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Pneumothorax is a common injury in disaster rescue, traffic accidents, and war trauma environments and requires early diagnosis and treatment. The commonly used X-ray, CT, and other diagnostic instruments are not suitable for rescue sites due to their large size, heavy weight, and difficulty in transportation. Ultrasound equipment is easy to carry and suitable for rescue environments. However, ultrasound images are noisy, have low resolution, and are difficult to get started, which affects the efficiency of diagnosis. This paper studies the effect of lung ultrasound image recognition and classification based on compressed sensing and BP neural network. We use ultrasound equipment to build a lung simulation model, collect five typical features of lung ultrasound images in M-mode, and build a dataset. Using compressed sensing theory, we design sparse matrix and observation matrix and perform data compression on the image data in the dataset to obtain observation values. We design a BP neural network, input the observations into the network for training, and compare it with the commonly used VGG16 network. The method proposed in this paper has higher recognition accuracy and significantly fewer parameters than VGG16, so it is suitable for use in embedded devices. [ABSTRACT FROM AUTHOR]

World's science and technologies have been challenged by the COVID‐19 pandemic. Each and every community across the globe are trying to find a real‐time novel method for accurate treatment and cure of COVID‐19 infected patients. The most important lead to take from this pandemic is to detect the infected patients as soon as possible and provide them an accurate treatment. At present, the worldwide methodology to detect COVID‐19 is reverse transcription‐polymerase chain reaction (RT‐PCR). This technique is costly and time taking. For this reason, the implementation of a novel method is required. This paper includes the use of deep learning analysis to develop a system for identifying COVID‐19 patients. Proposed technique is based on convolution neural network (CNN) and deep neural network (DNN). This paper proposes two models, first is designing DNN on the basis of fractal feature of the images and second is designing CNN using lungs x‐ray images. To find the infected area (tissues) of the lungs image using CNN architecture, segmentation process has been used. Developed CNN architecture gave results of classification with accuracy equal to 94.6% and sensitivity equal to 90.5% which is much better than the proposed DNN method, which gave accuracy 84.11% and sensitivity 84.7%. The outcome of the presented model shows 94.6% accuracy in detecting infected regions. Using this method the growth of the infected regions can be monitored and controlled. The designed model can also be used in post‐COVID‐19 analysis. [ABSTRACT FROM AUTHOR]

Background: With the use of targeted drugs in lung cancer patients, targeted drug-induced interstitial lung disease (ILD) has attracted more and more attention. The incidence, time, and severity of different targeted drug-induced ILD vary. Almonertinib/HS-10296 is a third-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI). Post-marketing safety and effectiveness of almonertinib have been confirmed. The reported adverse events of almonertinib were mainly an increase in creatine phosphokinase, aspartate aminotransferase, and alanine aminotransferase, and onset of rash. Almonertinib-induced ILD is rare. Case report: This paper reported the case of a patient with lung adenocarcinoma complicated with interstitial lung abnormality (ILA). Gene detection showed L858R mutation in exon 21 of the EGFR gene. After operation, almonertinib (110 mg per day) was prescribed. 3 months later, ILD was found by chest CT due to dyspnea. Management and outcome: Subsequently, almonertinib was stopped. With the administration of intravenous glucocorticoid and oxygen inhalation, the patient's dyspnea was significantly regressed and lung lesions regressed on follow-up chest CT done after discharge. Discussion: This case suggested that we should pay attention to the existence of ILD/ILA before using targeted drugs. The use of targeted drugs should be more strictly controlled and monitored in patients with previous ILA or ILD. This paper also reviewed the relevant literature on the drug characteristics and summarized the risk factors of ILD caused by EGFR-TKI. [ABSTRACT FROM AUTHOR]

Lung cancer manifests itself as lung nodules at an early stage. Segmentation of lung nodules and quantitative evaluation of spiculation can assist physicians in distinguishing benign and malignant lung nodules. The identification of malignant nodules for early diagnosis and treatment can improve the survival rate of patients. In this paper, a quantitative evaluation method of lung nodule spiculation is proposed based on image enhancement. The proposed method combines super‐resolution reconstruction‐based image enhancement techniques with lung nodule segmentation algorithms to improve the accuracy of segmentation and to quantify the degree of distinctness of the spiculation of lung nodule, which can provide a reliable basis for computer‐aided diagnosis and treatment of lung nodules. First, the method uses Laplacian pyramid image restoration technique and Gaussian differential scale‐invariant feature to enhance the details and edge information of lung CT images. Then the improved Random Walk algorithm is used to segment the enhanced lung images and extract lung nodules. Finally, the spiculation index, which measures the spiculation of breast nodules, is used to quantify the spiculation of nodules. The experimental results show that the method can effectively segment lung nodules and quantitatively evaluate the spiculation of lung nodules. [ABSTRACT FROM AUTHOR]

Transbronchial lung biopsy is an effective and less-invasive treatment for the early diagnosis of lung cancer. However, the limited dexterity of existing endoscopic instruments and the complexity of bronchial access prevent the application of such procedures mainly for biopsy and diagnosis. This paper proposes a flexible robot for transbronchial lung biopsy with a cable-driven mechanism-based flexible manipulator. The robotic system of transbronchial lung biopsy is presented in detail, including the snake-bone end effector, the flexible catheters and the actuation unit. The kinematic analysis of the snake-bone end effector is conducted for the master-slave control. The experimental results show that the end effector reaches the target nodule through a narrow and tortuous pathway in a bronchial model. In conclusion, the proposed robotic system contributes to the field of advanced endoscopic surgery with high flexibility and controllability. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma is one of the most common cancers in the world, and accurate diagnosis of lung nodules is an important factor in reducing its mortality. In the diagnosis of pulmonary nodules, artificial intelligence (AI) assisted diagnosis technology has been rapidly developed, so testing its effectiveness is conducive to promoting its important role in clinical practice. This paper introduces the background of early lung adenocarcinoma and lung nodule AI medical imaging, and then makes academic research on early lung adenocarcinoma and AI medical imaging, and finally summarizes the biological information. In the experimental part, the relationship analysis of 4 driver genes in group X and group Y showed that there were more abnormal invasive lung adenocarcinoma genes, and the maximum uptake value and uptake function of metabolic value were also higher. However, there was no significant correlation between mutations in the four driver genes and metabolic values, and the average accuracy of AI-based medical images was 3.88% higher than that of traditional images. [ABSTRACT FROM AUTHOR]

