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PubMed Publications| Keyword search (4,164 papers available) |  |
"Naderkhani F" Authored Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Differentiation of COVID-19 from other types of viral pneumonia and severity scoring on baseline chest radiographs: Comparison of deep learning with multi-reader evaluation | Enshaei N; Mohammadi A; Naderkhani F; Daneman N; Abu Mughli R; Anconina R; Berger FH; Kozak RA; Mubareka S; Villanueva Campos AM; Narang K; Vivekanandan T; Chan AK; Lam P; Andany N; Oikonomou A; | 40729327 ENCS |
| 2 | Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals | Montazerin M; Rahimian E; Naderkhani F; Atashzar SF; Yanushkevich S; Mohammadi A; | 37419881 ENCS |
| 3 | Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans | Khademi S; Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Oikonomou A; Shafiee A; Babaki Fard F; Plataniotis KN; Mohammadi A; | 36862633 ENCS |
| 4 | Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network | Afshar P; Rafiee MJ; Naderkhani F; Heidarian S; Enshaei N; Oikonomou A; Babaki Fard F; Anconina R; Farahani K; Plataniotis KN; Mohammadi A; | 35318368 ENCS |
| 5 | COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images | Enshaei N; Oikonomou A; Rafiee MJ; Afshar P; Heidarian S; Mohammadi A; Plataniotis KN; Naderkhani F; | 35217712 ENCS |
| 6 | COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans | Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Babaki Fard F; Samimi K; Atashzar SF; Oikonomou A; Plataniotis KN; Mohammadi A; | 34113843 ENCS |
| 7 | COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images. | Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A | 32958971 ENCS |
| Title: | Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network | ||||
| Authors: | Afshar P, Rafiee MJ, Naderkhani F, Heidarian S, Enshaei N, Oikonomou A, Babaki Fard F, Anconina R, Farahani K, Plataniotis KN, Mohammadi A | ||||
| Link: | pubmed.ncbi.nlm.nih.gov/35318368/ | ||||
| DOI: | 10.1038/s41598-022-08796-8 | ||||
| Publication: | Scientific reports | ||||
| Keywords: | |||||
| PMID: | 35318368 | Category: | Date Added: | 2022-03-23 | |
| Dept Affiliation: |
ENCS
1 Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada. 2 Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada. 3 Department of Medicine and Diagnostic Radiology, McGill University Health Center-Research Institute, Montreal, QC, Canada. 4 Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada. 5 Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada. 6 Faculty of Medicine, University of Montreal, Montreal, QC, Canada. 7 Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), Rockville, MD, USA. 8 Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada. arash.mohammadi@concordia.ca. |
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Description: |
Reverse transcription-polymerase chain reaction 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. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. 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. |
