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.

Example 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.
Example 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

Example Input: Objectives: We aimed to develop a prospective prediction tool on CrimeaneCongo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. Methods: We used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. Results: We predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). Conclusions: Main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases. C. Ak, Clin Microbiol Infect 2020;26:123.e1e123.e7
Example Output: A prospective prediction tool for understanding CrimeaneCongo haemorrhagic fever dynamics in Turkey

Example Input: The aim of the present survey was to evaluate the prevalence of Canine rotavirus (CRV) infection in companion dogs in Ahvaz district, Iran. The studied dogs were selected between referred cases to Shahid Chamran University of Ahvaz. Fecal samples were collected from 98 diarrheic dogs between December 2011 and August 2013. The dogs were classified according to age, sex, breed, clinical signs, and season. They were divided into three age groups (<3 months, 3-6 months, and >6 months), five different breeds, and another two groups on the basis of clinical symptoms (hemorrhagic and non-hemorrhagic diarrhea) using Chi-square analysis, Fischer's exact test, and Z test. The prevalence of rotavirus infection was 16.33 % (16 out of 98) (95 % CI for proportion 9.1-23.6 %) by means of immunochromatography assay (ICA) (Catalog No. RG18-03). The infection had significantly more prevalence in dogs less than 3 months (48.15 %; 13 out of 27) compared with dogs between 3 and 6 months (6.06 %; 2 out of 33) and above 6 months (2.63 %; 1 out of 38) (p<0.05). Prevalence was higher in males (17.54 %; 10 out of 57) than females (14.63 %; 6 out of 41), in winter season (21.74 %; 5 out of 23), and breed of German shepherd (19.05 %; 4 of 21), but the difference was not significant between the prevalence of infection relative to gender, season, and breed (p>0.05). Nevertheless, infection was significantly higher in none-hemorrhagic diarrheic dogs (23.08 %; 15 of 65) (p<0.05). The obtained results provide useful information on the epidemiology of Canine rotavirus in this area.
Example Output:
Antigenic detection of Canine rotavirus group A in diarrheic dogs in Ahvaz district, Southwestern Iran