Bidirectional generative adversarial networks (BiGANs) and cycle generative adversarial networks (CycleGANs) are two emerging machine learning models that, up to now, have been used as generative models, i.e., to generate output data sampled from a target probability distribution. However, these models are also equipped with encoding modules, which, after weakly supervised training, could be, in principle, exploited for the extraction of hidden features from the input data. At the present time, how these extracted features could be effectively exploited for classification tasks is still an unexplored field. Hence, motivated by this consideration, in this paper, we develop and numerically test the performance of a novel inference engine that relies on the exploitation of BiGAN and CycleGAN-learned hidden features for the detection of COVID-19 disease from other lung diseases in computer tomography (CT) scans. In this respect, the main contributions of the paper are twofold. First, we develop a kernel density estimation (KDE)-based inference method, which, in the training phase, leverages the hidden features extracted by BiGANs and CycleGANs for estimating the (a priori unknown) probability density function (PDF) of the CT scans of COVID-19 patients and, then, in the inference phase, uses it as a target COVID-PDF for the detection of COVID diseases. As a second major contribution, we numerically evaluate and compare the classification accuracies of the implemented BiGAN and CycleGAN models against the ones of some state-of-the-art methods, which rely on the unsupervised training of convolutional autoencoders (CAEs) for attaining feature extraction. The performance comparisons are carried out by considering a spectrum of different training loss functions and distance metrics. The obtained classification accuracies of the proposed CycleGAN-based (resp., BiGAN-based) models outperform the corresponding ones of the considered benchmark CAE-based models of about 16% (resp., 14%). [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19), with a start line in China, has spread hastily amongst human beings dwelling in other international locations, and is coming near approximately 34,986,502 instances worldwide in line with the facts of Edith Cowan University (ecu) Centre for disorder prevention and control. There are a restrained number of COVID-19 test kits to be had in hospitals due to the growing cases day by day. Consequently, it is important to implement an automated detection machine as a brief opportunity diagnosis choice to prevent COVID-19 spreading among human beings. Fusion was considered as a concatenation between the two-person vectors on this context. Speckle-affected and coffee-fine X-ray images along with top first-class pictures have been utilized in our test for carrying out exams. If training and trying out are done with best selected right fine X-ray photos in a super situation, the output accuracy can be observed higher. However, this doesn't constitute a real-existence situation, wherein the photo database would be a mixture of each appropriate- and poor-first-rate pictures. Therefore, this technique of the use of different excellent snap shots could test how nicely the machine can react to such real-lifestyles situations. A modified anisotropic diffusion filtering technique become hired to take away multiplicative speckle noise from the test photographs. The software of these techniques ought to successfully conquer the restrictions in enter photograph quality. Subsequent, the function extraction changed into finished on the test photographs. Ultimately, the Convolutional Neural Network (CNN) classifier accomplished a type of X-ray photographs to pick out whether or not it changed into COVID-19 or until now. Pneumonia, an interstitial lung sickness, is the main reason of loss of life in children under the age of five. It accounted for approximately 16% of the deaths of kids below the age of 5, killing around 880,000 kids in 2016 according to a look at conducted with the aid of United Nations International Children's Emergency Fund (UNICEF). Affected children were mostly much less than two years old. Well timed detection of pneumonia in youngsters can assist to the technique of restoration. This paper gives convolutional neural community fashions to accurately hit upon pneumonic lungs from chest X-rays, which can be utilized inside the actual global by using medical practitioners to treat pneumonia. Experimentation was conducted on Chest X-Ray images dataset to be had on Kaggle. [ABSTRACT FROM AUTHOR]

COVID-19 is a contagious respiratory illness that can be passed from person to person. Because it affects the lungs, damages blood arteries, and causes cardiac problems, COVID-19 must be diagnosed quickly. The reverse transcriptase polymerase chain reaction (RT-PCR) is a method for detecting COVID-19, but it is time consuming and labor expensive, as well as putting the person collecting the sample in danger. As a result, clinicians prefer to use CT scan and Xray images. COVID-19 classification can be done manually, however AI makes the process go faster. AI approaches include image processing, machine learning, and deep learning. An AI model is required to diagnose COVID-19, and a dataset is necessary to train that model. A dataset consists of the information from which the model is trained. This paper consists of the review of different image processing, machine learning and deep learning papers proposed by different researchers. As well as models based on deep learning and pretrained model using gradient boosting algorithm The goal of this paper is to provide information for future researchers to work with. [ABSTRACT FROM AUTHOR]

Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important step in improving patients' quality of life. In general, image methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on. X-ray images, in particular, are utilized in this study to diagnose brain tumors. This paper describes the investigation of the convolutional neural network (CNN) to identify brain tumors from X-ray images. It expedites and increases the reliability of the treatment. Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy. Python and Google Colab were utilized to perform this investigation. Deep feature extraction was accomplished with the help of pretrained deep CNN models, VGG19, InceptionV3, and MobileNetV2. The classification accuracy is used to assess the performance of this paper. MobileNetV2 had the accuracy of 92%, InceptionV3 had the accuracy of 91%, and VGG19 had the accuracy of 88%. MobileNetV2 has offered the highest level of accuracy among these networks. These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments. [ABSTRACT FROM AUTHOR]

