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• First work to investigate solutions for collaborative medical AI model for COVID-19. • A novel federated semi-supervised learning technique is proposed to train a network. • Method fully utilizes available data from different sources, w/ or w/o annotations. • Multinational dataset of 1704 scans from 3 countries (China, Italy, and Japan). • Studies fully evaluate and understand different configurations of the method. [Display omitted] The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, chest Computed Tomography (CT) has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis with chest CT scan, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. A common way to alleviate this issue is to fine-tune the model locally with the target domains local data and annotations. Unfortunately, the availability and quality of local annotations usually varies due to heterogeneity in equipment and distribution of medical resources across the globe. This impact may be pronounced in the detection of COVID-19, since the relevant patterns vary in size, shape, and texture. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing. [ABSTRACT FROM AUTHOR]

Objective: To assess the quality of the doctor-patient relationship (DPR) in China and possible influencing factors during the COVID-19 period from the patient's perspective. Methods: An online survey was carried out nationwide from March 12, 2020 to March 30, 2020 in China via a convenience sampling strategy. Patients who met the inclusion criteria were invited to complete a questionnaire regarding the quality of DPR, including sociodemographic information, the Patient-Doctor Relationship Questionnaire (PDRQ-9), and influencing factors for DPR during the pandemic. Results: A total of 1903 patients were included. Our result showed that participants had a higher PDRQ-9 score during the COVID-19 pandemic (4.18 ± 0.51) than that before the COVID-19 pandemic (3.86 ± 0.67). Importance-performance analysis (IPA) revealed that doctor-patient communication, patient satisfaction, consultation time, doctor's attitude, and medical knowledge were specific aspects that needed to be prioritized to improve the DPR. Multiple linear regression analysis suggested that positive media reports, telemedicine, and national policies had a significantly positive effect on the DPR during the pandemic (P < 0.05). Conclusion: In general, the DPR had been improved during the COVID-19 pandemic. Our research found the key points that needed to be prioritized to improve the DPR during the pandemic, which may provide effective suggestions for building a harmonious DPR in the future. [ABSTRACT FROM AUTHOR]

Objectives: The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2. Methods: Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression. Results: Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms. Conclusions: COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure. Key Points: • Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1–5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms. [ABSTRACT FROM AUTHOR]

The article offers information on the lessons learned regarding the pain management during the Covid-19 pandemic in China. It mentions the closure of pain management departments, posing a challenge for patients with medical conditions causing severe chronic pain such as cancer, as well as for vulnerable populations who rely on our services such as the elderly and disabled amid the pandemic.