Learning assessment

Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses...

We developed a prototype VR platform, VECTORS L&M (VLM), aiming to enhance the understanding of digestive surgery for students, interns, and young surgeons by limiting costs. Its efficacy was assessed via questionnaires before implementation in surgical education. The VLM provides nine-minute VR views of surgeries, from both 180- and 360-degree angles. It was created with L.A.B. Co., Ltd. and incorporates surgery videos from biliary malignancy patients. Following VLM development, a survey was conducted among surgeons who had experienced it...

Dairy calves are typically fed restricted amounts of milk. Although feed restrictions are predicted to result in negative affective states, the relative aversiveness of 'hunger' remains largely unexplored in this species. Here, we investigated whether the conditioned place preference paradigm can be used to explore how calves feel when experiencing different levels of satiation. This paradigm provides insight into what animals remember from past experiences, the assumption being that individuals will prefer places associated with more pleasant or less unpleasant experiences...

Although Severe Acute Respiratory Syndrome Coronavirus 2 infection (SARS-CoV-2) is primarily recognized as a respiratory disease, mounting evidence suggests that it may lead to neurological and cognitive impairments. The current study used three eye-tracking tasks (free-viewing, fixation, and smooth pursuit) to assess the oculomotor functions of mild infected cases over six months with symptomatic SARS-CoV-2 infected volunteers. Fifty symptomatic SARS-CoV-2 infected, and 24 self-reported healthy controls completed the eye-tracking tasks in an initial assessment...

Perinatal mood and anxiety disorders (PMADs) are the most common complication of childbirth. When poorly controlled, they are associated with worse obstetric outcomes, such as higher rates of preterm birth and unplanned cesarean delivery. They are also associated with suicide, a leading cause of perinatal maternal death. This article provides an overview of evidence-based recommendations for screening, assessment, and management of PMADs, including suicide risk assessment and management and pharmacological and nonpharmacological treatment options compatible with pregnancy and lactation...

Maritime accidents frequently lead to severe property damage and casualties, and an accurate and reliable risk prediction model is necessary to help maritime stakeholders assess the current risk situation. Therefore, the present study proposes a hybrid methodology to develop an explainable prediction model for maritime accident types. Based on the advantages of selective ensemble learning method, this study pioneers to introduce a two-stage model selection method, aiming to enhance the predictive accuracy and stability of the model...

The classification of garbage types is an important issue in today's world, and its proper implementation can contribute to environmental conservation and improved efficiency of recycling processes. Unfortunately, the classification of garbage types is currently predominantly performed through human supervision, which leads to high errors and environmental risks. It is crucial to automate this procedure utilizing machine vision methods as a result. This research proposes a revolutionary deep learning-based strategy for classifying domestic waste...

Women tend to face many problems throughout their lives; cervical cancer is one of the most dangerous diseases that they can face, and it has many negative consequences. Regular screening and treatment of precancerous lesions play a vital role in the fight against cervical cancer. It is becoming increasingly common in medical practice to predict the early stages of serious illnesses, such as heart attacks, kidney failure, and cancer, using machine learning-based techniques. To overcome these obstacles, we propose the use of auxiliary modules and a special residual block, to record contextual interactions between object classes and to support the object reference strategy...

Maladaptive avoidance behaviour is often observed in patients suffering from anxiety and trauma- and stressor-related disorders. The prefrontal-amygdala-hippocampus network is implicated in learning and memory consolidation. Neuroinflammation in this circuitry alters network dynamics, resulting in maladaptive avoidance behaviour. The two-way active avoidance test is a well-established translational model for assessing avoidance responses to stressful situations. While some animals learn the task and show adaptive avoidance (AA), others show strong fear responses to the test environment and maladaptive avoidance (MA)...

OBJECTIVE: Depth camera-based measurement has demonstrated efficacy in automated assessment of upper limb Fugl-Meyer Assessment for paralysis rehabilitation. However, there is a lack of adequately sized studies to provide clinical support. Thus, we developed an automated system utilizing depth camera and machine learning, and assessed its feasibility and validity in a clinical setting. DESIGN: Validation and feasibility study of a measurement instrument based on single cross-sectional data...

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2...

OBJECTIVE: Individuals with psychiatric disorders are incarcerated at disproportionately high rates and often have low educational attainment. Access to psychiatric and education services within prisons has been described as inadequate, but recent data are lacking. The authors sought to assess the association of psychiatric disorders with both educational attainment before incarceration and access to psychiatric and education services during incarceration. METHODS: Data were from the 2016 Survey of Prison Inmates, a national survey of adults incarcerated in U...

BACKGROUND: Dental education in India predominantly relies on traditional lecture-based learning (LBL), which may hinder student engagement and learning outcomes. To address these limitations, innovative learning methodologies, such as spaced repetition learning (SRL), are imperative. SRL prioritizes active recall and can enhance long-term knowledge retention. This study aims to assess the effectiveness of SRL delivered through a mobile flashcard application, in enhancing knowledge retention among dental undergraduates...

OBJECTIVE: Predicting whether the posterior cruciate ligament (PCL) should be preserved during total knee arthroplasty (TKA) procedures is a complex task in the preoperative phase. The choice to either retain or excise the PCL has a substantial effect on the surgical outcomes and biomechanical integrity of the knee joint after the operation. To enhance surgeons' ability to predict the removal and retention of the PCL in patients before TKA, we developed machine learning models. We also identified significant feature factors that contribute to accurate predictions during this process...

BACKGROUND: Virtual reality (VR) training can enhance health professionals' learning. However, there are ambiguous findings on the effectiveness of VR as an educational tool in mental health. We therefore reviewed the existing literature on the effectiveness of VR training on health professionals' knowledge, skills, and attitudes in assessing and treating patients with mental health disorders. METHODS: We searched MEDLINE, PsycINFO (via Ovid), the Cochrane Library, ERIC, CINAHL (on EBSCOhost), Web of Science Core Collection, and the Scopus database for studies published from January 1985 to July 2023...

BACKGROUND: Dementia is a leading cause of disability in people older than 65 years worldwide. However, diagnosing dementia in its earliest symptomatic stages remains challenging. This study combined specific questions from the AD8 scale with comprehensive health-related characteristics, and used machine learning (ML) to construct diagnostic models of cognitive impairment (CI). METHODS: The study was based on the Shenzhen Healthy Ageing Research (SHARE) project, and we recruited 823 participants aged 65 years and older, who completed a comprehensive health assessment and cognitive function assessments...

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR...

How does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy...

Ischemic stroke segmentation at an acute stage is vital in assessing the severity of patients' impairment and guiding therapeutic decision-making for reperfusion. Although many deep learning studies have shown attractive performance in medical segmentation, it is difficult to use these models trained on public data with private hospitals' datasets. Here, we demonstrate an ensemble model that employs two different multimodal approaches for generalization, a more effective way to perform on external datasets...

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named "BraNet" for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models...