Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?

被引:19
|
作者
Irmici, Giovanni [1 ]
Ce, Maurizio [1 ]
Caloro, Elena [1 ]
Khenkina, Natallia [1 ]
Della Pepa, Gianmarco [1 ]
Ascenti, Velio [1 ]
Martinenghi, Carlo [2 ]
Papa, Sergio [3 ]
Oliva, Giancarlo [4 ]
Cellina, Michaela [4 ]
机构
[1] Univ Milan, Postgrad Sch Radiodiagnost, Via Festa Perdono 7, I-20122 Milan, Italy
[2] Osped San Raffaele, Radiol Dept, Via Olgettina 60, I-20132 Milan, Italy
[3] Ctr Diagnost Italiano, Unit Diagnost Imaging & Stereotact Radiosurg, Via St Bon 20, I-20147 Milan, Italy
[4] Fatebenefratelli Hosp, Radiol Dept, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, I-20121 Milan, Italy
关键词
artificial intelligence; chest X-ray; emergency radiology; deep learning; chest radiography; DEEP NEURAL-NETWORK; CARDIOTHORACIC RATIO; IMAGES; COVID-19; MODEL; OPPORTUNITIES;
D O I
10.3390/diagnostics13020216
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.
引用
收藏
页数:18
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