ABSTRACT
Introduction: Pulmonary fibrosis is a consequential complication of microbial infections, which has notably been observed in SARS-CoV-2 infections in recent times. Macrophage polarization, specifically the M2-type, is a significant mechanism that induces pulmonary fibrosis, and its role in the development of Post- COVID-19 Pulmonary Fibrosis is worth investigating. While pathological examination is the gold standard for studying pulmonary fibrosis, manual review is subject to limitations. In light of this, we have constructed a novel method that utilizes artificial intelligence techniques to analyze fibro-pathological images. This method involves image registration, cropping, fibrosis degree classification, cell counting and calibration, and it has been utilized to analyze microscopic images of COVID-19 lung tissue. Methods: Our approach combines the Transformer network with ResNet for fibrosis degree classification, leading to a significant improvement over the use of ResNet or Transformer individually. Furthermore, we employ semi-supervised learning which utilize both labeled and unlabeled data to enhance the ability of the classification network in analyzing complex samples. To facilitate cell counting, we applied the Trimap method to localize target cells. To further improve the accuracy of the counting results, we utilized an effective area calibration method that better reflects the positive density of target cells. Results: The image analysis method developed in this paper allows for standardization, precision, and staging of pulmonary fibrosis. Analysis of microscopic images of COVID-19 lung tissue revealed a significant number of macrophage aggregates, among which the number of M2-type macrophages was proportional to the degree of fibrosis. Discussion: The image analysis method provids a more standardized approach and more accurate data for correlation studies on the degree of pulmonary fibrosis. This advancement can assist in the treatment and prevention of pulmonary fibrosis. And M2-type macrophage polarization is a critical mechanism that affects pulmonary fibrosis, and its specific molecular mechanism warrants further exploration.
ABSTRACT
In recent development of machine learning (ML)-based medical image analysis that have contributed to the prediction, planning, and early diagnostic process. Different chronic hermitic diseases like blood cancer/leukemia, AIDs, malaria, anemia and even COVID-19, all these are diagnoses via analyzing leucocytes or white blood cells (WBCs). Leucocytes analysis is the process of detection, localization, counting, analyzing WBCs, and it perform an active role in clinical hematology to assist health specialists in early stage disease diagnosing process. An automatic leucocytes analysis provide valuable diagnostics facts to doctors, via they can automatically detect, blood cancer, brain tumor and significantly improve the hematological, pathological activities. Manual Detection, counting and classification of WBCs is very slow, challenging and boring task due to having complex overlapping and morphological uneven structure. In this chapter, we provide a concise analysis of available ML techniques, to use these techniques for leucocytes analysis in microscopic images. The main aim of this chapter is to identify high performer and suitable ML algorithms for WBCs analysis using blood microscopic smear images. In the proposed review study, the recent and most relevant research papers are collected from IEEE, Science Direct, springer, and web of science (WoS) with the following keywords: ‘leucocytes detection’ or ‘leucocytes classification’. This study gives an extensive review of MIA but the research focuses more on the ML-based leucocytes/WBCs analysis in smear images. These techniques include traditional machine learning (TML), deep learning (DL), convolutional neural network (CNN) models, hybrid learning, and attention learning-based techniques to analyze medical image modalities to detect and classify cells in smear images. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Increasing COVID-19 infections are reason of concern for all the inside workplaces where physical presence is necessary for collaborating. Classrooms are one of the suspected places, where usually students are closely placed to learn together as in times before the pandemic. To reduce the infection rate in classrooms, an air purifier was designed around a commercial filter which removes 99, 9% of particles with 3μm. A baseline optical study of air purification was carried out to ensure effectiveness of the purifier during operation in closed environment. With conclusive evidence of microscopic images, breathing tests and aerosol penetration test using oil, the filter effectiveness was recorded. Optical values for suspended particle counts are recorded for variations in air flow rates of the air purifier and the gradual change is helping to understand the filter performance. Already around 70% minimum effectiveness of one flattened tissue layer removed from the filter was recorded during the tests, where the functional filter is folded in zigzags and 25 times thicker than a single layer. Furthermore, microscopic images showed solids deposited on the filter fabric and fuzzy spots on the tissue could indicate possible dried aerosol spots. This could be the hint supporting the hypothesis that aerosols can be effectively filtered reducing the virus load thus also risk of super-spreading of potential infection risk to an acceptable level. Beyond this research, and with the same group, measurements were made finding out the degree of reduction in potential aerosols particles in a classroom with a continuously aerosol emitting person. On that basis from this and the other optical studies, it was concluded that the spread of COVID-19 virus can be mitigated through effective air purification systems in classrooms and students can continue learning smoothly during the ongoing pandemic. © 2021, Society for Imaging Science and Technology.