Video-based learning

Traditional block-based spatially scalable video coding has been studied for over twenty years. While significant advancements have been made, the scope for further improvement in compression performance is limited. Inspired by the success of learned video coding, in this paper, we propose an end-to-end learned spatially scalable video coding scheme-LSSVC, which provides a new solution for scalable video coding. In LSSVC, we propose to use the motion, texture, and latent information of the base layer (BL) as interlayer information...

BACKGROUND: Staffing and resource shortages, especially during the COVID-19 pandemic, have increased stress levels among health care workers. Many health care workers have reported feeling unable to maintain the quality of care expected within their profession, which, at times, may lead to moral distress and moral injury. Currently, interventions for moral distress and moral injury are limited. OBJECTIVE: This study has the following aims: (1) to characterize and reduce stress and moral distress related to decision-making in morally complex situations using a virtual reality (VR) scenario and a didactic intervention; (2) to identify features contributing to mental health outcomes using wearable, physiological, and self-reported questionnaire data; and (3) to create a personal digital phenotype profile that characterizes stress and moral distress at the individual level...

PURPOSE: Specialized robotic and surgical tools are increasing the complexity of operating rooms (ORs), requiring elaborate preparation especially when techniques or devices are to be used for the first time. Spatial planning can improve efficiency and identify procedural obstacles ahead of time, but real ORs offer little availability to optimize space utilization. Methods for creating reconstructions of physical setups, i.e., digital twins, are needed to enable immersive spatial planning of such complex environments in virtual reality...

AUDIENCE: This airway trainer modification is designed to instruct all levels of training in emergency medicine in order to familiarize trainees with airway anatomy and obtain superior views of the glottic inlet. INTRODUCTION: During intubation with a standard geometry laryngoscope, such as the Macintosh blade, placement of the distal end of the blade within the vallecula and engagement of the median glossoepiglottic fold, also referred to as the midline vallecular fold (MVF), has long been championed by experts in airway management for its ability to improve glottic inlet visualization...

MediaPipe Hand (MediaPipe) is an artificial intelligence (AI)-based pose estimation library. In this study, MediaPipe was combined with four machine learning (ML) models to estimate the rotation angle of the thumb. Videos of the right hands of 15 healthy volunteers were recorded and processed into 9000 images. The rotation angle of the thumb (defined as angle θ from the palmar plane, which is defined as 0°) was measured using an angle measuring device, expressed in a radian system. Angle θ was then estimated by the ML model by using parameters calculated from the hand coordinates detected by MediaPipe...

OBJECTIVE: To develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL). METHODS: The AI system was developed, trained and validated on 5362 images of 551 patients from three hospitals. Automated regions of interest (ROI) segmentation algorithm was utilized to construct image-level features. MIL was used to fusion image level results to patient level features, then the extracted features were modeled by seven machine learning algorithms...

The purpose of this study is to establish a deep learning automatic assistance diagnosis system for benign and malignant classification of mediastinal lesions in endobronchial ultrasound (EBUS) images. EBUS images are in the form of video and contain multiple imaging modes. Different imaging modes and different frames can reflect the different characteristics of lesions. Compared with previous studies, the proposed model can efficiently extract and integrate the spatiotemporal relationships between different modes and does not require manual selection of representative frames...

Recently, action recognition has attracted considerable attention in the field of computer vision. In dynamic circumstances and complicated backgrounds, there are some problems, such as object occlusion, insufficient light, and weak correlation of human body joints, resulting in skeleton-based human action recognition accuracy being very low. To address this issue, we propose a Multi-View Time-Series Hypergraph Neural Network (MV-TSHGNN) method. The framework is composed of two main parts: the construction of a multi-view time-series hypergraph structure and the learning process of multi-view time-series hypergraph convolutions...

