Early Detection Of Lung Disease Research Articles

Regional Pulmonary Ventilation Assessment Method and System Based on Impedance Sensing Information from the Pentapulmonary Lobes

Regional lung ventilation assessment is a critical tool for the early detection of lung diseases and postoperative evaluation. Biosensor-based impedance measurements, known for their non-invasive nature, among other benefits, have garnered significant attention compared to traditional detection methods that utilize pressure sensors. However, solely utilizing overall thoracic impedance fails to accurately capture changes in regional lung air volume. This study introduces an assessment method for lung ventilation that utilizes impedance data from the five lobes, develops a nonlinear model correlating regional impedance with lung air volume, and formulates an approach to identify regional ventilation obstructions based on impedance variations in affected areas. The electrode configuration for the five lung lobes was established through numerical simulations, revealing a power–function nonlinear relationship between regional impedance and air volume changes. An analysis of 389 pulmonary function tests refined the equations for calculating pulmonary function parameters, taking into account individual differences. Validation tests on 30 cases indicated maximum relative errors of 0.82% for FVC and 0.98% for FEV1, all within the 95% confidence intervals. The index for assessing regional ventilation impairment was corroborated by CT scans in 50 critical care cases, with 10 validation trials showing agreement with CT lesion localization results.

Analyzing the performance of a bio-sensor integrated improved blended learning model for accurate pneumonia prediction

Pneumonia has been considered a life-threatening disease for elderly human beings and those with weakened immune systems in the present medical era. The contemporary scenario highlights the significance of intelligent and automatic handheld devices to detect pneumonia and other pulmonary diseases. Hence, this research has designed an improved blended learning paradigm (IBLP) for real-time pneumonia detection from chest X-rays, and early detection of lung diseases from alveolar gas using biosensors with a graphical processing unit (GPU) developed to overcome and resolve such challenges. It emphasizes the medical applications of blended learning techniques, particularly for identifying pneumonia from chest X-ray images and other pulmonary diseases from exhaled breath using biosensors and support vector machine (SVM). The experimental findings indicate that the blended learning based VGG16 (91.99%) consistently outperforms the VGG19 (88.91%) and ResNet50 (87.02%) model in diagnostic accuracy. IBLP VGG16 provided 95.5% precision, 97.69% F1 score, and a 100% recall rate with no false-negative results. The future of X-ray classification in pneumonia diagnosis will likely involve using artificial intelligence-based systems that can provide accurate and timely analysis of X-ray images, thereby improving patient outcomes and reducing healthcare costs.

Correlation Between Lung Clearance Index (LCI) and Forced Expiratory Volume (FEV1) in Children with Cystic Fibrosis (CF): A Cross-Sectional Study

Background: The Lung Clearance Index (LCI) serves as a non-uniform ventilation index utilized for monitoring pulmonary function in patients with cystic fibrosis (CF). Lung Clearance Index exhibits higher sensitivity compared to forced expiratory volume in 1 second (FEV1) for early detection of lung disease and does not necessitate active patient cooperation, as required for FEV1 measured through spirometry. Presently, FEV1 is the standard parameter employed for monitoring lung function in CF patients. Objectives: The objective of this study was to assess the correlation between LCI and FEV1 in patients with cystic fibrosis. Methods: This cross-sectional study enrolled children aged 6 to 18 years with confirmed CF diagnosis, who were referred to the CF clinic at Children's Medical Center Hospital (Tehran, Iran). Participants completed consent forms and subsequently underwent pulmonary function tests. Lung Clearance Index was calculated using the exhaling-D device via the Multiple Breath Washout (MBW) method, followed by FEV1 assessment through spirometry. Results: The study included 52 patients with an average age of 12 years, among whom 52% were males. The mean ± standard deviation of FEV1 and LCI were 80.2% ± 25.3 and 8.9 ± 2.8, respectively. A significant inverse relationship was observed between these two parameters in the study (r = -0.49, P = 0.001). Conclusions: These findings further underscore the potential utility of LCI, which offers ease of administration and demonstrates high reliability and accuracy compared to FEV1 for monitoring pulmonary function in CF patients.