Deep learning has become ubiquitous in the field of computer vision for tasks such as image classification and segmentation. A Computer-Aided Diagnostic (CAD) system for lung cancer detection and diagnosis works by identifying lung nodules and characterizing the same. Transfer learning allows for pre-trained weights to be ported from one model to another. Replacement of pre-trained encoders in encoder-decoder networks opens up the number of possibilities of such networks and motivates us to check the possibility of each combination for a segmentation task of interest. This paper reports the experiments carried out using such combinations and presents the various observations as a result of the experiments for the nodule segmentation task on the LIDC-IDRI dataset. This work also examines the effect of network parameters on some of the deep learning semantic segmentation architectures in the context of the lung cancer dataset, LIDC-IDRI. The efficient network architecture, based on observations, is determined to be UNet with the backbone architecture, Efficientnet-b3 trained on the ImageNet dataset. This specific network presents an IoU score of 0.59 on the training dataset and 0.45 on the validation dataset. The architectures were compared and analyzed in terms of the time and space taken as well. [ABSTRACT FROM AUTHOR]

Background: With the rise of targeted treatments for asthma, treatment with omalizumab is a new option. Objectives: To assess the improvement of pulmonary function with additional omalizumab treatment in patients (⩾6 years old) with moderate-to-severe allergic asthma. Data sources and methods: Observational studies of randomized controlled trials of add-on omalizumab for the treatment of patients with moderate-to-severe allergic asthma, published from the establishment till August 2022, were retrieved from WAN FANG DATA, PubMed, CNKI, Embase, Cochrane, and Web of Science databases. Data extraction and quality evaluation were performed on the literature that met the inclusion criteria, using RevMan 5.3 to analyze the data. Results: A total of 11 randomized controlled clinical trials were included, involving a total of 3578 patients with asthma, 1856 patients in the omalizumab group, and 1722 patients in the control group. The improvement in Forced expiratory volume in 1 s as a percentage of predicted normal and Forced expiratory volume in 1 s was more pronounced in the omalizumab-treated group [MD = 3.91, 95% confidence interval (CI): 1.89–5.94, p = 0.0002; MD = 0.09, 95% CI: 0.05–0.13, p < 0.0001], while the improvement in Morning Peak expiratory flow rate was not statistically different between the two groups (MD = 3.64, 95% CI: −22.17–29.45, p = 0.78). Conclusion: Additional omalizumab treatment showed some improvement in lung function in patients with moderate-to-severe asthma. Trial registration: PROSPERO ID:CRD42022378498. Plain language summary: Improvement of lung function in asthmatic patients with additional omalizumab In this paper, by screening clinical trials related to the treatment of patients with moderate and severe asthma with omalizumab plus, we extracted indicators related to lung function for meta-analysis and found that in patients with moderate to severe asthma, the addition of omalizumab can improve lung function to a certain extent and delay the worsening of lung function. [ABSTRACT FROM AUTHOR]

Patients with COVID-19 have been reported to experience neurological complications, although the main cause of death in these patients was determined to be lung damage. Notably, SARS-CoV-2-induced pathological injuries in brains with a viral presence were also found in all fatal animal cases. Thus, an appropriate animal model that mimics severe infections in the lungs and brain needs to be developed. In this paper, we compared SARS-CoV-2 infection dynamics and pathological injuries between C57BL/6Smoc-Ace2em3(hACE2-flag-Wpre-pA)Smoc transgenic hACE2-C57 mice and Syrian hamsters. Importantly, the greatest viral distribution in mice occurred in the cerebral cortex neuron area, where pathological injuries and cell death were observed. In contrast, in hamsters, viral replication and distribution occurred mainly in the lungs but not in the cerebrum, although obvious ACE2 expression was validated in the cerebrum. Consistent with the spread of the virus, significant increases in IL-1β and IFN-γ were observed in the lungs of both animals. However, in hACE2-C57 mice, the cerebrum showed noticeable increases in IL-1β but only mild increases in IFN-γ. Notably, our findings revealed that both the cerebrum and the lungs were prominent infection sites in hACE2 mice infected with SARS-CoV-2 with obvious pathological damage. Furthermore, hamsters exhibited severe interstitial pneumonia from 3 dpi to 5 dpi, followed by gradual recovery. Conversely, all the hACE2-C57 mice experienced severe pathological injuries in the cerebrum and lungs, leading to mortality before 5 dpi. According to these results, transgenic hACE2-C57 mice may be valuable for studying SARS-CoV-2 pathogenesis and clearance in the cerebrum. Additionally, a hamster model could serve as a crucial resource for exploring the mechanisms of recovery from infection at different dosage levels. [ABSTRACT FROM AUTHOR]

This paper presents a novel semi-automatic method for lung segmentation in thoracic CT datasets. The fully three-dimensional algorithm is based on a level set representation of an active surface and integrates texture features to improve its robustness. The method's performance is enhanced by the graphics processing unit (GPU) acceleration. The segmentation process starts with a manual initialisation of 2D contours on a few representative slices of the analysed volume. Next, the starting regions for the active surface are generated according to the probability maps of texture features. The active surface is then evolved to give the final segmentation result. The recent implementation employs features based on grey-level co-occurrence matrices and Gabor filters. The algorithm was evaluated on real medical imaging data from the LCTCS 2017 challenge. The results were also compared with the outcomes of other segmentation methods. The proposed approach provided high segmentation accuracy while offering very competitive performance. [ABSTRACT FROM AUTHOR]

This paper provides a comparative study of the conceptualization of ANGER in Dagbani and Dangme, classified as Mabia and Kwa languages respectively. We employ the Conceptual Metaphor Theory as an analytical tool for explaining the empirical facts on the conceptualization of anger in these languages. We show that the mental concepts of the Dangme and the Dagbani people reflect in their conceptualizations of ANGER. We demonstrate that although the two languages deploy varied body organs (especially the heart and stomach), to express anger, Dagbani tends to focus more on the use of the heart, the lungs and the chest to express anger, whereas Dangme uses the heart, stomach and the body or self. The two languages share a common conceptual metaphor of anger as 'heat' with other schematic nodes, such as 'fire', 'fluid' and 'container'. We finally establish that although most of the expressions of anger in these two languages fit into cross-linguistic patterns, there are also salient cultural specifics that demonstrate how their physiological and experiential realities tend to play an important role in their conception of anger. Data for this study were collected from native speakers of these languages through spontaneous conversations as well as from literary works produced in the two languages under investigation. [ABSTRACT FROM AUTHOR]

