AIMC Topic: Video Recording

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Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.

Sensors (Basel, Switzerland)
In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystag...

Real-time sports injury monitoring system based on the deep learning algorithm.

BMC medical imaging
In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detection is performed to capture human...

Training deep learning based dynamic MR image reconstruction using open-source natural videos.

Scientific reports
To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding samp...

Automated tear film break-up time measurement for dry eye diagnosis using deep learning.

Scientific reports
In the realm of ophthalmology, precise measurement of tear film break-up time (TBUT) plays a crucial role in diagnosing dry eye disease (DED). This study aims to introduce an automated approach utilizing artificial intelligence (AI) to mitigate subje...

Real-time detection of active bleeding in laparoscopic colectomy using artificial intelligence.

Surgical endoscopy
BACKGROUND: Most intraoperative adverse events (iAEs) result from surgeons' errors, and bleeding is the majority of iAEs. Recognizing active bleeding timely is important to ensure safe surgery, and artificial intelligence (AI) has great potential for...

Laryngeal Cancer Screening During Flexible Video Laryngoscopy Using Large Computer Vision Models.

The Annals of otology, rhinology, and laryngology
OBJECTIVE: Develop an artificial intelligence assisted computer vision model to screen for laryngeal cancer during flexible laryngoscopy.

Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals.

Sensors (Basel, Switzerland)
Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation...

Development and validation of a novel colonoscopy withdrawal time indicator based on YOLOv5.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: The study aims to introduce a novel indicator, effective withdrawal time (WTS), which measures the time spent actively searching for suspicious lesions during colonoscopy and to compare WTS and the conventional withdrawal time (WT...

HFSCCD: A Hybrid Neural Network for Fetal Standard Cardiac Cycle Detection in Ultrasound Videos.

IEEE journal of biomedical and health informatics
In the fetal cardiac ultrasound examination, standard cardiac cycle (SCC) recognition is the essential foundation for diagnosing congenital heart disease. Previous studies have mostly focused on the detection of adult CCs, which may not be applicable...

Detecting the symptoms of Parkinson's disease with non-standard video.

Journal of neuroengineering and rehabilitation
BACKGROUND: Neurodegenerative diseases, such as Parkinson's disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, a...