Video-based learning

PURPOSE: The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos. METHODS: A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported...

BACKGROUND: Inflammatory back pain is a chronic condition with localized pain, particularly in the axial spine and sacroiliac joints, that is associated with morning stiffness and improves with exercise. YouTube is the second most frequently used social media platform for accessing health information. This study sought to investigate the quality and reliability of YouTube videos on inflammatory back pain (IBP). METHODS: The study design was planned as cross-sectional...

Algorithm audits have increased in recent years due to a growing need to independently assess the performance of automatically curated services that process, filter and rank the large and dynamic amount of information available on the Internet. Among several methodologies to perform such audits, virtual agents stand out because they offer the ability to perform systematic experiments, simulating human behaviour without the associated costs of recruiting participants. Motivated by the importance of research transparency and replicability of results, this article focuses on the challenges of such an approach...

Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which models the procedure at four levels of granularity - corridor, activity, view, and frame value - simulating the pelvic fracture fixation workflow as a Markov process to provide fully annotated training data...

BACKGROUND: Early identification of autism spectrum disorder (ASD) improves long-term outcomes, yet significant diagnostic delays persist. METHODS: A retrospective cohort of 449 children (ASD: 246, typically developing [TD]: 203) was used for model development. Eye-movement data were collected from the participants watching videos that featured eye-tracking paradigms for assessing social and non-social cognition. Five machine learning algorithms, namely random forest, support vector machine, logistic regression, artificial neural network, and extreme gradient boosting, were trained to classify children with ASD and TD...

BACKGROUND: eLearning can be an effective tool to achieve learning objectives. It facilitates asynchronous distance learning, increasing flexibility for learners and instructors. In this context, the high educational value of videos provides an invaluable primary component for longitudinal digital curricula, especially for maintaining knowledge on otherwise rarely taught subjects. Although literature concerning eLearning evaluation exists, research comprehensively describing how to design effective educational videos is lacking...

BACKGROUND: Video-demonstrated action-observation-execution is an effective intervention for motor re-learning in stroke rehabilitation. But customization of video for each task repeatedly questions its feasibility within limited resources, particularly for daily routine practice and in community settings. Physiotherapist-demonstrated action-observation-execution is a practical intervention based on the principle of observation and consecutive repetitions of observed real, live movements...

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0...

The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process...

With declining populations in the wild, captive rescue and breeding have become one of the most important ways to protect pangolins from extinction. At present, the success rate of artificial breeding is low, due to the insufficient understanding of the breeding behavior characteristics of pangolins. The automatic recognition method based on machine vision not only monitors for 24 h but also reduces the stress response of pangolins. This paper aimed to establish a temporal relation and attention mechanism network (Pangolin breeding attention and transfer network, PBATn) to monitor and recognize pangolin behaviors, including breeding and daily behavior...

Video-based person re-identification (ReID) aims to exploit relevant features from spatial and temporal knowledge. Widely used methods include the part- and attention-based approaches for suppressing irrelevant spatial-temporal features. However, it is still challenging to overcome inconsistencies across video frames due to occlusion and imperfect detection. These mismatches make temporal processing ineffective and create an imbalance of crucial spatial information. To address these problems, we propose the Spatiotemporal Multi-Granularity Aggregation (ST-MGA) method, which is specifically designed to accumulate relevant features with spatiotemporally consistent cues...

Video super-resolution (VSR) remains challenging for real-world applications due to complex and unknown degradations. Existing methods lack the flexibility to handle video sequences with different degradation levels, thus failing to reflect real-world scenarios. To address this problem, we propose a degradation-adaptive video super-resolution network (DAVSR) based on a bidirectional propagation network. Specifically, we adaptively employ three distinct degradation levels to process input video sequences, aiming to obtain training pairs that reflect a variety of real-world corrupted images...

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources...

This study aimed to develop a problem-based learning program for occupational health nursing using smart learning, and to report the program's effects on occupational health knowledge and nursing professionalism. A quasi-experimental study was performed using a non-equivalent control group pre-test-post-test design. An occupational health nursing problem-based learning program using smart learning was developed, and students produced videos containing problem-solving strategies in groups. The experimental and control groups consisted of 34 and 29 participants, respectively...

PURPOSE: To address a gap in radiation oncology education in low- and middle-income countries (LMICs), we sought to evaluate the effectiveness and generalizability of a refined curriculum on intensity modulated radiotherapy (IMRT) offered to existing radiation therapy (RT) clinics across Africa and Latin America (LATAM) at no cost. METHODS: A curriculum was created based on prior needs assessments and adapted for participating medical physicists, radiation oncologists, radiation therapists, and trainees in LMICs...

The purpose of this work is to analyze how new technologies can enhance clinical practice while also examining the physical traits of emotional expressiveness of face expression in a number of psychiatric illnesses. Hence, in this work, an automatic facial expression recognition system has been proposed that analyzes static, sequential, or video facial images from medical healthcare data to detect emotions in people's facial regions. The proposed method has been implemented in five steps. The first step is image preprocessing, where a facial region of interest has been segmented from the input image...

The field of cancer genetics has evolved significantly over the past 30 years. Genetic testing has become less expensive and more comprehensive which has changed practice patterns. It is no longer necessary to restrict testing to those with the highest likelihood of testing positive. In addition, we have learned that the criteria developed to determine who has the highest likelihood of testing positive are neither sensitive nor specific. As a result, the field is moving from testing only the highest risk patients identified based on testing criteria to testing all cancer patients...

In signal processing applications, the multipliers are essential component of arithmetic functional units in many applications, like digital signal processors, image/video processing, Machine Learning, Cryptography and Arithmetic & Logical units (ALU). In recent years, Profuse multipliers are there. In that, Vedic multiplier is one of the high-performance multiplications and it is used to signal/image processing applications. In order to ameliorate the performance of this multiplier further, by proposed a novel multiplier using hybrid compressor...

Heart University [https://www.heartuniversity.org/] is a free educational website providing structured training curricula with knowledge-based testing and access to webinars and conference recordings for practicing and in-training providers of paediatric and congenital cardiac care. To date, there are over 15,000 registered website users from over 140 countries on Heart University, with over 2,000 training modules and/or recorded educational videos. Heart University has developed an "asynchronous" educational lecture series entitled "Pediatric and Congenital Cardiac Care in Resource-Limited Settings...

Accurate and objective pain evaluation is crucial in developing effective pain management protocols, aiming to alleviate distress and prevent patients from experiencing decreased functionality. A multimodal automatic assessment framework for acute pain utilizing video and heart rate signals is introduced in this study. The proposed framework comprises four pivotal modules: the Spatial Module , responsible for extracting embeddings from videos; the Heart Rate Encoder , tasked with mapping heart rate signals into a higher dimensional space; the AugmNet , designed to create learning-based augmentations in the latent space; and the Temporal Module , which utilizes the extracted video and heart rate embeddings for the final assessment...