The value of Artificial Intelligence on the detection of pathologies in chest radiographs compared with High Resolution Multi slice Computed Tomography
Albadry, D., Khater, H., Reffat, M. (2022). The value of Artificial Intelligence on the detection of pathologies in chest radiographs compared with High Resolution Multi slice Computed Tomography. EKB Journal Management System, 39(Special issue (Radiology)), 303-314. doi: 10.21608/bmfj.2021.79899.1424
Dunia Albadry; Hamada Mohamed Khater; Medhat Reffat. "The value of Artificial Intelligence on the detection of pathologies in chest radiographs compared with High Resolution Multi slice Computed Tomography". EKB Journal Management System, 39, Special issue (Radiology), 2022, 303-314. doi: 10.21608/bmfj.2021.79899.1424
Albadry, D., Khater, H., Reffat, M. (2022). 'The value of Artificial Intelligence on the detection of pathologies in chest radiographs compared with High Resolution Multi slice Computed Tomography', EKB Journal Management System, 39(Special issue (Radiology)), pp. 303-314. doi: 10.21608/bmfj.2021.79899.1424
Albadry, D., Khater, H., Reffat, M. The value of Artificial Intelligence on the detection of pathologies in chest radiographs compared with High Resolution Multi slice Computed Tomography. EKB Journal Management System, 2022; 39(Special issue (Radiology)): 303-314. doi: 10.21608/bmfj.2021.79899.1424

The value of Artificial Intelligence on the detection of pathologies in chest radiographs compared with High Resolution Multi slice Computed Tomography

Benha Medical Journal
Article 231, Volume 39, Special issue (Radiology), May and June 2022, Page 303-314  XML PDF (1.06 MB)
Document Type: Original Article
DOI: 10.21608/bmfj.2021.79899.1424
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Authors
Dunia Albadry email 1; Hamada Mohamed Khater2; Medhat Reffat3
1M.B.Ch.B
2Lecturer of Radiodiagnosis Faculty of Medicine, Benha University
3Professor and Head of Radiology Department Faculty of Medicine-Benha University
Abstract
Background: Over the last few years, there has been increasing interest in the use of deep learning algorithms to assist with abnormality detection on medical images. Aim of this study was to investigate the performance of Artificial Intelligence on the detection of pathologies in chest radiographs compared with High Resolution Multi slice Computed Tomography. Methods: this prospective study was done on 200 cases, who underwent automatic detection of chest disease based on chest radiography in a comprehensive survey on computer-aided detection systems, focuses on the artificial intelligence technology applied in chest radiography to detect the presence of different pathologies, including pleural effusion, pneumothorax, pneumonia, pulmonary masses, and nodules in AP and PA -view chest radiographs using modern digital radiography. Using High Resolution Multi slice Computed Tomography ( 16/64/128 detector ) for chest examination for abnormality detected by artificial intelligence technology. Axial scanning extending from base of the neck down below the diaphragm with coronal & sagittal reformate images. Results: The mean age of patients was 46.3 years and 123 patients (61.5 %) were males and 77 patients (38.5 %) were female. There was a statistically significant difference between CAD and MDCT diagnosed by radiologist according to Sensitivity, p < 0.001. Conclusion: Inspite CAD system has established fair accurancy , the need of more accurate algorithm is nessary to determine if it can replicate MDCT and radiologist observation of abnormality on chest X-rays
Keywords
Keywords: Artificial Intelligence; chest radiographs High Resolution; Multi slice; CT
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