In this paper, I will discuss recent studies using a cystic fibrosis pig model to better understand the origins of cystic fibrosis lung disease. Specifically, I will review our work investigating how loss of the cystic fibrosis transmembrane conductance regulator function (CFTR) impairs mucociliary transport in the cystic fibrosis airway. These studies reveal new insights into the early, underlying mechanisms of cystic fibrosis lung disease and could lead to novel therapeutic interventions. [ABSTRACT FROM AUTHOR]

Lung health in Africa is of great public health concern. To exchange knowledge, communicate the latest scientific advances in lung health, build the capacity of young researchers, and provide a platform for networking of lung health stakeholders in Africa, the Pan African Thoracic Society (PATS), in partnership with the Respiratory Society of Kenya organized and held the 3rd Biennial PATS congress, from June 7–10, 2023 in Mombasa, Kenya. There were a total of 30 conference sessions, which included six skills building workshops, two research dissemination workshops, six plenary sessions, and five each of pediatric, adult, and thoracic surgery symposia. A total of 60 abstracts were presented, including 27 oral abstracts and 33 posters. A wide range of issues for lung health in Africa emerged. These include the emerging role of the environment, especially climate change and global warming, on lung health, the threat posed by the use of tobacco and other nicotine products in Africa, the high burden of lung infections, insecurity of medical oxygen supply systems, and high burden of chronic respiratory disease. In this paper, we summarize the proceedings of this conference and draw lessons that can be used to advance lung health in the African continent. [ABSTRACT FROM AUTHOR]

Early evaluation and diagnosis can significantly reduce the life-threatening nature of lung diseases. Computer-aided diagnostic systems (CADs) can help radiologists make more precise diagnoses and reduce misinterpretations in lung disease diagnosis. Existing literature indicates that more research is needed to correctly classify lung diseases in the presence of multiple classes for different radiographic imaging datasets. As a result, this paper proposes RVCNet, a hybrid deep neural network framework for predicting lung diseases from an X-ray dataset of multiple classes. This framework is developed based on the ideas of three deep learning techniques: ResNet101V2, VGG19, and a basic CNN model. In the feature extraction phase of this new hybrid architecture, hyperparameter fine-tuning is used. Additional layers, such as batch normalization, dropout, and a few dense layers, are applied in the classification phase. The proposed method is applied to a dataset of COVID-19, non-COVID lung infections, viral pneumonia, and normal patients' X-ray images. The experiments take into account 2262 training and 252 testing images. Results show that with the Nadam optimizer, the proposed algorithm has an overall classification accuracy, AUC, precision, recall, and F1-score of 91.27%, 92.31%, 90.48%, 98.30%, and 94.23%, respectively. Finally, these results are compared with some recent deep-learning models. For this four-class dataset, the proposed RVCNet has a classification accuracy of 91.27%, which is better than ResNet101V2, VGG19, VGG19 over CNN, and other stand-alone models. Finally, the application of the GRAD-CAM approach clearly interprets the classification of images by the RVCNet framework. [ABSTRACT FROM AUTHOR]

This review article delves into the multifaceted relationship between climate change, air quality, and respiratory health, placing a special focus on the process of particle deposition in the lungs. We discuss the capability of climate change to intensify air pollution and alter particulate matter physicochemical properties such as size, dispersion, and chemical composition. These alterations play a significant role in influencing the deposition of particles in the lungs, leading to consequential respiratory health effects. The review paper provides a broad exploration of climate change's direct and indirect role in modifying particulate air pollution features and its interaction with other air pollutants, which may change the ability of particle deposition in the lungs. In conclusion, climate change may play an important role in regulating particle deposition in the lungs by changing physicochemistry of particulate air pollution, therefore, increasing the risk of respiratory disease development. Climate change influences particle deposition in the lungs by modifying the physicochemical properties of particulate air pollution, thereby escalating the risk of respiratory disease development. It is crucial for healthcare providers to educate patients about the relationship between climate change and respiratory health. People with conditions such as asthma, COPD, and allergies must understand how changes in weather, air pollution, and allergens can exacerbate their symptoms. Instruction on understanding air quality indices and pollen predictions, along with recommendations on adapting everyday activities and medication regimens in response, is essential. [ABSTRACT FROM AUTHOR]

Medical imaging treatment is one of the best-known computer science disciplines. It can be used to detect the presence of several diseases such as skin cancer and brain tumors, and since the arrival of the coronavirus (COVID-19), this technique has been used to alleviate the heavy burden placed on all health institutions and personnel, given the high rate of spread of this virus in the population. One of the problems encountered in diagnosing people suspected of having contracted COVID-19 is the difficulty of distinguishing symptoms due to this virus from those of other diseases such as influenza, as they are similar. This paper proposes a new approach to distinguishing between lung diseases and COVID-19 by analyzing chest x-ray images using a convolutional neural network (CNN) architecture. To achieve this, pre-processing was carried out on the dataset using histogram equalization, and then we trained two sub-datasets from the dataset using the Train et Test, the first to be used in the training phase and the second to be used in the model validation phase. Then a CNN architecture composed of several convolution layers and fully connected layers was deployed to train our model. Finally, we evaluated our model using two different metrics: the confusion matrix and the receiver operating characteristic. The simulation results recorded are satisfactory, with an accuracy rate of 96.27%. [ABSTRACT FROM AUTHOR]

