Leveraging Artificial Intelligence to Transform Thoracic Radiology for Lung Nodules and Lung Cancer: Applications, Challenges, and Future Directions [0.03%]
利用人工智能转化胸部放射学在肺结节和肺癌中的应用、挑战及未来方向
Geewon Lee,Hwan-Ho Cho,Dong Young Jeong et al.
Geewon Lee et al.
This review traces the historical path of artificial intelligence (AI) methods that have been applied to medical image interpretation. Early AI approaches, which were based on clinical expertise and domain-specific medical knowledge, establ...
Development and Validation of a Prediction Model of Hemoptysis After Computed Tomography-guided Percutaneous Transthoracic Needle Biopsy [0.03%]
基于胸部CT导引经皮肺穿刺活检后咯血的预测模型的建立与验证
Sowon Jang,Minseon Kim,Jeong Sub Lee et al.
Sowon Jang et al.
Purpose: To develop and validate a nomogram to predict hemoptysis after percutaneous transthoracic needle biopsy (PTNB) by integrating clinical and radiologic data, facilitating pre-biopsy decision-making. ...
Variability in Mediastinal Lymph Node Measurements in Chest Contrast-enhanced CT: Time to Change the Paradigm? [0.03%]
胸部增强CT中纵隔淋巴结测量的变异性：是改变范式的时候了吗？
Alon Olesinksi,Richard Lederman,Yusef Azraq et al.
Alon Olesinksi et al.
Purpose: Measurement of mediastinal lymph nodes (LNs) is an integral part of patient assessment, and is performed by manually measuring the short axis length (SAL) of the LNs on axial slices. LNs with SAL ≥10 mm are cons...
Quantitative Chest Computed Tomography and Machine Learning for Subphenotyping Small Airways Disease in Long COVID [0.03%]
定量胸部计算机断层扫描和机器学习在长新冠小气道疾病亚表型分型中的应用
Rodrigo Caruso Chate,Carlos Roberto Ribeiro Carvalho,Marcio Valente Yamada Sawamura et al.
Rodrigo Caruso Chate et al.
Purpose: To investigate imaging phenotypes in posthospitalized COVID-19 patients by integrating quantitative CT (QCT) and machine learning (ML), with a focus on small airway disease (SAD) and its correlation with plethysm...
Evaluating the Status of Cardiac Imaging Training in Radiology Residency Programs in the United States [0.03%]
评估美国放射 residency 培训项目中心脏影像学培训状况的研究
Nitai Bar,Ronald L Eisenberg,Yuval Liberman et al.
Nitai Bar et al.
Purpose: Cardiac imaging is an integral part of modern diagnostic imaging and a subject heavily tested on the Radiology Core exam. Therefore, radiology residency programs should provide adequate training in this area. Thi...
Myocardial Fibrosis Evaluated by T1 Mapping and Its Relationship to Left Ventricular Hypertrophy, Strain, and T2 Value in Hypertrophic Cardiomyopathy Without Late Gadolinium Enhancement [0.03%]
无延迟钆增强肥厚型心肌病患者的心肌纤维化T1图评估及其与左室肥厚、应变及T2值的关系
Yang Zhi,Tian-Yue Zhang,Fu-Dan Gui et al.
Yang Zhi et al.
Purpose: The aim of this study was to evaluate T1 and T2 values and to investigate their association with left ventricular (LV) hypertrophy and strains in hypertrophic cardiomyopathy (HCM) without late gadolinium enhancem...
A Noninvasive Prognostic Model for Pulmonary Arterial Hypertension Associated With Connective Tissue Diseases Based on Multislice Chest Computed Tomography Parameters [0.03%]
基于多层胸部CT参数的与结缔组织病相关的肺动脉高压非侵入性预后模型
Yingheng Huang,Chunfang Zhang,Huangshu Ye et al.
Yingheng Huang et al.
Purpose: Patients with connective tissue diseases (CTDs) and pulmonary arterial hypertension (PAH) have a poor prognosis, and there is a lack of effective noninvasive prognostic tools. This study aimed to retrospectively ...
Chest Computed Tomography-Based Radiomics and Machine Learning for Classifying Mediastinal Lymphadenopathy Caused By Hematologic Malignancies and Metastatic Abdominopelvic Solid Cancers [0.03%]
基于胸部CT的影像组学和机器学习鉴别血液系统恶性肿瘤与腹盆腔实体瘤纵隔淋巴结转移的研究
Haoru Wang,Qian Hu,Yingxue Tong et al.
Haoru Wang et al.
Purpose: To evaluate the role of chest CT radiomics in classifying mediastinal lymphadenopathy caused by hematologic malignancies and abdominopelvic solid cancers. ...
Empowering Radiologists With ChatGPT-4o: Comparative Evaluation of Large Language Models and Radiologists in Cardiac Cases [0.03%]
利用ChatGPT-4o增强放射科医生的能力：大型语言模型与放射科医生在心脏病案例中的比较评估
Turay Cesur,Yasin Celal Gunes,Eren Camur et al.
Turay Cesur et al.
Purpose: This study evaluated the diagnostic accuracy and differential diagnostic capabilities of 12 Large Language Models (LLMs), one cardiac radiologist, and 3 general radiologists in cardiac radiology. The impact of th...