The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate ai-corona on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates ai-corona outperforms all the alternative models. Lastly, our framework’s diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating ai-corona’s assistance.

Objectives To compare the performance of chest computed tomography (CT) scan versus reverse transcription polymerase chain reaction (RT-PCR) as the reference standard in the initial diagnostic assessment of coronavirus disease 2019 (COVID-19) patients. Design A systematic review and meta-analysis were performed as per the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A search of electronic information was conducted using the following databases: MEDLINE, EMBASE, EMCARE, CINAHL and the Cochrane Central Register of Controlled Trials. Setting Studies that compared the diagnostic performance within the same patient cohort of chest CT scan versus RT-PCR in COVID-19 suspected patients. Participants Thirteen non-randomised studies enrolling 4092 patients were identified. Main outcome measures Sensitivity, specificity and accuracy were primary outcome measures. Secondary outcomes included other test performance characteristics and discrepant findings between both investigations. Results Chest CT had a median sensitivity, specificity and accuracy of 0.91 (range 0.82–0.98), 0.775 (0.25–1.00) and 0.87 (0.68–0.99), respectively, with RT-PCR as the reference. Importantly, early small, China-based studies tended to favour chest CT versus later larger, non-China studies. Conclusions A relatively high false positive rate can be expected with chest CT. It is possible it may still be useful to provide circumstantial evidence, however, in some patients with a suspicious clinical presentation of COVID-19 and negative initial Severe Acute Respiratory Syndrome Coronavirus 2 RT-PCR tests, but more evidence is required in this context. In acute cardiorespiratory presentations, negative CT scan and RT-PCR tests is likely to be reassuring.

Purpose The origin of the deformity due to adolescent idiopathic scoliosis (AIS) is not known, but mechanical instability of the spine could be involved in its progression. Spine slenderness (the ratio of vertebral height to transversal size) could facilitate this instability, thus playing a role in scoliosis progression. The purpose of this work was to investigate slenderness and wedging of vertebrae and intervertebral discs in AIS patients, relative to their curve topology and to the morphology of control subjects. Methods A total of 321 AIS patients (272 girls, 14 ± 2 years old, median Risser sign 3, Cobb angle 35° ± 18°) and 83 controls were retrospectively included (56 girls, median Risser 2, 14 ± 3 years). Standing biplanar radiography and 3D reconstruction of the spine were performed. Geometrical features were computed: spinal length, vertebral and disc sizes, slenderness ratio, frontal and sagittal wedging angles. Measurement reproducibility was evaluated. Results AIS girls before 11 years of age had slightly longer spines than controls (p = 0.04, Mann-Whitney test). AIS vertebrae were significantly more slender than controls at almost all levels, almost independently of topology. Frontal wedging of apical vertebrae was higher in AIS, as expected, but also lower junctional discs showed higher wedging than controls. Conclusion AIS patients showed more slender spines than the asymptomatic population. Analysis of wedging suggests that lower junctional discs and apex vertebra could be locations of mechanical instability. Numerical simulation and longitudinal clinical follow-up of patients could clarify the impact of wedging, slenderness and growth on the biomechanics of scoliosis progression.

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