(1) Background: There is no reliable way to assess antenatal fetal pulmonary hypoplasia; however, the biological parameters of the fetal lung can help in evaluating fetal lung development. This study aimed to establish the reference intervals for normal fetal lung biological parameters at 21–40 weeks among the Chinese population. (2) Methods: This was a cross-sectional study of Chinese groups, and included a total of 1388 normal single pregnant women at 21–40 weeks' gestation. We selected 2134 images of a standard four-chamber view (4CV). ImageJ software (Release 2.14.0) was used to measure the left and right lung areas using a manual tracing method; the elliptic function key was used to measure the fetal thoracic circumference (TC), thoracic area (TA), head circumference (HC), heart area (HA), and abdominal circumference (AC). Based on the above measurements, the following parameters were calculated: lung area to head circumference ratio (LHR), total lung area (TLA), TLA/Weight (mm2/g), cardiothoracic ratio (CTR), lung–thoracic area ratio (TLA/TA), lung–heart area ratio (TLA/HA), TC/AC, and TC/HC. (3) Results: The left and right lung areas and LHRs positively correlated with gestational age (R2 = 0.85, 0.88, 0.66, 0.71, p < 0.001). From 21–40 weeks, the left and right lung areas and TLA increased by about 3.33 times, 3.16 times, and 3.22 times, respectively. The means of left and right LHRs increased by about 1.94 times and 1.84 times, respectively. TLA/Weight (mm2/g) was weakly correlated with gestational age, while CTR, TLA/TA, TLA/HA, TC/AC, and TC/HC had no significant correlation with gestational age. There was no statistically significant difference in fetal lung parameters between different genders of newborns, p > 0.05. (4) Conclusions: Our study establishes the reference intervals for normal Chinese fetal lung biological parameters at 21–40 weeks. Moreover, the reference intervals apply to fetuses of different genders. This paper can provide a reference for the prenatal non-invasive assessment of fetal pulmonary hypoplasia. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Deep Neural Networks is presented, laying the groundwork for the exploration of their use in hyperpolarized gas MRI and the quantification of lung ventilation defects. Five distinct studies are examined, each leveraging unique deep learning architectures and data augmentation techniques to optimize model performance. These studies encompass a range of approaches, including the use of 3D Convolutional Neural Networks, cascaded U-Net models, Generative Adversarial Networks, and nnU-net for hyperpolarized gas MRI segmentation. The findings highlight the potential of deep learning methods in the segmentation and analysis of lung MRI scans, emphasizing the need for consensus on lung ventilation segmentation methods. [ABSTRACT FROM AUTHOR]

Introduction: The paper presents a hybrid generative/discriminative classification method aimed at identifying abnormalities, such as cancer, in lung X-ray images.Methods: The proposed method involves a generative model that performs generative embedding in Probabilistic Component Analysis (PrCA). The primary goal of PrCA is to model co-existing information within a probabilistic framework, with the intent to locate the feature vector space for X-ray data based on a defined kernel structure. A kernel-based classifier, grounded in information-theoretic principles, was employed in this study.Results: The performance of the proposed method is evaluated against nearest neighbour (NN) classifiers and support vector machine (SVM) classifiers, which use a diagonal covariance matrix and incorporate normal linear and non-linear kernels, respectively.Discussion: The method is found to achieve superior accuracy, offering a viable solution to the class of problems presented. Accuracy rates achieved by the kernels in the NN and SVM models were 95.02% and 92.45%, respectively, suggesting the method's competitiveness with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Lung tumor is the first malignant tumor with the highest mortality, but only no more than one-third of patients can be treated by surgical resection. Microwave ablation (MWA) has become a new adjuvant therapeutic mean for lung tumors because of its low trauma, short treatment time, large ablation volume and wide application range. However, the treatment parameters of MWA, such as input power and ablation time, still depend on the doctors' experience, which leads to the ineffectiveness of MWA. Therefore, the accurate modeling of temperature distribution of lung tumor MWA has become a significant technical problem to be solved. Recent research was devoted to personalized characterization of lung tumor parameters, finite element analysis of temperature distribution in MWA and accurate ablation effect evaluation. In this paper, a review of the recently obtained results and data will be presented and discussed. [ABSTRACT FROM AUTHOR]

Background: EBV pneumonia with multiple cavitary pulmonary lesions is clinically rare. We analyzed the clinical characteristics, diagnosis and treatment, with an aim to enhance clinicians' awareness of this disease and reduce misdiagnosis and missed diagnoses.Case Presentation: This paper presents A 59-year-old male patient's initial symptoms included fever, cough, sputum, and asthma. The pulmonary CT imaging demonstrated multiple patchy and ground glass consolidation in both lungs, partial cavitary formation, and subpleural mesh lattice changes in the lower lobe of both lungs. Further analysis, including bronchoalveolar lavage fluid (BALF) metagenomic next-generation sequencing (mNGS) and CT-guided percutaneous lung biopsy mNGS, supported the diagnosis of EBV pneumonia. The patient was treated with acyclovir antiviral therapy for a week, resulting in symptom relief. Follow-up lung CT scans indicated interval reduction of inflammatory lesions, cavities, and interstitial changes. As the patient's condition improved significantly and he was discharged from hospital. The treatment was continued with oral acyclovir for 12 days. Subsequent outpatient follow-up CT performed 12 days after discharge revealed further improvement of the inflammatory lesions, as well as a reduction in cavitary and interstitial changes.Conclusion: The clinical imaging findings in the presented case were highly distinctive and have not been documented in either domestic or international literature. This uniqueness could have potentially contributed to misdiagnosis or overlooked diagnosis. mNGS of percutaneous lung puncture tissue is valuable for the diagnosis of infection with rare lung pathogens. [ABSTRACT FROM AUTHOR]

This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate organ segmentation is a fundamental step in disease-assisting diagnostic systems, and the precise segmentation of lung is crucial for subsequent lesion detection. Prior to this, lung segmentation algorithms had typically segmented the entire lung tissue. However, the trachea is also essential for diagnosing lung diseases. Challenges in lung parenchyma segmentation include the limited robustness of U-Net in acquiring contextual information and the small size of the trachea being mixed up with lung, making it difficult to identify and reconstruct the lungs. To overcome these difficulties, this paper proposes three improvements to U-Net: multiple concatenation modules to enhance the network's ability to capture context, multi-scale residual learning modules to improve the model's multi-scale learning capabilities, and an enhanced gated attention mechanism to enhance the fusion of various hierarchical features. The experimental results demonstrate that our model has achieved a significant improvement in trachea segmentation compared to existing models. [ABSTRACT FROM AUTHOR]

