Deep learning in chest radiography: Detection of findings and presence of change
Autor: | Victorine V. Muse, Atul Padole, Subba R. Digumarthy, Fatemeh Homayounieh, Pooja Rao, Mannudeep K. Kalra, Chayanin Nitiwarangkul, Preetham Putha, Ramandeep Singh, Amita Sharma, John A. Patti |
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Rok vydání: | 2018 |
Předmět: |
Male
Pulmonology Pleural effusion Radiography lcsh:Medicine 030218 nuclear medicine & medical imaging 0302 clinical medicine Mathematical and Statistical Techniques Medicine and Health Sciences Medical Personnel lcsh:Science Lung Optical Properties Observer Variation Multidisciplinary Pneumoconiosis Applied Mathematics Simulation and Modeling Middle Aged Reference Standards Radiographic Image Enhancement Professions medicine.anatomical_structure 030220 oncology & carcinogenesis Radiological weapon Area Under Curve Physical Sciences Female Radiography Thoracic Radiology Algorithms Statistics (Mathematics) Research Article Biotechnology Adult Opacity medicine.medical_specialty Catheters Materials Science Material Properties Research and Analysis Methods 03 medical and health sciences Deep Learning Radiologists medicine Humans Statistical Methods Aged Retrospective Studies Multiple abnormalities Receiver operating characteristic business.industry lcsh:R Biology and Life Sciences Retrospective cohort study medicine.disease Fibrosis Pleural Effusion ROC Curve People and Places lcsh:Q Population Groupings Medical Devices and Equipment business Mathematics Developmental Biology Forecasting |
Zdroj: | PLoS ONE PLoS ONE, Vol 13, Iss 10, p e0204155 (2018) |
ISSN: | 1932-6203 |
Popis: | Background Deep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs. Methods and findings We processed 874 de-identified frontal CXR from 724 adult patients (> 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis. Results About 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2–0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837–0.929 and 0.693–0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities. Conclusions DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings. |
Databáze: | OpenAIRE |
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