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Teng PH, Liang CH, Lin Y, Alberich-Bayarri A, González RL, Li PW, Weng YH, Chen YT, Lin CH, Chou KJ, Chen YS, Wu FZ. Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms. Medicine (Baltimore) 2021; 100:e26270. [PMID: 34115023 PMCID: PMC8202613 DOI: 10.1097/md.0000000000026270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 05/21/2021] [Indexed: 01/04/2023] Open
Abstract
The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, P < .001). In addition, heat-map algorithm could automatically detect and localize pulmonary nodules/masses in chest radiographs with high specificity.In conclusion, the deep-learning based computer-aided diagnosis system through 4 algorithms could potentially assist trained radiographers by increasing the confidence and access to chest radiograph interpretation in the age of digital age with the growing demand of medical imaging usage and radiologist burnout.
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Affiliation(s)
- Pai-Hsueh Teng
- Department of Radiology, Kaohsiung Veterans General Hospital
- Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung
| | - Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yun Lin
- Department of Radiology, Kaohsiung Veterans General Hospital
| | - Angel Alberich-Bayarri
- Radiology Department, Hospital Universitarioy Polite’cnico La Fe and Biomedical Imaging Research Group (GIBI230)
- QUIBIM SL, Valencia, Spain
| | - Rafael López González
- Radiology Department, Hospital Universitarioy Polite’cnico La Fe and Biomedical Imaging Research Group (GIBI230)
- QUIBIM SL, Valencia, Spain
| | - Pin-Wei Li
- Department of Radiology, Kaohsiung Veterans General Hospital
| | - Yu-Hsin Weng
- Department of Radiology, Kaohsiung Veterans General Hospital
| | - Yi-Ting Chen
- Department of Radiology, Kaohsiung Veterans General Hospital
| | - Chih-Hsien Lin
- Department of Radiology, Kaohsiung Veterans General Hospital
| | - Kang-Ju Chou
- Institute of Clinical Medicine, National Yang Ming University, Taipei
| | - Yao-Shen Chen
- Institute of Clinical Medicine, National Yang Ming University, Taipei
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital
- Faculty of Medicine, School of Medicine, i Institute of Clinical Medicine, National Yang Ming Chiao Tung University
- Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
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Hsiao CC, Teng PH, Wu YJ, Shen YW, Mar GY, Wu FZ. Severe, but not mild to moderate, non-alcoholic fatty liver disease associated with increased risk of subclinical coronary atherosclerosis. BMC Cardiovasc Disord 2021; 21:244. [PMID: 34011282 PMCID: PMC8132380 DOI: 10.1186/s12872-021-02060-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 05/12/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) is associated with high risk of cardiovascular disease. The prevalence is increasing to 45-65% in the general population with routine health check-up, and most subjects have the mild degree NAFLD in recent years. Moreover, there are no studies on the association between NAFLD severity and coronary atherosclerosis in the real-world setting by ultrasonography. METHODS The aim of this study was to determine the relationship between the severity of NAFLD and subclinical coronary atherosclerosis. Overall, 817 subjects meet criteria for NAFLD were enrolled in the retrospective cohort study (155 subjects were excluded). The severity of NAFLD was divided into the normal, mild, moderate and severe degree based on the finding of abdominal ultrasonography. The assessment of coronary atherosclerosis was based on CAC scan/coronary CT angiography finding in terms of CAC score ≧ 100, CAC score ≧ 400, CAD-RADS ≧ 3 and presence of vulnerable plaque(s). RESULTS A significant linear trend was observed between the severity of NAFLD and subclinical coronary atherosclerosis. Compared with the reference group (including normal, mild, and moderate NAFLD), severe degree NAFLD was the independently associated risk of subclinical coronary atherosclerosis in term of CAC score ≧ 100, CAC score ≧ 400, CAD-RADS ≧ 3 and presence of vulnerable plaque(s) based on binary logistic regression after adjustment for FRS score and body fat percentage. CONCLUSIONS Severe degree, but not mild to moderate, was associated with high risk of subclinical coronary atherosclerosis, independently of FRS score and body-fat percentage.
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Affiliation(s)
- Chia-Chi Hsiao
- Section of Thoracic and Circulation Imaging, Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Ta-Chung 1st Road, Kaohsiung, 81362, Taiwan.,Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan
| | - Pai-Hsueh Teng
- Section of Thoracic and Circulation Imaging, Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Ta-Chung 1st Road, Kaohsiung, 81362, Taiwan.,Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan
| | - Yun-Ju Wu
- Section of Thoracic and Circulation Imaging, Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Ta-Chung 1st Road, Kaohsiung, 81362, Taiwan
| | - Yi-Wen Shen
- Section of Thoracic and Circulation Imaging, Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Ta-Chung 1st Road, Kaohsiung, 81362, Taiwan
| | - Guang-Yuan Mar
- Physical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Fu-Zong Wu
- Section of Thoracic and Circulation Imaging, Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Ta-Chung 1st Road, Kaohsiung, 81362, Taiwan. .,Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan. .,Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan. .,Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
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