Lung Adenocarcinoma (LUAD) is a kind of Lung Cancer (LCA) with high incidence rate, which is very harmful to human body. It is hidden in the human body and is not easy to be discovered, so it brings great inconvenience to the treatment of LUAD. Artificial Intelligence (AI) technology provides technical support for the diagnosis and treatment of LUAD and has great application space in intelligent medicine. In this paper, 164 patients with primary LUAD who underwent surgery in Hospital A from January 2020 to December 2021 were selected as the study subjects, and the correlation between the imaging characteristics of LUAD and Epidermal Growth Factor Receptor (EGFR) gene mutation was analyzed. Finally, the conclusion was drawn. In terms of the study on the correlation between EGFR mutation of LUAD and the imaging characteristics of Computed Tomography (CT), it was concluded that there were significant differences between the patient's sex, smoking history, pulmonary nodule morphology and the EGFR gene, and there was no significant difference between the patient's tumor size and EGFR gene; in the study of the relationship between EGFR gene mutation and CT signs of LUAD lesions, it was found that there were significant differences between the symptoms of cavity sign, hair prick sign and chest depression sign and EGFR gene, but there was no significant difference between the symptoms of lobulation sign and EGFR gene; in the study of pathological subtype and EGFR gene mutation status of LUAD patients, it was concluded that the pathological subtype was mainly micropapillary. The mutation rate was 44.44%, which was the highest; in terms of CT manifestations of adjacent structures of lung cancer and the study of EGFR gene mutation status, it was found that there was a statistical difference between the tumor with vascular convergence sign and EGFR gene mutation, and pleural effusion, pericardial effusion, pleural thickening and other signs in tumor imaging were not significantly associated with EGFR gene mutation; in terms of the study of CT manifestations of adjacent structures of LCA and EGFR gene mutation status, it was concluded that pleural effusion, pericardial effusion, pleural thickening and other signs in tumor images were not significantly associated with EGFR gene mutation; in terms of analysis and cure of LUAD, it was concluded that the cure rate of patients was relatively high, and only a few people died of ineffective treatment. This paper provided a reference for the field of intelligent medicine and physical health. [ABSTRACT FROM AUTHOR]

Background: Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non‐small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning. Purpose: In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images. Methods: The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R‐CNN) and scoring fine‐tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor‐wise R‐CNN, a mask‐Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R‐CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask‐Net is used to segment tumor within a volume‐of‐interest (VOI) with a score head evaluating the segmentation performed by the mask‐Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R‐CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross‐validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center‐of‐mass distance; (2) Bland–Altman analysis and volumetric Pearson correlation analysis. Results: In fivefold cross‐validation, this method achieved Dice and MSD of 0.84 ± 0.15 and 1.38 ± 2.2 mm, respectively. A new PET/CT can be segmented in 1 s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods. Conclusion: The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Medical Residency Review is the property of Journal of Medical Residency Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world. Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease. No doubt, X-ray is considered as a quick screening method, but due to variations in features of images which are of X-rays category with Corona confirmed cases, the domain expert is needed. To address this issue, we proposed to utilize deep learning approaches. In this study, the dataset of COVID-19, lung opacity, viral pneumonia, and lastly healthy patients’ images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently. The performance of the Swin transformer is compared with the other seven deep learning models, including ResNet50, DenseNet121, InceptionV3, EfficientNetB2, VGG19, ViT, CaIT, Swim transformer provides 98% recall and 96% accuracy on corona affected images of the X-ray category. The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis, and proposed technique is found better in terms of accuracy. Our system could support clinicians in screening patients for COVID-19, thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients. Also, this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients. [ABSTRACT FROM AUTHOR]

In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies to help in providing diagnostic of respiratory pathologies in digital recorded respiratory sounds. Also, we will find out the best deep learning model for this task. In this paper, three different deep learning models have been evaluated on non-augmented and augmented datasets, where two different datasets have been utilized to generate four different sub-datasets. The results show that all the proposed deep learning methods were successful and achieved high performance in classifying the raw lung sounds, the methods were applied on different datasets and used either augmentation or non-augmentation. Among all proposed deep learning models, the CNN–LSTM model was the best model in all datasets for both augmentation and non-augmentation cases. The accuracy of CNN–LSTM model using non-augmentation was 99.6%, 99.8%, 82.4%, and 99.4% for datasets 1, 2, 3, and 4, respectively, and using augmentation was 100%, 99.8%, 98.0%, and 99.5% for datasets 1, 2, 3, and 4, respectively. While the augmentation process successfully helps the deep learning models in enhancing their performance on the testing datasets with a notable value. Moreover, the hybrid model that combines both CNN and LSTM techniques performed better than models that are based only on one of these techniques, this mainly refers to the use of CNN for automatic deep features extraction from lung sound while LSTM is used for classification. [ABSTRACT FROM AUTHOR]

Background: In recent years, immunotherapy has gradually become the first or second-line drug for non-small cell lung cancer. However, the side effects associated with immunotherapy should not be underestimated. Toxic reactions are commonly seen in the skin, endocrine, and liver, and rarely in the heart and nerves. These effects are often life-threatening when they occur. In this paper, we present a case of ICIs-associated myocarditis in advanced lung adenocarcinoma with unappreciated initial cardiac enzyme elevation in a driver gene negative. Methods: After electronic bronchoscopy and pathological examination, the patient was diagnosed with driver gene-negative advanced lung adenocarcinoma and treated with ICIs. Results: Driver gene-negative advanced lung adenocarcinoma, effectively treated with ICIs, initially had elevated cardiac enzymes and unilateral ptosis, but was not taken seriously and the patient eventually died after discharge from the hospital. Conclusions: For patients with driver gene-negative advanced lung adenocarcinoma treated with ICIs, regular and periodic monitoring of myocardial damage markers is a top priority, followed by timely initiation of hormonal therapy as a means to improve prognosis. [ABSTRACT FROM AUTHOR]

