Human-level COVID-19 Diagnosis from Low-dose CT Scans Using a Two-stage Time-distributed Capsule Network
Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-Ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. The AI model achieves COVID-19 sensitivity of 89.5% +\- 0.11, CAP sensitivity of 95% +\- 0.11, normal cases sensitivity (specificity) of 85.7% +\- 0.16, and accuracy of 90% +\- 0.06. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of 94.3% +\- pm 0.05, CAP sensitivity of 96.7% +\- 0.07, normal cases sensitivity (specificity) of 91% +\- 0.09 , and accuracy of 94.1% +\- 0.03. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.
Code (1)
Tasks
Computed Tomography (CT)COVID-19 DiagnosisSensitivitySpecificityMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Web Diagnosis for COVID-19 and Pneumonia Based on Computed Tomography Scans and X-rays
Pneumonia and COVID-19 are respiratory illnesses, the last caused by the severe acute respiratory syndrome virus, coronavirus 2 (SARS-CoV-2). Traditional detection processes can be slow, prone to errors, and laborious, l…
Computed Tomography (CT)Robust Framework for COVID-19 Identification from a Multicenter Dataset of Chest CT Scans
The objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on chest CT scans acquired in different imaging centers…
Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets
Deep learning has been used to assist in the analysis of medical imaging. One such use is the classification of Computed Tomography (CT) scans when detecting for COVID-19 in subjects. This paper presents Cov3d, a three d…
Computed Tomography (CT)COVID-19 DiagnosisMedical Image AnalysisUsing a Generative Adversarial Network for CT Normalization and its Impact on Radiomic Features
Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifying and classifying potentially malignant pulmonary nodules on chest CT scans using morphology and texture-based (radiomic) features. However, radi…
Generative Adversarial NetworkSSIMGenerative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study
Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, n…
Computed Tomography (CT)DecoderDeep AttentionDenoising+3