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Combined central and peripheral demyelination (CCPD) is not encountered frequently in the clinical practice, and it requires a high level of suspicion for diagnosis. We describe a case of a young man who was diagnosed with radiologically isolated syndrome (RIS) after presenting initially with symptoms suggestive of central nervous system (CNS) insult in the form of double vision, slurred speech, left-sided numbness, and unsteadiness. However, on the next day of admission, his neurological examination was remarkable for ataxia, areflexia, and ophthalmoplegia, the typical triad of Miller Fisher syndrome (MFS). After confirming both diagnoses, the final diagnosis of CCPD was made. The challenges one may face to diagnose and treat CCPD urge sharing of similar cases to open the door for further extensive and thorough investigations and to encourage further studies and analysis of available data to come up with consolidated management guidelines for rare disorders.

BACKGROUND: Recent occurrence of the 2019 coronavirus disease (COVID-19) outbreak, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has highlighted the need for fast, accurate, and simple strategies to identify cases on a large scale. OBJECTIVE: This study aims to develop and test an accurate detection and severity classification methodology that may help medical professionals and non-radiologists recognize the behavior and propagation mechanisms of the virus by viewing computed tomography (CT) images of the lungs with implicit materials. METHODS: In this study, the process of detecting the virus began with the deployment of a virtual material inside CT images of the lungs of 128 patients. Virtual material is a hypothetical material that can penetrate the healthy regions in the image by performing sequential numerical measurements to interpret images with high data accuracy. The proposed method also provides a segmented image of only the healthy parts of the lung. RESULTS: The resulting segmented images, which represent healthy parts of the lung, are classified into six levels of severity. These levels are classified according to physical symptoms. The results of the proposed methodology are compared with those of the radiologists’ reports. This comparison revealed that the gold-standard reports correlated with the results of the proposed methodology with a high accuracy rate of 93%. CONCLUSION: The study results indicate the possibility of relying on the proposed methodology for discovering the effects of the SARS-CoV-2 virus in the lungs through CT imaging analysis with limited dependency on radiologists.