The role of Bronchoalveolar Lavage (BAL) in the evaluation of systemic sclerosis (SSc) interstitial lung disease (ILD) is still controversial. The aim of this systematic literature review was to investigate the use of BAL in SSc-ILD, and to focus on the pros and cons of its real-life application. Methods: PubMed, Cochrane, and Embase were questioned from inception until 31 December 2021. Results: Eighteen papers were finally analyzed. A positive correlation was observed between lung function and BAL cytology; in particular, BAL neutrophilia/granulocytosis was related to lower diffusing capacity for carbon monoxide (DLCO) values and lower forced vital capacity (FVC). Moreover, a positive correlation between BAL cellularity and high-resolution computed tomography (HRCT) findings has been reported by several authors. Cytokines, chemokines, growth factors, coagulation factors, and eicosanoids have all been shown to be present, more often and in higher quantities in SSc-ILD patients than in the health control and, in some cases, they were related to more severe pulmonary disease. There was no consensus regarding the role of BAL cellularity as a predictor of mortality. [ABSTRACT FROM AUTHOR]

Deep neural networks have been tremendously successful at segmenting objects in images. However, it has been shown they still have limitations on challenging problems such as the segmentation of medical images. The main reason behind this lower success resides in the reduced size of the object in the image. In this paper we overcome this limitation through a cyclic collaborative framework, CyCoSeg. The proposed framework is based on a deep active shape model (D-ASM), which provides prior information about the shape of the object, and a semantic segmentation network (SSN). These two models collaborate to reach the desired segmentation by influencing each other: SSN helps D-ASM identify relevant keypoints in the image through an Expectation Maximization formulation, while D-ASM provides a segmentation proposal that guides the SSN. This cycle is repeated until both models converge. Extensive experimental evaluation shows CyCoSeg boosts the performance of the baseline models, including several popular SSNs, while avoiding major architectural modifications. The effectiveness of our method is demonstrated on the left ventricle segmentation on two benchmark datasets, where our approach achieves one of the most competitive results in segmentation accuracy. Furthermore, its generalization is demonstrated for lungs and kidneys segmentation in CT scans. [ABSTRACT FROM AUTHOR]

To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. The ATM'22 dataset includes a large group of COVID'19 patients and a range of other lung diseases, however, relatively few patients with severe pathologies affecting the airway tree anatomy was found. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available. [ABSTRACT FROM AUTHOR]

This paper presents a multi-source preventive maintenance (MPM) based precision sensitivity optimization method for parallel robotic systems. Taking a parallel mechanism as an example, the input error can be divided into finite mutually independent uncertain error sources, and the probabilistic error model is established by the Monte Carlo stochastic method. Considering that several times of maintenance can lead to an increase of the accuracy degradation factor, a generalized hierarchy maintenance yield model is established, where the cost of preventive maintenance and failure replacement maintenance are distinguished between the independent error source and the overall error source. The nonlinear relationship between the maintenance yield per unit cost and influencing factors, including the error threshold and the maintenance times is obtained, which allows to develop an optimal maintenance strategy. Preventive maintenance is introduced to avoid unintended failure of the mechanism while extending the lifetime. The high-precision parallel mechanism has demonstrated promising applications in medical, industrial and other fields, and its economic benefits can be effectively improved by incorporating the MPM method. The experiment of manufacturing human lung models using a parallel 3D printing device demonstrates that the MPM method can improve the long-term precision and reliability of the parallel device, and the optimized human lung contour tracking precision can be improved by up to 55.56%. [ABSTRACT FROM AUTHOR]

The generalisability of deep neural network classifiers is emerging as one of the most important challenges of our time. The recent COVID-19 pandemic led to a surge of deep learning publications that proposed novel models for the detection of COVID-19 from chest x-rays (CXRs). However, despite the many outstanding metrics reported, such models have failed to achieve widespread adoption into clinical settings. The significant risk of real-world generalisation failure has repeatedly been cited as one of the most critical concerns, and is a concern that extends into general medical image modelling. In this study, we propose a new dataset protocol and, using this, perform a thorough cross-dataset evaluation of deep neural networks when trained on a small COVID-19 dataset, comparable to those used extensively in recent literature. This allows us to quantify the degree to which these models can generalise when trained on challenging, limited medical datasets. We also introduce a novel occlusion evaluation to quantify model reliance on shortcut features. Our results indicate that models initialised with ImageNet weights then fine-tuned on small COVID-19 datasets, a standard approach in the literature, facilitate the learning of shortcut features, resulting in unreliable, poorly generalising models. In contrast, pre-training on related CXR imagery can stabilise cross-dataset performance. The CXR pre-trained models demonstrated a significantly smaller generalisation drop and reduced feature dependence outwith the lung region, as indicated by our occlusion test. This paper demonstrates the challenging problem of model generalisation, and the need for further research on developing techniques that will produce reliable, generalisable models when learning with limited datasets. [ABSTRACT FROM AUTHOR]

In addition to considering the main effects, understanding gene‐environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high‐dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity‐constrained accelerated failure time (BHAFT) models for identifying main and interaction (M‐I) effects with novel spike‐and‐slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high‐dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa‐bayesian/BHAFT. [ABSTRACT FROM AUTHOR]

