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The American Journal of Physiology: Lung Cellular & Molecular Physiology is seeking papers on the topic of "Opioids and Respiratory Depression" in response to the opioid health crisis. The call for papers aims to address the dangerous side effect of respiratory depression caused by opioid overdose. The journal encourages researchers and healthcare professionals to submit articles and reviews related to breathing regulation and the detrimental consequences of opioids. The document is a list of references cited in an article discussing the prediction of opioid-induced respiratory depression using continuous capnography and oximetry, including studies on the effects of opioids on breathing, sleep-disordered breathing, risk factors, and the neurobiology of respiratory control. [Extracted from the article]

Purpose: To study respiratory effects of exposure to soft paper dust exposure, a relationship that is rarely studied. Methods: Soft tissue paper mill workers at a Swedish paper mill were investigated using a questionnaire and lung function and atopy screening. Spirometry without bronchodilation was performed with a dry wedge spirometer, and forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) were obtained and expressed as percent predicted. Exposure to soft paper dust was assessed from historical stationary and personal measurements of total dust, in addition to historical information about the work, department, and production. The impact of high exposure to soft paper dust (> 5 mg/m3) vs. lower exposure ≤ 5 mg/m3, as well as cumulative exposure, was analyzed using multiple linear regression models. Multivariate models were adjusted for smoking, atopy, gender, and body mass index. Results: One hundred ninety-eight current workers (124 male and 74 female) were included. There were significant associations between both cumulative exposure and years of high exposure to soft paper dust and impaired lung function. Each year of high exposure to soft paper dust was associated with a 0.87% decrease in FEV1 [95% confidence interval (CI) − 1.39 to − 0.35] and decreased FVC (− 0.54%, 95% CI − 1.00 to − 0.08) compared to the lower exposed workers. Conclusions: The present study shows that occupational exposure to soft paper dust (years exceeding 5 mg/m3 total dust) is associated with lung function impairment and increased prevalence of obstructive lung function impairment. [ABSTRACT FROM AUTHOR]

Purpose: Some teenagers with adolescent idiopathic scoliosis (AIS) display compromised lung function. However, the evidence regarding the relations between pulmonary impairments and various spinal deformity parameters in these patients remains unclear, which affects clinical management. This systematic review and meta-analysis aimed to summarize the associations between various lung function parameters and radiographic features in teenagers with AIS. Methods: A search of PubMed, Embase, PEDro, SPORTDiscus, CINAHL, Cochrane Library, and PsycINFO (from inception to March 14, 2022) without language restriction. Original studies reporting the associations between lung function and spinal deformity in patients with AIS were selected. Independent reviewers extracted data and evaluated the methodological quality of the included studies according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Pearson correlation and 95% confidence intervals were calculated using random-effects meta-analysis. Results: Twenty-seven studies involving 3162 participants were included. Limited-quality evidence supported that several spinal parameters were significantly related to lung function parameters (e.g., absolute value and percent of the predicted forced vital capacity (FVC; %FVC), forced expiratory volume in one second (FEV1; %FEV1), and total lung capacity (TLC; %TLC)) in AIS patients. Specifically, meta-analyses showed that main thoracic Cobb angles in the coronal plane were significantly and negatively related to FVC (r = − 0.245), %FVC (r = − 0.302), FEV1 (r = − 0.232), %FEV1 (r = − 0.348), FEV1/FVC ratio (r = − 0.166), TLC (r = − 0.302), %TLC (r = − 0.183), and percent predicted vital capacity (r = − 0.272) (p < 0.001). Similarly, thoracic apical vertebral rotation was negatively associated with %FVC (r = − 0.215) and %TLC (r = − 0.126) (p < 0.05). Conversely, thoracic kyphosis angles were positively related to %FVC (r = 0.180) and %FEV1 (r = 0.193) (p < 0.05). Conclusion: Larger thoracic Cobb angles, greater apical vertebral rotation angle, or hypokyphosis were significantly associated with greater pulmonary impairments in patients with AIS, although the evidence was limited. From a clinical perspective, the results highlight the importance of minimizing the three-dimensional spinal deformity in preserving lung function in these patients. More research is warranted to confirm these results. [ABSTRACT FROM AUTHOR]

