The coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China, spread rapidly throughout China and gradually to some countries abroad. How is the development of an epidemic controlled? Early diagnosis is one of the important contents in prevention and control. COVID-19 patients with early mild pneumonia often lack typical evidence to make a definitive diagnosis. Based on the analysis of the cases of 4 patients, this article finds that early diagnosis requires a combination of epidemiology, clinical manifestations, imaging, and etiology, with particular emphasis on epidemiology history and chest computed tomography (CT) manifestations.

An outbreak of novel coronavirus(2019-nCoV) that began in Wuhan, China, is rapidly spreading to all over China, and gradually to multiple countries. Regarding to put the epidemic prevention and spread under the control, early identification and diagnosis play a critical role. Patients with initially no or mild symptoms of Novel Coronavirus Pneumonia (2019-nCoV), namely “stealth” infection, often lack of typical clinical evidence to establish the diagnosis. Based on the clinical analysis of 4 cases in stealth infection, the results of this study highlight that early diagnosis requires a combination of epidemiological history, clinical manifestations, early chest CT examination, and 2019-nCoV RNA test (nucleic acid test), with particular emphasis on definite epidemiological history and early chest CT findings when positive nucleic acid tests lag behind.

Objective: To evaluate the management mode for the prevention and control of coronavirus 2019 (COVID-19) transmission utilized at a general hospital in Shenzhen, China, with the aim to maintain the normal operation of the hospital. Methods: From January 2, 2020 to April 23, 2020, Hong Kong–Shenzhen Hospital, a tertiary hospital in Shenzhen, has operated a special response protocol named comprehensive pandemic prevention and control model, which mainly includes six aspects: 1) human resource management; 2) equipment management; 3) logistics management; 4) cleaning, disinfection and process reengineering; 5) environment layout; 6) and training and assessment. The detail of every aspect was described and its efficiency was evaluated. Results: A total of 198,802 patients were received. Of those, 10,821 were hospitalized; 26,767 were received by the emergency department and fever clinics; 288 patients were admitted for observation with fever; and 324 were admitted as suspected cases for isolation. Under the protocol of comprehensive pandemic prevention and control model, no case of hospital-acquired infection with COVID-19 occurred among the inpatients or staff. Conclusion: The present comprehensive response model may be useful in large public health emergencies to ensure appropriate management and protect the health and life of individuals.

Background/Objectives: Timely and accurate medical imaging in pre-hospital emergency care is crucial for improving the effectiveness of emergency medical rescue. Methods: In this study, a portable endoscopic system was developed for image acquisition and intelligent analysis. The core innovation lies in a dedicated image processing framework that integrates an improved U-Net neural network for real-time image dehazing and an enhanced Scale-Invariant Feature Transform (SIFT) algorithm for precise image stitching. Results: Experimental results demonstrated the exceptional performance of our method: the dehazing U-Net achieved a Structural Similarity Index (SSIM) of 0.98, a Peak Signal-to-Noise Ratio (PSNR) of 31.05, and a processing speed exceeding 70 frames per second (fps), while the enhanced SIFT algorithm effectively minimized stitching artifacts and vessel misalignment, yielding an SSIM of 0.9367 and a PSNR of 35.9768. Conclusions: This system significantly enhanced image quality and processing speed, enabling the acquisition of precise imaging information to support rapid diagnosis at the emergency scene. The findings establish a solid foundation for advancing pre-hospital emergency medical imaging and suggest promising avenues for future validation with diverse datasets and long-term clinical evaluations to further improve algorithmic robustness and applicability.