AIMC Topic: Prospective Studies
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Identifying pediatric heart murmurs and distinguishing innocent from pathologic using deep learning.
Artificial intelligence in medicine
Apr 4, 2024
OBJECTIVE: To develop a deep learning algorithm to perform multi-class classification of normal pediatric heart sounds, innocent murmurs, and pathologic murmurs.
Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm.
Clinics and research in hepatology and gastroenterology
Apr 4, 2024
BACKGROUND: In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE.
Mo-fi-disc scoring system: Towards understanding the radiological tools to better delineate the disease process and enhancing our solutions for low back pain in artificial intelligence era.
Journal of orthopaedic science : official journal of the Japanese Orthopaedic Association
Apr 3, 2024
BACKGROUND: 'Mo-fi-disc' is a new scoring system that quantifies degeneration of the lumbar spine and predicts the intensity of low back pain (LBP). However, its association with LBP-related disability is unknown. In the present study, we aimed to an...
Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study.
Endocrine
Apr 3, 2024
PURPOSE: This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of lateral cervical lymph nodes (LNs) on original ultrasound images of papillary thyroid carcinoma (PTC) pa...
Deep learning reconstruction for turbo spin echo to prospectively accelerate ankle MRI: A multi-reader study.
European journal of radiology
Apr 3, 2024
PURPOSE: To evaluate a deep learning reconstruction for turbo spin echo (DLR-TSE) sequence of ankle magnetic resonance imaging (MRI) in terms of acquisition time, image quality, and lesion detectability by comparing with conventional TSE.
Development and prospective clinical validation of a convolutional neural network for automated detection and segmentation of focal cortical dysplasias.
Epilepsy research
Apr 3, 2024
PURPOSE: Focal cortical dysplasias (FCDs) are a leading cause of drug-resistant epilepsy. Early detection and resection of FCDs have favorable prognostic implications for postoperative seizure freedom. Despite advancements in imaging methods, FCD det...
Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach.
Computer methods in biomechanics and biomedical engineering
Apr 2, 2024
Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac an...
Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions.
Tomography (Ann Arbor, Mich.)
Apr 2, 2024
To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conduc...
Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment.
International journal of surgery (London, England)
Apr 1, 2024
BACKGROUND: Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and...
Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study.
The Journal of pediatrics
Mar 30, 2024
OBJECTIVE: The objective of this study was to predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data.
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