with their CCU scores. The five most important predictors of CCU scores were related to the users' strength of craving for pornography experiences, suppression of negative emotions porn use motive, frequency of cyberporn use over the past year, acceptance of rape myths, and anxious attachment style. Conclusions: From a large and diverse pool of variables, we determined the most important predictors of CCU scores. The findings contribute to a better understanding of problematic pornography use and could enrich compulsive cyberporn treatment and prevention.

Automatic assessment of disproportionately enlarged subarachnoid-space hydrocephalus from 3D MRI using two deep learning models.

Background: Disproportionately enlarged subarachnoid-space hydrocephalus (DESH) is a key feature for Hakim disease (idiopathic normal pressure hydrocephalus: iNPH), but subjectively evaluated. To develop automatic quantitative assessment of DESH with automatic segmentation using combined deep learning models. Methods: This study included 180 participants (42 Hakim patients, 138 healthy volunteers; 78 males, 102 females). Overall, 159 three-dimensional (3D) T1-weighted and 180 T2-weighted MRIs were included. As a semantic segmentation, 3D MRIs were automatically segmented in the total ventricles, total subarachnoid space (SAS), high-convexity SAS, and Sylvian fissure and basal cistern on the 3D U-Net model. As an image classification, DESH, ventricular dilatation (VD), tightened sulci in the high convexities (THC), and Sylvian fissure dilatation (SFD) were automatically assessed on the multimodal convolutional neural network (CNN) model. For both deep learning models, 110 T1- and 130 T2-weighted MRIs were used for training, 30 T1- and 30 T2-weighted MRIs for internal validation, and the remaining 19 T1- and 20 T2-weighted MRIs for external validation. Dice score was calculated as (overlapping area) × 2/total area. Results: Automatic region extraction from 3D T1- and T2-weighted MRI was accurate for the total ventricles (mean Dice scores: 0.85 and 0.83), Sylvian fissure and basal cistern (0.70 and 0.69), and high-convexity SAS (0.68 and 0.60), respectively. Automatic determination of DESH, VD, THC, and SFD from the segmented regions on the multimodal CNN model was sufficiently reliable; all of the mean softmax probability scores were exceeded by 0.95. All of the areas under the receiver-operating characteristic curves of the DESH, Venthi, and Sylhi indexes calculated by the segmented regions for detecting DESH were exceeded by 0.97. Conclusion: Using 3D U-Net and a multimodal CNN, DESH was automatically detected with automatically segmented regions from 3D MRIs. Our developed diagnostic support tool can improve the precision of Hakim disease (iNPH) diagnosis.

Classification of high-grade glioblastoma and single brain metastases using a new SCAT-inception model trained with MRI images.

Background and objectives: Glioblastoma (GBM) and brain metastasis (MET) are the two most common intracranial tumors. However, the different pathogenesis of the two tumors leads to completely different treatment options. In terms of magnetic resonance imaging (MRI), GBM and MET are extremely similar, which makes differentiation by imaging extremely challenging. Therefore, this study explores an improved deep learning algorithm to assist in the differentiation of GBM and MET. Materials and methods: For this study, axial contrast-enhanced T1 weight (ceT1W) MRI images from 321 cases of high-grade gliomas and solitary brain metastasis were collected. Among these, 251 out of 270 cases were selected for the experimental dataset (127 glioblastomas and 124 metastases), 207 cases were chosen as the training dataset, and 44 cases as the testing dataset. We designed a new deep learning algorithm called SCAT-inception (Spatial Convolutional Attention inception) and used five-fold cross-validation to verify the results. Results: By employing the newly designed SCAT-inception model to predict glioblastomas and brain metastasis, the prediction accuracy reached 92.3%, and the sensitivity and specificity reached 93.5 and 91.1%, respectively. On the external testing dataset, our model achieved an accuracy of 91.5%, which surpasses other model performances such as VGG, UNet, and GoogLeNet. Conclusion: This study demonstrated that the SCAT-inception architecture could extract more subtle features from ceT1W images, provide state-of-the-art performance in the differentiation of GBM and MET, and surpass most existing approaches.

University students' academic vocabulary development through mobile-assisted learning: Exploring the impacts on receptive and productive knowledge.

This study explored the effectiveness of mobile-assisted vocabulary learning through digital flashcards on receptive and productive aspects of academic vocabulary knowledge among Iranian EFL university students. In a quasi-experimental design, 86 participants were divided into three groups: a digital flashcard group (DFs), a traditional paper flashcard (PFs) group, and a control group using word lists, to assess the impact of these methods on vocabulary acquisition over five weeks. The findings revealed that students utilizing DFs exhibited significant improvements in both receptive and productive vocabulary knowledge compared to those using PFs and the control method. Notably, the increase in receptive vocabulary was more substantial than in productive vocabulary, highlighting the differential effects of DFs on various aspects of vocabulary learning. This finding underscores the need for targeted strategies to enhance productive aspects of academic vocabulary specifically. The study supports the integration of DFs into English for Academic Purposes (EAP) programs to leverage their potential in boosting vocabulary acquisition effectively. However, the lesser gains in productive vocabulary suggest the necessity for complementary instructional methods, which focus on more active vocabulary learning tasks. Based on these findings, the study argues that mobile-assisted vocabulary learning should be considered a practical strategy for supporting academic vocabulary development among university students.

Supporting self-regulated-learning in colonoscopy training-A comparison cohort trial.

Aim: This study aimed to demonstrate that using a self-regulated learning (SRL) approach can improve colonoscopy performance skills. Background: Colonoscopy is the gold standard for detecting colorectal cancer and removing its precursors: polyps. Acquiring proficiency in colonoscopy is challenging, requiring completion of several hundred procedures. SRL seems to be beneficial to help trainees acquire competencies in regulating their future learning processes and enhance the outcomes of current learning situations. SRL is a learner-centred approach that refers to a trainee's ability to understand and control their learning environment, including cognitions, motivations and emotions. The key abilities include self- and situational awareness, task analysis, and strategic planning. This study is the first to use an SRL approach for workplace-based colonoscopy training. Methods: In this comparison cohort trial, participants used two SRL supports: a self-review of videotaped performance, and an online learning platform with procedural and conceptual knowledge about colonoscopy. In the control cohort, participants performed patient-based colonoscopy as usual in their departments. Improvement was monitored via three video-based ratings (study start, end of the study period, and follow-up) using the Gastrointestinal Endoscopy Competency Assessment Tool (GiECAT). Outcomes were analysed using two-way analysis of variance with repeated measurements. Results: This study recruited 21 participants (12, intervention cohort; nine, control cohort); 58 videos were recorded. The intraclass correlation coefficient was 0.88 (95% CI 0.61-0.98; p < 0.001). The global rating scale (GRS) and checklist (CL) in GiECAT were analysed separately. No statistically significant main effects of cohort (GRS: F(1,16) = 2.84, p = 0.11; CL: F(1,16) = 1.06, p = 0.32), test (GRS: F(2,32) = 2.56, p = 0.09; CL: F(2,32) = 0.76, p = 0.48), or interactions between cohort and test were observed (GRS: F(2,32) = 1.16, p = 0.33; CL: F(2,32) = 1.01, p = 0.37). Conclusions: SRL in patient-based colonoscopy is feasible; however, no clear effect on performance scores was observed.