This document is a letter to the editor of the British Journal of Neurosurgery commenting on a previous article about intraventricular metastases from small cell carcinoma of the lung. The letter raises questions about the previous article's claims and presents a different case of intraventricular metastasis. It discusses possible mechanisms and challenges the assertion that small cell lung carcinoma usually metastasizes to basal cisterns and sulci rather than ventricles. The letter also mentions the use of functional MRI for differentiation and presents a new case of intraventricular metastasis. Overall, the document provides additional information and perspectives on the topic of intraventricular metastases from lung cancer. [Extracted from the article]

This document is a letter to the editor of the British Journal of Neurosurgery commenting on a case report about intraventricular metastases from lung adenocarcinoma. The authors of the letter raise a few questions about the case report, specifically regarding the claim that no cases of intraventricular metastasis from lung adenocarcinoma have been reported in the literature. They point out a previous report from 1996 that described a case of intraventricular metastasis from primary pulmonary adenocarcinoma. The authors also question the completeness of the literature search conducted by the authors of the case report. They suggest that functional MRI could be used to differentiate intraventricular metastases from other conditions. The letter is written by Lihua Liu from the Department of Geriatrics at The Affiliated Nanchong Central Hospital of North Sichuan Medical College in China and Xueyun Deng from the Department of Neurosurgery at the same hospital. [Extracted from the article]

Every year, hundreds of thousands of children worldwide are born with sickle cell disease, a genetic disorder that impacts the hemoglobin molecules in blood. If left undiagnosed and untreated, most affected children will die before reaching the age of five. However, highly accurate diagnostic methods and effective treatment regimens for sickle cell disease have been known for many years, and children who receive early diagnosis and subsequent comprehensive care survive well into adulthood-as evidenced by the tremendous success of universal newborn screening programs in North America and Europe. [ABSTRACT FROM PUBLISHER]

This article presents a paper that was delivered at the 25th annual meeting of the Health Physics Society. The first application of research data came from studies of the nephrotoxic action of soluble uranium compounds in animals. It became clear that the amount of soluble salts of natural uranium depositing and remaining in lung or bone would never constitute a sufficient radiological hazard to override the chemical toxicity to the kidney. For insoluble uranium it was usually assumed that radiation effects would control, even though there was no evidence of radiation effects in any of the animal work. By calculation from a data, which included predictions of uranium deposition in lung and pulmonary lymph nodes, an allowable air concentration was suggested for insoluble uranium. In the area of effects a large gap still persisted in that clear-cut radiation effects from insoluble natural uranium had yet to be demonstrated in animals or man. It took an inhalation experiment of heroic proportions, parts of it going as long as eleven years, to finally load up an animal with enough insoluble uranium and to wait long enough to see significant and uncontestable radiation effects in lung and accessory structures.

Membrane transport proteins are involved in the absorption, disposition, efficacy, and/or toxicity of many drugs. Numerous mechanisms (e.g., nuclear receptors, epigenetic gene regulation, microRNAs, alternative splicing, post‐translational modifications, and trafficking) regulate transport protein levels, localization, and function. Various factors associated with disease, medications, and dietary constituents, for example, may alter the regulation and activity of transport proteins in the intestine, liver, kidneys, brain, lungs, placenta, and other important sites, such as tumor tissue. This white paper reviews key mechanisms and regulatory factors that alter the function of clinically relevant transport proteins involved in drug disposition. Current considerations with in vitro and in vivo models that are used to investigate transporter regulation are discussed, including strengths, limitations, and the inherent challenges in predicting the impact of changes due to regulation of one transporter on compensatory pathways and overall drug disposition. In addition, translation and scaling of in vitro observations to in vivo outcomes are considered. The importance of incorporating altered transporter regulation in modeling and simulation approaches to predict the clinical impact on drug disposition is also discussed. Regulation of transporters is highly complex and, therefore, identification of knowledge gaps will aid in directing future research to expand our understanding of clinically relevant molecular mechanisms of transporter regulation. This information is critical to the development of tools and approaches to improve therapeutic outcomes by predicting more accurately the impact of regulation‐mediated changes in transporter function on drug disposition and response. [ABSTRACT FROM AUTHOR]

Autologous cancer vaccines (ACV) are an emerging option for adjuvant cancer treatment in veterinary medicine. I The aim of this online cross-sectional, questionnaire-based survey by i Amie Wilson and co-workers I in the UK was to characterise current antimicrobial prescribing practices by equine veterinarians and to describe surveillance, audit processes and identification of antimicrobial resistance (AMR) i . There was a significant increase in the amount of I-lines (10.8 ± 8.7 vs. 15.28 ± 8.19), B-lines (3.2 ± 3.5 vs. 8.72 ± 4.86) and coalescent B-lines (0.04 ± 0.2 vs. 1.12 ± 1.45) after anaesthesia compared with before anaesthesia, and a significantly higher LUS score 2 h after anaesthesia (4.92 ± 8.40) than that before anaesthesia (0.9 ± 1.8). [Extracted from the article]

