In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Ex Input:
Purpose: COVID-19 has become global threaten. CT acts as an important method of 60 diagnosis. However, human-based interpretation of CT imaging is time consuming. 61 More than that, substantial inter-observer-variation cannot be ignored. We aim at 62 developing a diagnostic tool for artificial intelligence (AI)-based classification of CT 63 images for recognizing COVID-19 and other common infectious diseases of the lung. 64 Experimental Design: In this study, images were retrospectively collected and 65 prospectively analyzed using machine learning. CT scan images of the lung that show 66 or do not show COVID-19 were used to train and validate a classification framework 67 based on convolutional neural network. Five conditions including COVID-19 68 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary 69 tuberculosis, and normal lung were evaluated. Training and validation set of images 70 were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were 71 collected from Zhongshan Hospital Xiamen University and the fifth Hospital of 72 Wuhan. 73 Results: Accuracy, sensitivity, and specificity of the AI framework were reported. For 74 test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, 75 non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis 76 were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, 77 accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 78 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. 79 All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Ex Output:
Deep Learning-Based Recognizing COVID-19 Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT 2 Scan Images


Ex Input:
In this study, the performance of 10 serological assays for the diagnosis of Mycoplasma pneumoniae infection was evaluated. A total of 145 sera from 120 patients were tested. They were obtained from patients who were serologically positive for M. pneumoniae infection as well as from patients who were infected with microorganisms that may cause interstitial pneumonia. The following assays were utilized: SeroMP IgM and IgG, SeroMP recombinant IgM, IgA and IgG, Liaison M. pneumoniae IgM and IgG and M. pneumoniae IgM, IgA and IgG ELISA Medac. The SeroMP Recombinant and Liaison assays both showed low IgM specificity, and crossreactivity was mainly observed in groups of patients with acute cytomegalovirus and Epstein-Barr virus infections. For IgA, the Medac assay was less specific than the SeroMP Recombinant assay. Discrepancies between the four tests were observed in IgG analyses, and due to the lack of a gold standard, 22 results were removed prior to determining the sensitivity and specificity. Therefore, the overall performance of IgG assays may be overstated; nevertheless, the SeroMP assay demonstrated a lack of sensitivity. The seroprevalence of IgG appears to be very low, raising concerns regarding whether the serological techniques can detect IgG levels over time. Serology remains a biological tool of choice for diagnosing M. pneumoniae infection, but improvement and standardization of the assays are needed, particularly for the determination of IgG.

Ex Output:
Evaluation of 10 serological assays for diagnosing Mycoplasma pneumoniae infection ☆


Ex Input:
Background: We performed an assessment of an independent rapid flu clinic service (RFCS) unit, which was set up outside the emergency department (ED) during the 2009 H1N1 pandemic season. The unit was able to relieve the crowding of regular ambulatory and emergency services. Methods: Between August and December 2009, a total of 6,152 patients with influenza-like illness were enrolled in this observational retrospective study. Patients with positive influenza tests were interviewed to evaluate the efficiency of RFCS. Results: The mean length of stay (LOS) for the RFCS was 50 minutes, which was shorter than the LOS for ambulatory services (1 hour) and regular ED services (3.5 hours). Overall, 88% of patients were satisfied with the RFCS. Of 6,152 patients receiving flu tests, 1,235 (20%) had a positive result. Fever (odds ratio [OR], 4.28, 95% confidence interval [CI]: 3.11-5.89), fever combined with cough and sore throat (OR, 2.52; 95% CI: 2.18-2.92), fever combined with sore throat (OR, 2.42; 95% CI: 2.13-2.75), history of contacting confirmed flu patients within 7 days (OR, 2.40; 95% CI: 2.07-2.78), fever combined with cough (OR, 2.19; 95% CI: 1.92-2.47), sore throat (OR, 2.03, 95% CI: 1.79-2.30); and cough (OR, 1.91, 95% CI: 1.69-2.17) were significantly associated with positive influenza tests. Conclusion: Setting up the RFSC was beneficial to health care facilities during a pandemic flu season.

Ex Output:

Impacts of rapid flu clinic services at an emergency department during the pandemic flu season