Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population [0.03%]
儿童不是成人的缩小版：解决成人深度学习CT器官分割模型在儿科人群中的有限适用性问题
Devina Chatterjee,Adway Kanhere,Florence X Doo et al.
Devina Chatterjee et al.
Deep learning (DL) tools developed on adult data sets may not generalize well to pediatric patients, posing potential safety risks. We evaluated the performance of TotalSegmentator, a state-of-the-art adult-trained CT organ segmentation mod...
Effect of Deep Learning Image Reconstruction on Image Quality and Pericoronary Fat Attenuation Index [0.03%]
基于深度学习的图像重建技术对冠脉CTA图像质量及冠状动脉周围脂肪CT值的影响研究
Junqing Mei,Chang Chen,Ruoting Liu et al.
Junqing Mei et al.
To compare the image quality and fat attenuation index (FAI) of coronary artery CT angiography (CCTA) under different tube voltages between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASIR-...
Intra-Individual Reproducibility of Automated Abdominal Organ Segmentation-Performance of TotalSegmentator Compared to Human Readers and an Independent nnU-Net Model [0.03%]
自动腹部器官分割的组内可重复性：TotalSegmentator与人工阅片及独立nnU-Net模型的表现比较
Lorraine Abel,Jakob Wasserthal,Manfred T Meyer et al.
Lorraine Abel et al.
The purpose of this study is to assess segmentation reproducibility of artificial intelligence-based algorithm, TotalSegmentator, across 34 anatomical structures using multiphasic abdominal CT scans comparing unenhanced, arterial, and porta...
Teleradiology-Based Referrals for Patients with Gastroenterological Diseases Between Tertiary and Regional Hospitals: A Hospital-to-Hospital Approach [0.03%]
基于远程放射学的三级医院与区域医院之间的胃肠病患者会诊：医院对医院的方法
Kosuke Suzuki,Hiroaki Saito,Yoshika Saito et al.
Kosuke Suzuki et al.
Teleradiology is recognized for fostering collaboration between regional and tertiary hospitals. However, its application in gastroenterological diseases remains underexplored. This study aimed to assess the effectiveness of teleradiology i...
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...