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis. However, in PET/CT (Positron Emission Tomography/Computed Tomography) lung images, the lesion shapes are complex, the edges are blurred, and the sample numbers are unbalanced. To solve these problems, this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model (MCIF-Transformer Mask RCNN) for PET/CT lung tumor instance segmentation, The main innovative works of this paper are as follows: Firstly, the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images. The pixel dependence relationship is established in local and non-local fields to improve the model perception ability. Secondly, the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features, and the cross-scale interactive feature enhancement module (CIFEM) is used to enhance the attention ability of the fine-grained features. Thirdly, the Cross-scale Interactive Feature fusion FPN network (CIF-FPN) is constructed to realize bidirectional interactive fusion between deep features and shallow features, and the low-level features are enhanced in deep semantic features. Finally, 4 ablation experiments, 3 comparison experiments of detection, 3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets. The results showed that APdet, APseg, ARdet and ARseg indexes are improved by 5.5%, 5.15%, 3.11% and 6.79% compared with Mask RCNN (resnet50). Based on the above research, the precise detection and segmentation of the lesion region are realized in this paper. This method has positive significance for the detection of lung tumors. [ABSTRACT FROM AUTHOR]

Keywords: COPD; cystic fibrosis; hypertension; lung cancer; pulmonary embolism EN COPD cystic fibrosis hypertension lung cancer pulmonary embolism 492 495 4 01/27/21 20210201 NES 210201 This is a synopsis from a recently published Fleischner Society Position Paper entitled "Expanding Applications of Pulmonary MRI in the Clinical Evaluation of Lung Disorders."[1] Pulmonary MRI has had limited clinical utility for patients with lung disease, and as such, it is not performed with any regularity in the clinic. Puderbach, M. Eichinger, J. Haeselbarth, Assessment of morphological MRI for pulmonary changes in cystic fibrosis (CF) patients: comparison to thin-section CT and chest x-ray. COPD, cystic fibrosis, hypertension, lung cancer, pulmonary embolism. [Extracted from the article]

Ground-glass nodules (GGN) are the main manifestation of early lung cancer, and accurate and efficient identification of ground-glass pulmonary nodules is of great significance for the treatment of lung diseases. In response to the problem of traditional machine learning requiring manual feature extraction, and most deep learning models applied to 2D image classification, this paper proposes a Transformer-based recognition model for ground-glass nodules from the view of global 3D asymmetry feature representation. Firstly, a 3D convolutional neural network is used as the backbone to extract the features of the three-dimensional CT-image block of pulmonary nodules automatically; secondly, positional encoding information is added to the extracted feature map and input into the Transformer encoder layer for further extraction of global 3D asymmetry features, which can preserve more spatial information and obtain higher-order asymmetry feature representation; finally, the extracted asymmetry features are entered into a support vector machine or ELM-KNN model to further improve the recognition ability of the model. The experimental results show that the recognition accuracy of the proposed method reaches 95.89%, which is 4.79, 2.05, 4.11, and 2.74 percentage points higher than the common deep learning models of AlexNet, DenseNet121, GoogLeNet, and VGG19, respectively; compared with the latest models proposed in the field of pulmonary nodule classification, the accuracy has been improved by 2.05, 2.05, and 0.68 percentage points, respectively, which can effectively improve the recognition accuracy of ground-glass nodules. [ABSTRACT FROM AUTHOR]

Objective. The percutaneous puncture lung mass biopsy procedure, which relies on preoperative CT (Computed Tomography) images, is considered the gold standard for determining the benign or malignant nature of lung masses. However, the traditional lung puncture procedure has several issues, including long operation times, a high probability of complications, and high exposure to CT radiation for the patient, as it relies heavily on the surgeon's clinical experience. Approach. To address these problems, a multi-constrained objective optimization model based on clinical criteria for the percutaneous puncture lung mass biopsy procedure has been proposed. Additionally, based on fuzzy optimization, a multidimensional spatial Pareto front algorithm has been developed for optimal path selection. The algorithm finds optimal paths, which are displayed on 3D images, and provides reference points for clinicians' surgical path planning. Main results. To evaluate the algorithm's performance, 25 data sets collected from the Second People's Hospital of Zigong were used for prospective and retrospective experiments. The results demonstrate that 92% of the optimal paths generated by the algorithm meet the clinicians' surgical needs. Significance. The algorithm proposed in this paper is innovative in the selection of mass target point, the integration of constraints based on clinical standards, and the utilization of multi-objective optimization algorithm. Comparison experiments have validated the better performance of the proposed algorithm. From a clinical standpoint, the algorithm proposed in this paper has a higher clinical feasibility of the proposed pathway than related studies, which reduces the dependency of the physician's expertise and clinical experience on pathway planning during the percutaneous puncture lung mass biopsy procedure. [ABSTRACT FROM AUTHOR]

