Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen

Sungwon Lee; Ronald M Summers

doi:10.1016/j.rcl.2021.07.001

Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen

Radiol Clin North Am. 2021 Nov;59(6):987-1002. doi: 10.1016/j.rcl.2021.07.001.

Authors

Sungwon Lee¹, Ronald M Summers²

Affiliations

¹ Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA.
² Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA. Electronic address: rms@nih.gov.

PMID: 34689882
DOI: 10.1016/j.rcl.2021.07.001

Abstract

Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have achieved performance comparable to humans in less time for several organ segmentation tasks, and some lesion detection and classification tasks. This article introduces the current published articles of AI applied to chest and abdominal radiology, including organ segmentation, lesion detection, classification, and predicting prognosis.

Keywords: Abdomen; Artificial intelligence; Chest; Deep learning; Pelvic; Radiology; Thorax.

Publication types

Review

MeSH terms

Artificial Intelligence*
Gastrointestinal Diseases / diagnostic imaging*
Humans
Image Interpretation, Computer-Assisted / methods*
Radiography, Abdominal / methods*
Radiography, Thoracic / methods*
Thoracic Diseases / diagnostic imaging*