A Multi-step Integrative Workflow Implementation to Improve Documentation of Point of Care Ultrasound in Medical Intensive Care Unit [0.03%]
提高医疗重症监护病房(point-of-care ultrasound, POC-US) 文档质量的多步综合工作流程实施
Vishal Deepak,Haroon Ahmed,Joseph Minardi et al.
Vishal Deepak et al.
Point of care ultrasound (POCUS) provides quick bedside assessment for diagnosing and managing life-threatening conditions in critical care medicine. There has been increasing interest in developing infrastructure to archive images, record ...
Assessment of Age-Related Differences in Lower Leg Muscles Quality Using Radiomic Features of Magnetic Resonance Images [0.03%]
基于磁共振影像的放射组学特征评估下肢肌肉质量随年龄的变化规律研究
Takuro Shiiba,Suzumi Mori,Takuya Shimozono et al.
Takuro Shiiba et al.
Sarcopenia, characterised by a decline in muscle mass and strength, affects the health of the elderly, leading to increased falls, hospitalisation, and mortality rates. Muscle quality, reflecting microscopic and macroscopic muscle changes, ...
Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network [0.03%]
一种哥伦比亚远程卒中网络专用的ASPECTS自动分割和评分工具方法
Esteban Ortiz,Juan Rivera,Manuel Granja et al.
Esteban Ortiz et al.
To evaluate our two non-machine learning (non-ML)-based algorithmic approaches for detecting early ischemic infarcts on brain CT images of patients with acute ischemic stroke symptoms, tailored to our local population, to be incorporated in...
Vital Characteristics Cellular Neural Network (VCeNN) for Melanoma Lesion Segmentation: A Biologically Inspired Deep Learning Approach [0.03%]
用于黑色素病变分割的关键特征细胞神经网络（VCeNN）的生物启发式深度学习方法
Tongxin Yang,Qilin Huang,Fenglin Cai et al.
Tongxin Yang et al.
Cutaneous melanoma is a highly lethal form of cancer. Developing a medical image segmentation model capable of accurately delineating melanoma lesions with high robustness and generalization presents a formidable challenge. This study draws...
Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control [0.03%]
基于统计过程控制的出域检测和放射学数据监测方法研究
Ghada Zamzmi,Kesavan Venkatesh,Brandon Nelson et al.
Ghada Zamzmi et al.
Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework fo...
Septic Arthritis Modeling Using Sonographic Fusion with Attention and Selective Transformation: a Preliminary Study [0.03%]
基于声学融合注意和选择性转换的脓毒性关节炎建模：初步研究
Chung-Ming Lo,Kuo-Lung Lai
Chung-Ming Lo
Conventionally diagnosing septic arthritis relies on detecting the causal pathogens in samples of synovial fluid, synovium, or blood. However, isolating these pathogens through cultures takes several days, thus delaying both diagnosis and t...
Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data [0.03%]
基于多中心腹部MRI数据的ComBat调和对放射组学和深度特征的影响研究
Wei Jia,Hailong Li,Redha Ali et al.
Wei Jia et al.
ComBat harmonization has been developed to remove non-biological variations for data in multi-center research applying artificial intelligence (AI). We investigated the effectiveness of ComBat harmonization on radiomic and deep features ext...
Automated Three-Dimensional Imaging and Pfirrmann Classification of Intervertebral Disc Using a Graphical Neural Network in Sagittal Magnetic Resonance Imaging of the Lumbar Spine [0.03%]
基于腰椎磁共振矢状位图像的图形神经网络椎间盘三维影像自动化及Pfirrmann分级
David Baur,Richard Bieck,Johann Berger et al.
David Baur et al.
This study aimed to develop a graph neural network (GNN) for automated three-dimensional (3D) magnetic resonance imaging (MRI) visualization and Pfirrmann grading of intervertebral discs (IVDs), and benchmark it against manual classificatio...
Deep Learning for Automated Classification of Hip Hardware on Radiographs [0.03%]
基于X射线片的髋关节内固定物自动分类深度学习方法
Yuntong Ma,Justin L Bauer,Acacia H Yoon et al.
Yuntong Ma et al.
Purpose: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency amo...
Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance) [0.03%]
卷积神经网络在胸膜间皮瘤分割中的应用：概率图阈值分析（CALGB 30901，联盟）
Mena Shenouda,Eyjólfur Gudmundsson,Feng Li et al.
Mena Shenouda et al.
The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothelioma (PM) tumor delineations generated using a convolutional neural network (CNN). One hundred eighty-six CT scans from 48 PM patients were...