Daily Self-Health Management and the Role of Nurses of Diabetic Patients during the COVID-19 Pandemic: A Review (preprint)

Aims: The study aims to take the COVID-19 pandemic as an example to provide a scientific reference for diabetic patients’ self-health management and nurses’ work for these patients when facing future pandemics. Review Methods: It conducted a comprehensive literature search and analysed relevant texts. This article reviews the daily health management of diabetic patients in the context of the COVID-19 pandemic and the role that nurses should play in this process. Results: Diabetic patients face challenges in self-health management during the pandemic, including disruptions in healthcare access, increased mental problems, and unhealthy lifestyles. Nurses serve as the front-line interface between these patients and the healthcare system. Adopting telehealth and remote consultation has effectively bridged the gap created by social distancing measures. Conclusion: Daily self-health management can significantly improve glycemic control and reduce the risk of diabetes-related complications, which is vital during the pandemic when patients may be experiencing disruptions to their routine care. Moreover, empowering patients through educational initiatives led by nurses can bring better self-monitoring, medication adherence, and lifestyle modifications, all of which are crucial in mitigating the effects of diabetes on the body’s immune response, thus reducing the severity of COVID-19 if contracted.

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge (preprint)

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made towards enhancing airway modelling, current public-available datasets concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for prognosis, a strong airway-derived biomarker (Hazard ratio>1.5, p<0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Knowing the Psychological Risks of Anti-epidemic Rescue Teams for COVID-19 by Simplified Risk Probability Scale (preprint)

Introduction: The COVID-19 pandemic has posed significant challenges to anti-epidemic rescue teams composed ofnon-medical professionals, such as security guards and volunteers. Our study aims to address how the Simplified Risk Probability Scale (SRPS), such as the Self-Assessed Anxiety Scale (SAS) and the Self-Assessed Depression Scale (SDS), can be used to assess the psychological risks of anti-epidemic rescue teams when facing COVID-19 and how to implement appropriate interventions for high-risk groups.  Methods: The study employed a cross-sectional study and collected data through an online survey. Participants were recruited through convenience sampling and were provided with informed consent. The survey included a COVID-19 self-made survey scale, as well as the SAS and SDS questionnaires to assess depression and anxiety symptoms. The study followed ethical standards and included 566 respondents from Lanzhou, China.  Results: 43.8% of anti-epidemic rescue team members experienced depression symptoms, while 33.3% experienced anxiety. The severity of depression and anxiety symptoms was positively correlated with the duration of work and the frequency of contact with COVID-19 patients. Female participants and those with lower education levels were more likely to experience thesesymptoms.  Conclusions: Our research has found that the psychological risks of rescue teams when facing COVID-19 come from anxiety and depression, and compelling psychological intervention can significantly reduce these risks. Overall, this study provides important insights into the psychological impact of the COVID-19 pandemic on anti-epidemic rescue teams and underscores the need for ongoing support and resources to protect the mental health of these essential workers.

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation (preprint)

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.

FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation (preprint)

3D Convolutional Neural Networks (CNNs) have been widely adopted for airway segmentation. The performance of 3D CNNs is greatly influenced by the dataset while the public airway datasets are mainly clean CT scans with coarse annotation, thus difficult to be generalized to noisy CT scans (e.g. COVID-19 CT scans). In this work, we proposed a new dual-stream network to address the variability between the clean domain and noisy domain, which utilizes the clean CT scans and a small amount of labeled noisy CT scans for airway segmentation. We designed two different encoders to extract the transferable clean features and the unique noisy features separately, followed by two independent decoders. Further on, the transferable features are refined by the channel-wise feature recalibration and Signed Distance Map (SDM) regression. The feature recalibration module emphasizes critical features and the SDM pays more attention to the bronchi, which is beneficial to extracting the transferable topological features robust to the coarse labels. Extensive experimental results demonstrated the obvious improvement brought by our proposed method. Compared to other state-of-the-art transfer learning methods, our method accurately segmented more bronchi in the noisy CT scans.