AIM: Malocclusion has emerged as a burgeoning global public health concern. Individuals with an anterior crossbite face an elevated risk of exhibiting characteristics such as a concave facial profile, negative overjet, and poor masticatory efficiency. In response to this issue, we proposed a convolutional neural network (CNN)-based model designed for the automated detection and classification of intraoral images and videos. MATERIALS AND METHODS: A total of 1865 intraoral images were included in this study, 1493 (80%) of which were allocated for training and 372 (20%) for testing the CNN...

BACKGROUND: Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. While deep learning has been shown to uncover findings not recognized by clinicians, it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning...

Video-based referring expression comprehension is a challenging task that requires locating the referred object in each video frame of a given video. While many existing approaches treat this task as an object-tracking problem, their performance is heavily reliant on the quality of the tracking templates. Furthermore, when there is not enough annotation data to assist in template selection, the tracking may fail. Other approaches are based on object detection, but they often use only one adjacent frame of the key frame for feature learning, which limits their ability to establish the relationship between different frames...

BACKGROUND: Lumbar disc herniation is a common degenerative lumbar disease with an increasing incidence. Percutaneous endoscopic lumbar discectomy can treat lumbar disc herniation safely and effectively with a minimally invasive procedure. However, the learning curve of this technology is steep, which means that initial learners are often not sufficiently proficient in endoscopic operations, which can easily lead to iatrogenic damage. At present, the application of computer deep learning technology to clinical diagnosis, treatment, and surgical navigation has achieved satisfactory results...

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...

BACKGROUND AND OBJECTIVE: Timely stroke treatment can limit brain damage and improve outcomes, which depends on early recognition of the symptoms. However, stroke cases are often missed by the first respondent paramedics. One of the earliest external symptoms of stroke is based on facial expressions. METHODS: We propose a computerized analysis of facial expressions using action units to distinguish between Post-Stroke and healthy people. Action units enable analysis of subtle and specific facial movements and are interpretable to the facial expressions...

The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants...

Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform...

The development of robust artificial intelligence models for echocardiography has been limited by the availability of annotated clinical data. Here, to address this challenge and improve the performance of cardiac imaging models, we developed EchoCLIP, a vision-language foundation model for echocardiography, that learns the relationship between cardiac ultrasound images and the interpretations of expert cardiologists across a wide range of patients and indications for imaging. After training on 1,032,975 cardiac ultrasound videos and corresponding expert text, EchoCLIP performs well on a diverse range of benchmarks for cardiac image interpretation, despite not having been explicitly trained for individual interpretation tasks...

OBJECTIVE: We created a framework to assess the competency-based EEG curriculum, outlined by the International League Against Epilepsy (ILAE) through a video-based online educational resource ("Roadmap to EEGs") and assessed its effectiveness and feasibility in improving trainees' knowledge. METHODS: Ten video-based e-learning modules addressed seven key topics in EEG and epileptology (normal EEG, normal variants, EEG artifacts, interictal epileptiform discharges (IED), focal seizures, idiopathic generalized epilepsy (IGE), and developmental and epileptic encephalopathies (DEE))...

BACKGROUND: Digital modalities which enable asynchronous learning, such as audio podcasts and videos demonstrating procedures, may benefit acquisition and retention of knowledge and clinical skills. The main objective of this nationwide cross-sectional survey study was to evaluate key aspects and factors related to usage of audio podcasts and procedural videos in anaesthesiology and intensive care. METHODS: A 20-item multiple-choice-question online survey was created through a consensus process including pilot testing among residents and consultants...

The subject of anatomy is an integral component of medical and dental education which are constantly evolving. Hence, educators continuously attempt to take advantage of technological advancements to create resources that will improve students' higher cognitive skills. This article describes the creation of an e-learning resource in the form of an animated PowerPoint video that was designed based on evidence-based principles and educational theories to introduce the concept of tooth anatomy. Additionally, it outlines how this resource can be potentially integrated into a broader educational system as well as encourage anatomy and medical educators to use less complex technological equipment to create accessible educational resources...