Patients with compromised respiratory function frequently require mechanical ventilation to survive. Unfortunately, non-uniform ventilation of injured lungs generates complex mechanical forces that lead to ventilator induced lung injury (VILI). Although investigators have developed lung-on-a-chip systems to simulate normal respiration, modeling the complex mechanics of VILI as well as the subsequent recovery phase is a challenge. Here we present a novel humanized in vitro ventilator-on-a-chip (VOC) model of the lung microenvironment that simulates the different types of injurious forces generated in the lung during mechanical ventilation. We used transepithelial/endothelial electrical impedance measurements to investigate how individual and simultaneous application of mechanical forces alters real-time changes in barrier integrity during and after injury. We find that compressive stress (i.e. barotrauma) does not significantly alter barrier integrity while over-distention (20% cyclic radial strain, volutrauma) results in decreased barrier integrity that quickly recovers upon removal of mechanical stress. Conversely, surface tension forces generated during airway reopening (atelectrauma), result in a rapid loss of barrier integrity with a delayed recovery relative to volutrauma. Simultaneous application of cyclic stretching (volutrauma) and airway reopening (atelectrauma), indicates that the surface tension forces associated with reopening fluid-occluded lung regions are the primary driver of barrier disruption. Thus, our novel VOC system can monitor the effects of different types of injurious forces on barrier disruption and recovery in real-time and can be used to interogate the biomechanical mechanisms of VILI. [ABSTRACT FROM AUTHOR]

Objective. To enable the registration network to be trained only once, achieving fast regularization hyperparameter selection during the inference phase, and to improve registration accuracy and deformation field regularity. Approach. Hyperparameter tuning is an essential process for deep learning deformable image registration (DLDIR). Most DLDIR methods usually perform a large number of independent experiments to select the appropriate regularization hyperparameters, which are time-consuming and resource-consuming. To address this issue, we propose a novel dynamic hyperparameter block, which comprises a distributed mapping network, dynamic convolution, attention feature extraction layer, and instance normalization layer. The dynamic hyperparameter block encodes the input feature vectors and regularization hyperparameters into learnable feature variables and dynamic convolution parameters which changes the feature statistics of the high-dimensional features layer feature variables, respectively. In addition, the proposed method replaced the single-level structure residual blocks in LapIRN with a hierarchical multi-level architecture for the dynamic hyperparameter block in order to improve registration performance. Main results. On the OASIS dataset, the proposed method reduced the percentage of | J ϕ | ⩽ 0 by 28.01 % , 9.78 % and improved Dice similarity coefficient by 1.17 % , 1.17 % , compared with LapIRN and CIR, respectively. On the DIR-Lab dataset, the proposed method reduced the percentage of | J ϕ | ⩽ 0 by 10.00 % , 5.70 % and reduced target registration error by 10.84 % , 10.05 % , compared with LapIRN and CIR, respectively. Significance. The proposed method can fast achieve the corresponding registration deformation field for arbitrary hyperparameter value during the inference phase. Extensive experiments demonstrate that the proposed method reduces training time compared to DLDIR with fixed regularization hyperparameters while outperforming the state-of-the-art registration methods concerning registration accuracy and deformation smoothness on brain dataset OASIS and lung dataset DIR-Lab. [ABSTRACT FROM AUTHOR]

Background: Pleural empyema is significant cause of morbidity and mortality. Debate in the literature exists regarding the best initial and definitive therapy, with recent research demonstrating superior short‐term outcomes with initial surgical intervention. Despite this, the impact of surgical intervention on long‐term outcomes has been incompletely described. A systematic review was undertaken to assess the current evidence evaluating the long‐term impact of surgical intervention. Methods: A systematic review was undertaken according to PRISMA guidelines utilizing three databases. Articles included all papers where patients received surgical intervention for empyema with outcomes evaluated beyond 90 days. Two reviewers extracted and reviewed the articles. Grey literature was included. Results: Eleven studies and two abstracts were extracted. One study and two abstracts evaluated the quality of life outcomes, two studies evaluated dyspnoea outcomes, seven studies evaluated long‐term lung function and two studies evaluated mortality and re‐admissions. 60–65% of patients had no dyspnoea between 2 and 7 years follow‐up. In six of seven studies, normal lung function was achieved in patients with chronic fibrothorax with FEV1% and FVC% improvements between 14–30% and 13–50%, respectively. The results from such biased cohorts could not be extrapolated to conclude that surgical intervention results in better outcomes than ICC drainage. Risk of bias was severe for all 11 studies. Conclusion: Surgical intervention potentially improves post‐operative lung function, long‐term dyspnoea, and mortality. The impact this has on quality of life remains unknown. Future prospective trials with homogenous comparative groups are required to better define the role of surgery and its impact on long‐term outcomes. [ABSTRACT FROM AUTHOR]

A key study area throughout the medical sector for image processing and analysis is medical image segmentation. The diagnosis and treatment strategies of doctors may have a solid foundation owing to accurate and effective medical image segmentation. Conventional approaches in this field rely on manual feature extraction, which makes segmentation complex, costs doctors’ time and energy, and involves a subjective evaluation that is readily susceptible to diagnostic errors. Researchers have applied convolutional neural network-based deep learning techniques to the segmentation of medical images as a result of their impressive advancements and successes in the field of computer vision. The research described here uses the U-Net network’s outstanding feature learning capabilities and end-to-end processing mode for lung CT image segmentation via fully convolutional network (FCN) research. However, focusing on valuable, crucial information aspects in the U-Net network is challenging. This study employed multilevel attention mechanisms on the basis of U-Net networks to enhance the model’s accuracy in lung CT image segmentation. These mechanisms were inspired by attention mechanisms. By improving the segmentation accuracy and optimizing the segmentation effect, the new model embeds a self-attention module in front of each upsampling layer in the U-Net model. This module provides more detailed information by stitching the self-attention module of the original image and then suppresses irrelevant and redundant information by using the effect of feature extraction of the upsampling layer. Several additional comparative experiments were conducted on the 2019nCoVR dataset. The outcomes demonstrate the efficacy of the optimized model described in this paper and its application results in improved segmentation effects in lung CT images. Additionally, the new model has distinct advantages over existing approaches that are typical of medical image segmentation, which represent its higher level of lung CT image segmentation. [ABSTRACT FROM AUTHOR]