The Effect of Ferritin Level on Respiratory Functions in Patients with β-Thalassemia Major

Introduction-Purpose β-thalassemia major (β-TM) is an autosomal recessive disorder caused by mutations in the β-globin gene of hemoglobin. The disease is characterized by splenomegaly due to ineffective erythropoiesis, iron accumulation signs in tissues as a result of increased iron absorption, bone expansion due to increased erythropoietic activity, and decreased tissue oxygenation. One of the effected organ can be the lungs due to excessive iron deposition in these patients. The current study aimed to investigate the effect of serum ferritin level, which is known as a marker of iron accumulation in tissues, on pulmonary function tests (PFT) in patients with β-TM. Methods and Materials Patients aged ≥6 years who were regularly followed in the pediatric hematology section of Mersin City Research and Training Hospital with a diagnosis of β-TM were included. All patients received regular blood transfusion and iron chelation therapy. Study participants underwent PFT prior to blood transfusion in the pediatric pulmonology section Results A total of 43 patients with β-TM were studied. Included patients were divided into two groups according to the mean annual ferritin level; low ferritin group if below 2000 ml/ng and high ferritin group if above 2000 ml/ng. The low ferritin group was consisted of 19 patients and the high ferritin group was consisted of 24 patients. The characteristics of these two groups are shown in Table 1. There were no statistical significance in age, gender, body mass index, age at diagnosis, mean annual hemoglobin, splenectomy, cardiac involvement and oxygen saturation among both groups, but the number of annual transfusion was significantly higher in the high ferritin group than lower ferritin group. When PFT parameters of both groups were compared, FVC (forced vital capacity) was statistically lower in the high ferritin group compared to the low ferritin group. Other parameters included FEV 1 (forced expiratory volume in 1 second), FEV 1/FVC ratio, PEF (peak expiratory flow) and FEF 25-75 (forced expiratory flow between 25% and 75% of vital capacity) were similar among groups. (Table 2) Discussion Patients with β-TM may accumulate iron in the interstitial area of the lungs which can lead fibrosis and impaired lung function over time. There are several studies investigated lung dysfunction and its etiology in these patients. Although the results of the studies are varied, the majority of them reported a restrictive pattern of respiratory dysfunction in thalassemia patients. Additionally, some studies showed the presence of mild or moderate obstruction in small airways and decrease in diffusion capacity with the increase of alveolar-capillary membrane thickness at advanced ages. In the present study, we found that patients with β-TM who had high ferritin level showed restrictive type lung function compared to those with low ferritin level. There were no difference among the groups in obstructive parameters (i.e. FEV 1, FEV 1/FVC, FEF 25-75) of PFT. In the literature, studies investigating PFT in patients with high ferritin levels had variable results, impaired or no change, in pulmonary status. In conclusion, loss of respiratory function and impaired tissue oxygenation in patients with β-TM may develop over time due to iron deposition in the interstitial area. PFT assessment of these patients is essential and recommended for the detection of early lung disease. Routine PFT follow in patients with β-TM of high ferritin values is highly important.

Early Detection of Lung Disease Using Deep Learning Algorithms on Image Data

Abstract: This research paper presents a deep learning-based algorithm for the early detection of lung diseases using medical image data. The algorithm demonstrates high accuracy, sensitivity, specificity, precision, and AUC-ROC values, outperforming existing methods. By leveraging deep learning techniques, the algorithm provides a valuable tool for accurate disease identification, enabling timely interventions and improving patient outcomes. The study discusses the algorithm's performance, generalizability, and clinical relevance, highlighting its potential impact on clinical practice. Future work includes integrating multi-modal data, exploring model explainability, conducting external validation, and continuous model improvement to enhance the algorithm's diagnostic capabilities and real-world applicability. Overall, the proposed algorithm shows promise in advancing the early detection of lung diseases, contributing to improved healthcare outcomes

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy.