We developed a new interactive web-based teaching material to improve lung auscultation skills. Our objective was to investigate the effectiveness of the web-based teaching material on nurses with less than one-year work experience, using a prospective, open-label, stratified block randomized controlled trial. Of the 69 participants, 23, 22, and 24 participants were assigned to the web-based, paper-based, and control (with no intervention) groups, respectively. Using a simulator, a discrimination test on seven lung sounds, such as “normal,” “wheeze,” “rhonchi,” “coarse crackles,” “fine crackles,” “left lung diminish,” and “right lung absent,” was conducted. Next, a post-test was conducted after one-week of training. Answers with formal names were considered “correct”; those with common names, misspellings, and without left and right parts were considered “insufficient”; and wrong answers were considered “incorrect.” The control group showed no significant difference between the pre-test and post-test for any lung sounds. The paperbased group showed significant improvement in performance for “wheeze” (p=0.004) and “coarse crackles” (p=0.035). The web-based group showed a significant improvement in performance for “fine crackles” (p=0.026). The number of correct answers in the post-test was higher in the paper- and web-based groups than the control group (p=0.023). The web-based teaching materials that we had developed effectively improved the ability of new graduate nurses to auscultate lung sounds. Additionally, the results suggest that the combined use of web- and paper-based teaching materials may be more effective since the sounds that each method enhanced their ability to auscultate different lung sounds. [ABSTRACT FROM AUTHOR]

Background and Aims: Journal impact factor has historically been taken as a proxy for quality. However, this is open to significant manipulation and bias. There is currently not widely adopted, robust journal and paper ranking metric which is focused solely on risk of bias. Methods: Risk of bias data was extracted from all Cochrane database systematic reviews in Child Health, Lungs, and Airways for the years 2017–2019. A novel paper quality score, the Clinical Research Bias Index (CRBI), was applied. Individual paper data were pooled for each journal. A comparison was made to journal impact factors, individual paper citations, reads, and altmetric scores. Results: 927 papers were analyzed for risk of bias. 119 (12·8%) scored a CRBI of 100%, with a mean score of 70%. A journal's overall CRBI risk of bias score was poorly correlated with impact factor (r 0.25). Citations (r 0.02), and reads (r 0.01) of individual papers showed very little association with the paper's risk of bias. Likewise, reads were not correlated with citations (r 0.03). H‐index and Altmetric scores were similarly poorly correlated with CRBI. Conclusion: The novel research quality tool CRBI demonstrates the poor correlation between journal impact factor, citations, and risk of bias. Journal and paper ranking metrics should ensure that they are fit for purpose, and enable the dissemination of high‐quality research for the benefit of patients. We propose the CRBI as a potential solution which is resistant to manipulation and will reward the creation and publication of bias‐free research. [ABSTRACT FROM AUTHOR]

The analysis of vertical reverberation artefacts is an essential component of the differential diagnosis in pulmonary ultra-sound. Traditionally, they are often, but not exclusively, called B-line artefacts (BLA) and/or comet tail artefacts (CTA), but this view is misleading. In this position paper we clarify the terminology and relation of the two lung reverberation artefacts BLA and CTA to spe-cific clinical scenarios. BLA are defined by a normal pleura line and are a typical hallmark of cardiogenic pulmonary edema after exclusion of certain pathologies including pneumonia or lung contusion, whereas CTAs show an irregular pleura line representing a variety of parenchymal lung diseases. The dual approach using low frequency transducers to determine BLA and high frequency transducer to determine the pleural surface is recommended. [ABSTRACT FROM AUTHOR]

