Does CT help in reducing RT-PCR false negative rate for COVID-19?

A Hybrid Deep Transfer Learning Model With Kernel Metric for COVID-19 Pneumonia Classification Using Chest CT Images

Coronavirus disease-2019 (COVID-19) as a new pneumonia which is extremely infectious, the classification of this coronavirus is essential to effectively control the development of the epidemic. Pathological changes in the chest computed tomography (CT) scans are often used as one of the diagnostic criteria of COVID-19. Meanwhile, deep learning-based transfer learning is currently an effective strategy for computer-aided diagnosis (CAD). To further improve the performance of deep transfer learning model used for COVID-19 classification with CT images, in this article, we propose a hybrid model combined with a semi-supervised domain adaption model and extreme learning machine (ELM) classifier, and the application of a novel multikernel correntropy induced loss function in transfer learning is also presented. The proposed model is evaluated on open-source datasets. The experimental results are compared to some baseline models to verify the effectiveness, while adopting accuracy, precision, recall, F1 score and area under curve (AUC) as the evaluation metrics. Experimental results show that the proposed method improves the performance of original model and is more suitable for CT images analysis.

Ultra-Low Dose CT Chest in Acute COVID-19 Pneumonia: A Pilot Study from India

The rapid increase in the number of CT acquisitions during the COVID-19 pandemic raised concerns about increased radiation exposure to patients and the resultant radiation-induced health risks. It prompted researchers to explore newer CT techniques like ultra-low dose CT (ULDCT), which could improve patient safety. Our aim was to study the utility of ultra-low dose CT (ULDCT) chest in the evaluation of acute COVID-19 pneumonia with standard-dose CT (SDCT) chest as a reference standard. This was a prospective study approved by the institutional review board. 60 RT-PCR positive COVID-19 patients with valid indication for CT chest underwent SDCT and ULDCT. ULDCT and SDCT were compared in terms of objective (noise and signal-to-noise ratio) and subjective (noise, sharpness, artifacts and diagnostic confidence) image quality, various imaging patterns of COVID-19, CT severity score and effective radiation dose. The sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy of ULDCT for detecting lung lesions were calculated by taking SDCT as a reference standard. The mean age of subjects was 47.2 ± 10.7 years, with 66.67% being men. 90% of ULDCT scans showed no/minimal noise and sharp images, while 93.33% had image quality of high diagnostic confidence. The major imaging findings detected by SDCT were GGOs (90%), consolidation (76.67%), septal thickening (60%), linear opacities (33.33%), crazy-paving pattern (33.33%), nodules (30%), pleural thickening (30%), lymphadenopathy (30%) and pleural effusion (23.33%). Sensitivity, specificity and diagnostic accuracy of ULDCT for detecting most of the imaging patterns were 100% (p < 0.001); except for GGOs (sensitivity: 92.59%, specificity: 100%, diagnostic accuracy: 93.33%), consolidation (sensitivity: 100%, specificity: 71.43%, diagnostic accuracy: 93.33%) and linear opacity (sensitivity: 90.00%, specificity: 100%, diagnostic accuracy: 96.67%). CT severity score (range: 15-25) showed 100% concordance on SDCT and ULDCT, while effective radiation dose was 4.93 ± 1.11 mSv and 0.26 ± 0.024 mSv, respectively. A dose reduction of 94.38 ± 1.7% was achieved with ULDCT. Compared to SDCT, ULDCT chest yielded images of reasonable and comparable diagnostic quality with the advantage of significantly reduced radiation dose; thus, it can be a good alternative to SDCT in the evaluation of COVID-19 pneumonia.

Development and external validation of a deep learning-based computed tomography classification system for COVID-19

BACKGROUND

We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR).

METHODS

We used 2,928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2,295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR.

RESULTS

In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76.

CONCLUSIONS

Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity.

An optimistic point in COVID-19 pandemic: a case report of large adult congenital cystic adenomatoid malformation

Background

Covid-19 pandemic is a major health calamity causing global crisis involving every aspect of the society. CT chest has become an essential diagnostic investigation and as a prognostic tool for assessment for COVID-19 bronchopneumonia. This case report is about an incidental unexpected finding in a young female, who underwent CT chest screening with suspicion of COVID-19 bronchopneumonia.

Case presentation

A 29-year-old female came with the complaints of sore throat, myalgia, and fever for the past 3 days. She was referred to our department for plain screening CT chest to rule out COVID 19 infection. She was an active sports person since childhood. CT chest revealed a large well-defined bullous cystic lesion of size 16 × 9.5 × 9.5 cm in the left lung lower lobe with partial sparing of its superior, anterior, and posterior basal segments. Imaging diagnosis of large bullous cystic lesion with emphysematous changes was made. No features of COVID 19 bronchopneumonia. Thoracoscopy-guided lobectomy was done, and tissue was sent to histopathological examination. Final diagnosis was large type 1 congenital cystic adenomatoid malformation with mucinous metaplasia. Our case is unique in the sense that large adult CCAM with mucinous metaplasia of the epithelium is a rare presentation. Further it was diagnosed as a part of COVID 19 screening.

Conclusion

CCAM presentation in adult is rare. Asymptomatic CCAM lesion of this size diagnosed during COVID 19 chest CT screening was rarely described.