DeepLungNet: An Effective DL-Based Approach for Lung Disease Classification Using CRIs

Infectious disease-related illness has always posed a concern on a global scale. Each year, pneumonia (viral and bacterial pneumonia), tuberculosis (TB), COVID-19, and lung opacity (LO) cause millions of deaths because they all affect the lungs. Early detection and diagnosis can help create chances for better care in all circumstances. Numerous tests, including molecular tests (RT-PCR), complete blood count (CBC) tests, Monteux tuberculin skin tests (TST), and ultrasounds, are used to detect and classify these diseases. However, these tests take a lot of time, have a 20% mistake rate, and are 80% sensitive. So, with the aid of a doctor, radiographic tests such as computed tomography (CT) and chest radiograph images (CRIs) are used to detect lung disorders. With CRIs or CT-scan images, there is a danger that the features of various lung diseases’ diagnoses will overlap. The automation of such a method is necessary to correctly classify diseases using CRIs. The key motivation behind the study was that there is no method for identifying and classifying these (LO, pneumonia, VP, BP, TB, COVID-19) lung diseases. In this paper, the DeepLungNet deep learning (DL) model is proposed, which comprises 20 learnable layers, i.e., 18 convolution (ConV) layers and 2 fully connected (FC) layers. The architecture uses the Leaky ReLU (LReLU) activation function, a fire module, a maximum pooling layer, shortcut connections, a batch normalization (BN) operation, and group convolution layers, making it a novel lung diseases classification framework. This is a useful DL-based method for classifying lung disorders, and we tested the effectiveness of the suggested framework on two datasets with a variety of images from different datasets. We have performed two experiments: a five-class classification (TB, pneumonia, COVID-19, LO, and normal) and a six-class classification (VP, BP, COVID-19, normal, TB, and LO). The suggested framework’s average accuracy for classifying lung diseases into TB, pneumonia, COVID-19, LO, and normal using CRIs was an impressive 97.47%. We have verified the performance of our framework on a different publicly accessible database of images from the agriculture sector in order to further assess its performance and validate its generalizability. This study offers an efficient and automated method for classifying lung diseases that aids in the early detection of lung disease. This strategy significantly improves patient survival, possible treatments, and limits the transmission of infectious illnesses throughout society.

A forced cough sound based pulmonary function assessment method by using machine learning.

Pulmonary function testing (PFT) has important clinical value for the early detection of lung diseases, assessment of the disease severity, causes identification of dyspnea, and monitoring of critical patients. However, traditional PFT can only be carried out in a hospital environment, and it is challenging to meet the needs for daily and frequent evaluation of chronic respiratory diseases. In this study, we propose a novel method for accurately assessing pulmonary function by analyzing recorded forced cough sounds by mobile device without time and location restrictions. In the experiment, 309 clips of cough sound segments were separated from 133 patients who underwent PFT by using Audacity software. There are 247 clips of training samples and 62 clips of testing samples. Totally 52 features were extracted from the dataset, and principal component analysis (PCA) was used for feature reduction. Combined with biological attributes, the normalized features were regressed by using machine learning models with pulmonary function parameters (i.e., FEV1, FVC, FEV1/FVC, FEV1%, and FVC%). And a 5-fold cross-validation was applied to evaluate the performance of the regression models. As described in the experimental result, the result of coefficient of determination (R2) indicates that the support vector regression (SVR) model performed best in assessing FVC (0.84), FEV1% (0.61), and FVC% (0.62) among these models. The gradient boosting regression (GBR) model performs best in evaluating FEV1 (0.86) and FEV1/FVC (0.54). The result confirmed that the proposed method was capable of accurately assessing pulmonary function with forced cough sound. Besides, the cough sound sampling by a smartphone made it possible to conduct sampling and assess pulmonary function frequently in the home environment.