Lung is the most important gas exchange organ of human, and the smooth airway is the basis of lung function. The condition of the trachea is associated with a variety of diseases. In this paper several methods of tracheal simulation based on CT-based data since 2003 are reviewed. Reasonable algorithms and image processing methods are important development directions for airway scanning reconstruction. The development of airway reconstruction needs to be closely integrated with mathematical modelling to improve the accuracy and precision of reconstruction. [ABSTRACT FROM AUTHOR]

aims to perform a systematic assessment of the influence of physical exercise on asthma patients and discuss the intervention effects of different exercises on the lung function FEV1 (%pred) and quality of life among asthma patients so as to lay a scientific foundation for improving asthma symptoms. Methods: Both Chinese and English databases were retrieved, including PubMed, Web of Science, Embase, The Cochrane Library, CBM, CNKI, Wan Fang Data, and VIP, whose retrieval period started from the founding date of each database to 1st, November 2021. Randomized controlled trials (RCT) studying the symptom indicators of asthma patients were collected. Those collected papers were screened according to the Inclusion Criteria and Exclusion Criteria. Then, methodological quality assessments were conducted on the included papers, and combined effect sizes were analyzed by using software ReMan 5.3.5. Results: The meta analysis showed that physical exercise could significantly improve lung function FEV1 (%pred) and quality of life score. Trails containing breathing exercise are the main source of heterogeneity, and the subgroup of breathing exercise may have better performance than the subgroup of aerobic exercise in improving FEV1 (%pred). Conclusion: Physical exercise can significantly improve the symptoms and quality of life of asthma patients. Except the breathing exercise that showed heterogeneity, the subgroup of aerobic exercise could improve the capacity of FEV1 (%pred) more effectively, which led to a significant difference in the influence of quality of life. However, with regard to the gymnastic exercise including breathing exercise, there are limited same intervention methods and insufficient same outcome indicators. Therefore, more precise and high-quality researches are needed to make deeper verification in the future. [ABSTRACT FROM AUTHOR]

The article presents research summaries related to dermatopathology in Great Britain. In one of the studies, it aims to increase the awareness of prototheca wickerhamii which causes neutropenic sepsis after a chemotherapy. Another study reports a case of histiocyctic sarcoma having secondary skin involvement and a presumed primary in the lung. Furthermore, a case report on symplastic haemangioma which is developing on a background of chronic leg ulcer is given.

The Editor and Publisher of I DNA and Cell Biology i are committed to preserving the scientific literature and the community it serves. I DNA and Cell Biol i ogy officially retracts the paper entitled, "miR-1244/Myocyte Enhancer Factor 2D Regulatory Loop Contributes to the Growth of Lung Carcinoma" by Rui Zhang, Yong Zhang, and He Li (DNA Cell Biol 34(11):692-700; 10.1089/dna.2015.2915) due to image duplication in multiple published papers by numerous different authors, suggests the paper was submitted via a paper mill. [Extracted from the article]

COVID-19 is a highly contagious epidemic, and detection in the incipient phase is essential to curb the expansion of the disease. Chest Xrays are used in detecting COVID-19 infection. Lung's images and CT -Scan photos are available for coronavirus analysis. This paper is composed of deep learning techniques and methods used to detect COVID-19 contamination in the lung images. The methods employed and collected datasets used for testing metrics are summed up. The Analytical metrics utilized by the methods which are completely comparable. Through this work, we have taken a perspective on COVID-19 affected chest x-ray scanners and healthy patients. After sorting and pre-processing the images and implementing the data addition, we applied deep-learning-based CNN models to compare their performance with other models. The aim is to provide a helping hand to the most distressed medical professionals who are analyzing images with two eyes, detect COVID-19. According to this analysis we provide a proposed methodology that uses deep learning, dropout technique with python language on Google Colab platform for reduces over-fitting by this deep learning technology. During the testing phase, I got 98.1% accuracy by increasing convolution layer and dropout layer. Our proposed methodology gives better accuracy than other compared models. The primary goal of this paper is to present research on medical image processing and define and implement the proposed CNN model. [ABSTRACT FROM AUTHOR]

