UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Vascular and non-vascular UNet systems dominated with sUNet classes with attention. Most of the studies suffered from a low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings.

Five Strategies for Bias Estimation in Artificial Intelligence-based Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients using AP(ai)Bias 2.0: A Systematic Review

Coronavirus 2019 (COVID-19) has led to a global pandemic infecting 224 million people and has caused 4.6 million deaths. Nearly 80 Artificial Intelligence (AI) articles have been published on COVID-19 diagnosis. The first systematic review on the Deep Learning (DL)-based paradigm for COVID-19 diagnosis was recently published by Suri et al. [IEEE J Biomed Health Inform. 2021]. The above study used AtheroPoint’s “AP(ai)Bias 1.0”using 10 AI attributes in the DL framework. The proposed study uses “AP(ai)Bias 2.0”as part of the three quantitative paradigms for Risk-of-Bias quantification by using the best 40 dedicated Hybrid DL (HDL) studies and utilizing 39 AI attributes. In the first method, the radial-bias map (RBM) was computed for each AI study, followed by the computation of bias value. In the second method, the regional-bias area (RBA) was computed by the area difference between the best and the worst AI performing attributes. In the third method, ranking-bias score (RBS) was computed, where AI-based cumulative scores were computed for all the 40 studies. These studies were ranked, and the cutoff was determined, categorizing the HDL studies into three bins: low, moderate, and high. Using the Venn diagram, these three quantitative methods were benchmarked against the two qualitative non-randomized-based AI trial methods (ROBINS-I and PROBAST). Using the analytically derived moderate-high and low-moderate cutoff of 2.9 and 3.6, respectively, we observed 40%, 27.5%, 17.5%, 10%, and 20% of studies were low-biased for RBM, RBA, RBS, ROBINS-I, and PROBAST, respectively. We present an eight-point recommendation for AP(ai)Bias 2.0 minimization. IEEE

Non-medical Interventions to Reduce the Burden of Psychosocial and Economic Impacts of COVID-19 Pandemic: A Scope Review

Context: COVID-19, like the other pandemics, apart from its impacts on peoples’ health, has had diverse huge impacts on psychosocio-economic aspects of societies globally. Hence, applying appropriate interventions to reduce the indirect burden of this pandemic is as important as patients’ care. Objectives: In this study, we aimed to review the main interventions against the economic and psychosocial impacts of the COVID-19 pandemic. Method: This scope review was conducted to determine what measures have been taken by governments against different nonmedical (economic and psychosocial) consequences of the COVID-19 pandemic. The authors reviewed the relevant articles published from December 2019 to December 2020 through three databases of PubMed/MEDLINE, Scopus, and Google Scholar. The interventions in three areas of economic, social, or psychological were exerted, and in the review of the articles, the country and the target population were considered. Finally, the results were categorized and presented descriptively. Results: Regarding the negative consequences of the COVID-19 pandemic in psychosocial and economic aspects of societies, governments, especially in developed countries, have established measures to reduce the burdens of these consequences. Apart from interventions related to the general population, at-risk and vulnerable groups and also those with low socio-economic status are specific target populations for interventions. Conclusions: The future of the COVID-19 pandemic is uncertain and unpredictable. Governments and their decisions will play a vital role in determining the trend of the pandemic. Therefore, it is the responsibility of governments, especially in lower-middle-income countries (LMICs), to support vulnerable people and protect them against the devastating socio-economic and psychological effects of this pandemic using all their capacity.

Transverse myelitis in COVID-19 patients: Report of two cases (preprint)

Background: COVID-19 has been associated with several neurological complications. One of these complications is transverse myelitis. Several cases of acute transverse myelitis are reported in association with this disease among the world. As there is lack of knowledge about the association of COVID-19 and myelitis and the clinical features of this complication are still ambiguous, we report two patients with transverse myelitis following COVID-19 infection.  Patients: This study was performed in a referral center of COVID-19 in Iran(Shohada Tajrish hospital) and two patients with paraparesis and diagnosis of transverse myelitis were enrolled. Both patients had longitudinally extensive transverse myelitis that resulted in paraparesis. One of the patients had favorable outcome after treatment with plasma exchange but the other had no improvement following treatment.Conclusion: Transverse myelitis could be a complication of COVID-19 and infarction and inflammation could be suggested as probable mechanisms for this condition.