Lung cancer diagnosis in CT images based on Alexnet optimized by modified Bowerbird optimization algorithm

ObjectiveCancer is the uncontrolled growth of abnormal cells that do not function as normal cells. Lung cancer is the leading cause of cancer death in the world, so early detection of lung disease will have a major impact on the likelihood of a definitive cure. Computed Tomography (CT) has been identified as one of the best imaging techniques. Various tools available for medical image processing include data collection in the form of images and algorithms for image analysis and system testing. MethodsThis study proposes a new diagnosis system for lung cancer based on image processing and artificial intelligence from CT-scan images. In the present study, after noise reduction based on wiener Filtering, Alexnet has been utilized for diagnosing healthy and cancerous cases. The system also uses optimum terms of different features, including Gabor wavelet transform, GLCM, and GLRM to be used in replacing with the network feature extraction part. The study also uses a new modified version of the Satin Bowerbird Optimization Algorithm for optimal designing of the Alexnet architecture and optimal selection of the features. ResultsSimulation results of the proposed method on the RIDER Lung CT collection database and the comparison results with some other state-of-the-art methods show that the proposed method provides a satisfying tool for lung cancer diagnosis. The comparison results show that the proposed method with 95.96% accuracy shows the highest value toward the others. The results also show that a higher harmonic mean value for the proposed method with higher F1-score of the method toward the others. Plus, the highest test recall results (98.06%) of the proposed method indicate its higher rate of relevant instances that are retrieved for the images. ConclusionTherefore, using the proposed method can provide an efficient tool for optimal diagnosis of the Lung Cancer from the CT Images. Significancethis shows that using the proposed method as a new deep-learning-based methodology, can provide higher accuracy and can resolve the big problem of optimal hyperparameters selection of the deep-learning-based methodology techniques for the aimed case.

Time series and fractal analyses of wheezing: a novel approach.

Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.

Predictive value of spirometry in early detection of lung disease in adults: a cohort study.

BackgroundSpirometry is essential to identify cases with obstructive lung diseases (OLDs) in primary care. However, knowledge about the long-term prognostic outcome among younger individuals is sparse.AimTo describe the predictive value of spirometry among individuals in the age groups 30–49 years and 45–64 years.Design & settingA population-based cohort study supplied with data from Danish national registries.MethodSpirometry was performed in 905 adults aged 30–49 years in 1991 and in 1277 adults aged 45–64 years in 2006. The participants were categorised into three groups: forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) <70, 70–75, and >75. They were followed throughout 2017 using Danish national registries. Lung disease was defined as fulfilling at least one of the following: two prescriptions for respiratory medicine were redeemed within a year; one lung-related contact to the hospital; or lung-related death.ResultsIn the 1991 cohort, 21% developed lung diseases and in the 2006 cohort 17% developed lung diseases throughout 2017. The probability of developing lung disease if FEV1/FVC 70–75 was 35% (95% confidence interval [CI] = 25% to 44%) in the 1991 cohort and 23% (95% CI = 17% to 28%) in the 2006 cohort. The positive predicted value (PPV) was higher for both cohorts when focusing on smoking history and self-reported respiratory symptoms.ConclusionThe initial spirometry has a high predictive value to identify cases of future lung diseases. In addition, the group with FEV1/FVC 70–75 had a high risk of developing lung diseases later in life, suggesting this group would be a meaningful target of special interest.

Preliminary Study on Human Lung Function of Artisanal and Small-scale Gold Miner in Gorontalo Province, Indonesia

Effects of mercury from ASGM in humans have been reported in various studies. The effect depends on the dose, the form of mercury, and a way of entering the body. The human body with all its perfection has a remarkable system of defense against external negative effects including toxic effects of mercury. One of the leading defense systems of the human body is the respiratory system, because it is an organ connected directly to the outside environment. Early detection is very necessary to prevent the incidence of more severe, then the initial lung examination is needed and important. For that, there needs to be an examination of the lungs that can be used to find early lung disorders, namely the spirometry test. During this time in each study was only performed blood tests, urine and hair to assess the levels of mercury, but the disease-based examination is limited to only a neuropsychological examination. Spirometry is useful and basic in the diagnosis of lung diseases. In addition to assessing respiratory function primarily assessing breathing pattern, such as pulmonary fibrosis or asthma, it is also very helpful in health surveillance. In the preliminary survey, 4 samples were taken consisting of 2 miner samples and 2 samples non-miners (control). These 4 sample have the same age range, with the same smoking status. The spirometry test result shows that all miner had lung disorders and control samples had normal results. The conclusions from this survey are: (1) Mercury pollution must be a serious attention for all of us; (2) all miners tend to be exposed to evaporated mercury; (3) Spirometry tests can be one of the health examinations for early detection of lung disease.