The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Gastroparesis is a gastric disease characterized by delayed gastric emptying. "Vomiting" is the main clinical manifestation of this disease, while "atrophy" indicates dysfunction of the stomach. Based on the theory of "atrophy, dyspnea and vomiting are ascribed to the upper part", originating from the Chapter of "Zhi Zhen Yao Da Lun" from the Suwen(Basic Questions) section this paper explored the pathogenesis and treatment idea of gastroparesis from the function of the lung. It is put forward that failure of lung qi in dispersion, so the lung qi cannot maintain the circulation of harmonizing qi and blood, leading to malnutrition of stomach and loss of stomach receiving food and drink function. Melancholy impairing lung, so lung cannot govern diffusion, leading to stomach governing the disfunction of dredging and descending. Impaired depurative descending of lung qi, so the large intestine and stomach are lost in the alternating operation of deficiency and excess, leading to stomach governing the disfunction of transportation and transformation. Therefore, the treatment approach is proposed to improve the stomach's function to receive food and drink, transport and transform and promote the recovery of gastric motility by replenishing and restoring lung qi, regulating emotions and catharsis and smoothing lung. Analyzing the effect of function of the lung on gastric motility is of great value for expanding the application of traditional Chinese medicine in gastrointestinal motility disorders, and digging new connotations of classic theories in modern clinical practice. [ABSTRACT FROM AUTHOR]

The in-depth combination and application of AI technology and medical imaging, especially high- definition CT imaging technology, make accurate diagnosis and treatment possible. Retrieving similar CT image(CI)s to an input one from the large-scale CI database of labeled diseases is helpful to realize a precise computer-aided diagnosis. In this paper, we take lung CI as an example and propose progressive content-based similarity retrieval(CBSR) method of the lung CIs based on a Weakly Supervised Similarity LearningNetwork (WSSLN) model. Two enabling techniques (i.e., the WSSLN model and the distance- based pruning scheme) are proposed to facilitate the CBSR processing of the large lung CIs. The main result of our paper is that, our approach is about 45% more effective than the state-of-the-art methods in terms of the mean average precision(mAP). Moreover, for the retrieval efficiency, the WSSLN-based CBSR method is about 150% more efficient than the sequential scan. [ABSTRACT FROM AUTHOR]

Lung disease is one of the most harmful diseases in traditional days and is the same nowadays. Early detection is one of the most crucial ways to prevent a human from developing these types of diseases. Many researchers are involved in finding various techniques for predicting the accuracy of the diseases. On the basis of the machine learning algorithm, it was not possible to predict the better accuracy when compared to the deep learning technique; this work has proposed enhanced artificial neural network approaches for the accuracy of lung diseases. Here, the discrete Fourier transform and the Burg auto-regression techniques are used for extracting the computed tomography (CT) scan images, and feature reduction takes place by using principle component analysis (PCA). This proposed work has used the 120 subjective datasets from public landmarks with and without lung diseases. The given dataset is trained by using an enhanced artificial neural network (ANN). The preprocessing techniques are handled by using a Gaussian filter; thus, our proposed approach provides enhanced classification accuracy. Finally, our proposed method is compared with the existing machine learning approach based on its accuracy. [ABSTRACT FROM AUTHOR]

Lung ultrasound has great application value in the differential diagnosis of pulmonary exudative lesions. It has good sensitivity and specificity for the diagnosis of various pulmonary diseases in neonates and children. It is believed that it can replace chest CT examination. It is routinely used for the diagnosis of pulmonary diseases in emergency critical care medicine. However, the interpretation of the impact of ultrasound on the human lungs relies heavily on experienced physicians, which greatly restricts the interpretation efficiency of the impact of ultrasound. In order to improve the efficiency of monitoring and interpretation of the impact of ultrasound, this paper proposes an intelligent detection algorithm for human lung clinical ultrasound images based on recurrent neural network. Transfer learning is used to replace the fully connected layer of the VGG16 model and improve the loss function, so that the same the Euclidean distance between category images can be reduced, and the Euclidean distance between different categories of images can be increased, enhancing the resolution of the entire model, thereby achieving better image feature extraction results. The experimental results show that the algorithm proposed in this paper can surpass the doctor's level in the identification of various lung diseases. [ABSTRACT FROM AUTHOR]

In this paper, the Scale-Invariant Feature Transform (SIFT) and Fast Library for Approximate Nearest Neighbors (FLANN) based algorithm is used to detect the abnormalities in the National Lung Screening Trial (NLST) CT scans as the exact clinical nodule locations are not provided in the dataset. These identified nodules on NLST CT Scans are then annotated using LabelImg tool. This process consumes time and so furthermore, the automatic nodule detection, You Only Look Once version 4 (YOLOv4) object detection model is implemented. The YOLOv4 object detection model is provided with total of 4187 labelled images in form of training (70%), validation (20%), and test (10%) datasets. Our YOLOv4 model achieves precision of 95%, sensitivity of 81% and mean Average Precision (mAP) of 89.1%. [ABSTRACT FROM AUTHOR]