A non-invasive AI-based system for precise grading of anosmia in COVID-19 using neuroimaging
Hossam Magdy Balaha,
Mayada Elgendy,
Ahmed Alksas,
Mohamed Shehata,
Norah Saleh Alghamdi,
Fatma Taher,
Mohammed Ghazal,
Mahitab Ghoneim,
Eslam Hamed Abdou,
Fatma Sherif,
Ahmed Elgarayhi,
Mohammed Sallah,
Mohamed Abdelbadie Salem,
Elsharawy Kamal,
Harpal Sandhu,
Ayman El-Baz
Affiliations
Hossam Magdy Balaha
Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA; Corresponding author.
Mayada Elgendy
Applied Theoretical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
Ahmed Alksas
Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
Mohamed Shehata
Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
Norah Saleh Alghamdi
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Fatma Taher
The College of Technological Innovation, Zayed University, Dubai, 19282, United Arab Emirates
Mohammed Ghazal
Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
Mahitab Ghoneim
Department of Radiology, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt
Eslam Hamed Abdou
Otolaryngology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt
Fatma Sherif
Department of Radiology, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt
Ahmed Elgarayhi
Applied Theoretical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
Mohammed Sallah
Applied Theoretical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt; Department of Physics, College of Sciences, University of Bisha, Saudi Arabia
Mohamed Abdelbadie Salem
Otolaryngology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt
Elsharawy Kamal
Otolaryngology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt
Harpal Sandhu
Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
Ayman El-Baz
Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA; Corresponding author.
COVID-19 (Coronavirus), an acute respiratory disorder, is caused by SARS-CoV-2 (coronavirus severe acute respiratory syndrome). The high prevalence of COVID-19 infection has drawn attention to a frequent illness symptom: olfactory and gustatory dysfunction. The primary purpose of this manuscript is to create a Computer-Assisted Diagnostic (CAD) system to determine whether a COVID-19 patient has normal, mild, or severe anosmia. To achieve this goal, we used fluid-attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging (FLAIR-MRI) and Diffusion Tensor Imaging (DTI) to extract the appearance, morphological, and diffusivity markers from the olfactory nerve. The proposed system begins with the identification of the olfactory nerve, which is performed by a skilled expert or radiologist. It then proceeds to carry out the subsequent primary steps: (i) extract appearance markers (i.e., 1st and 2nd order markers), morphology/shape markers (i.e., spherical harmonics), and diffusivity markers (i.e., Fractional Anisotropy (FA) & Mean Diffusivity (MD)), (ii) apply markers fusion based on the integrated markers, and (iii) determine the decision and corresponding performance metrics based on the most-promising classifier. The current study is unusual in that it ensemble bags the learned and fine-tuned ML classifiers and diagnoses olfactory bulb (OB) anosmia using majority voting. In the 5-fold approach, it achieved an accuracy of 94.1%, a balanced accuracy (BAC) of 92.18%, precision of 91.6%, recall of 90.61%, specificity of 93.75%, F1 score of 89.82%, and Intersection over Union (IoU) of 82.62%. In the 10-fold approach, stacking continued to demonstrate impressive results with an accuracy of 94.43%, BAC of 93.0%, precision of 92.03%, recall of 91.39%, specificity of 94.61%, F1 score of 91.23%, and IoU of 84.56%. In the leave-one-subject-out (LOSO) approach, the model continues to exhibit notable outcomes, achieving an accuracy of 91.6%, BAC of 90.27%, precision of 88.55%, recall of 87.96%, specificity of 92.59%, F1 score of 87.94%, and IoU of 78.69%. These results indicate that stacking and majority voting are crucial components of the CAD system, contributing significantly to the overall performance improvements. The proposed technology can help doctors assess which patients need more intensive clinical care.
