You are here

Applying Artificial Intelligence and Radiomics for Computer Aided Diagnosis and Risk Assessment in Chest Radiographs.

Printer-friendly versionPrinter-friendly version
Guan Y, Li M, Jaeger S, Lure F, Raptopoulos V, Lu P, Folio LR, Candemir S, Antani SK, Siegelman J, Li J, Wu T, Thoma GR, Qu S
2nd Conference on Machine Intelligence in Medical Imaging (CMIMI) of the Society for Imaging Informatics in Medicine (SIIM), Poster, 2017.
Abstract: 

In urban areas of China,air pollutionand particulate exposure seriously affect population lung and cardiovascular health. • The incidence and severity of lung cancer and lung diseases progressively increase each year. • Chest radiography is the most utilized imaging technique among all modalities because it can provide an overall health conditions aiding diagnosis for the thoracic region. • In China, chest radiography is also a standard procedure for the annual physical health exam and for job entry. • Over 800 million chest radiographs annually are interpreted in China for multiple diseases by wide varieties of radiologists ranging from small amount of highly experienced  to large amount of less or little experienced radiologists,who may have inadequate formalized training. • For these reasons timeliness of accurate interpretation can be poor. • Excluding infection and trauma, most chest diseases are not acute. By the time symptoms become obvious or severe, the condition is already advanced. • Salient signs in “normal” chest radiographs can be used to analyze the disease risk for diseases and triage examinations which need further, urgent review. • Further development of computational decision support tools should improve diagnosing multiple diseases earlier and analyzing risks for clinically asymptomatic patients from chest radiographs; and thus will improve the quality of healthcare.
 

Guan Y, Li M, Jaeger S, Lure F, Raptopoulos V, Lu P, Folio LR, Candemir S, Antani SK, Siegelman J, Li J, Wu T, Thoma GR, Qu S. Applying Artificial Intelligence and Radiomics for Computer Aided Diagnosis and Risk Assessment in Chest Radiographs. 2nd Conference on Machine Intelligence in Medical Imaging (CMIMI) of the Society for Imaging Informatics in Medicine (SIIM), Poster, 2017.