An application of ANFIS for Lung Diseases Early Detection System

Indonesian Basic Health Research in 2018 showed the prevalence of pneumonia, pulmonary tuberculosis (TB) and lung cancer in Indonesia 4.0% 0.4% and 0.18%, respectively. However, the number of lung specialists is small. According to the Indonesian Lung Specialist Association webpage, the number of doctors joined in the association up to 2008 were 452. This amount is very less when compared with existing lung disease cases. Thus, the handling of lung disease will be too late. The use of ANFIS for early detection of lung disease is growing. However, the systems designed are need preprocessing data to be executed and still applied for one type of disease. This paper will design a desktop application based on ANFIS expert system to detect lung disease early, i.e. for pneumonia, pulmonary TB and lung cancer. The system will work based on simple symptoms expressed by the patient. Subtractive clustering is used for clustering process. The results of the training showed that the models were able to give better performance compared to the model built using conventional clustering methods. The test results show that those three models have comparable performance compared to their counterpart. Software validation shows that the it gives 94.00% succeed for training data and up to 100% for testing data. This application is not intended to replace the role of a doctor, but to help diagnose the patient's condition earlier.

Design of pneumonia and pulmonary tuberculosis early detection system based on adaptive neuro fuzzy inference system

The results of Basic Health Research in 2018 showed the prevalence of pneumonia and pulmonary tuberculosis (TB) in Indonesia 4.0 percent and 0.4 percent, respectively. However, with a minimal number of lung specialists, the handling of lung disease will be too late. There are only 600-700 lung specialists in Indonesia. This amount is very less when compared with existing lung disease cases. The use of ANFIS for early detection of lung disease is growing. However, the system designed is still used for one type of disease. This research will design an expert system based on ANFIS to detect lung disease early, i.e. for pneumonia and pulmonary TB. Subtractive clustering is used for clustering process. The results of the training showed that both models were able to give better performance compared to the model built using conventional clustering methods. The test results show that both models have comparable performance compared to their counterpart.

Wireless smartphone-assisted personal healthcare monitoring system using a MoS2-based flexible, wearable and ultra-low-cost functional sensor

Flexible, wearable, functional sensors that can quantify electrical signals generated by human activities are of great importance in personal healthcare monitoring. However, the high fabrication cost of these sensors along with the unreliable front-end processing of data restricts their widespread usage. In this report, we demonstrate MoS2 growth on Al foil which was further integrated onto eraser substrate to develop smart, low-cost motion sensors (pedometer and gesture communication) and a breath sensor by measuring physiological parameters such as strain, touch and hydration levels of lungs, respectively. The data generated are wirelessly transmitted to the smartphone via Bluetooth and analyzed using dedicated Android applications for individual sensing displays. For the pedometer, the fabricated sensor was integrated onto the knee which could then calculate the steps taken, distance covered, speed and approximate number of calories burned by the individual. Gesture communication helps deaf/dumb/paralyzed individuals to communicate with the external environment using finger movements. Breath sensing allows for the early detection of lung diseases by monitoring the hydration levels of the lungs. Furthermore, the piezotronic effect of MoS2 on breath sensing was systematically studied, and a 56.8% increase in the response was observed under a 16% strain. The sensing mechanism for each stimulus is explained via modulation in the charge transport properties for each stimulus. The sensor exhibited excellent durability where the device performance was found to be stable even after 500 continuous bending cycles. The successful demonstration of such low-cost functional wireless personal healthcare monitoring systems for Internet of Things applications is a major step forward in flexible and wearable electronics.

Smart System for Lung Disease Early Detection

Tuberculosis and Pneumonia are two of the ten deadliest diseases in Indonesia, and Chronic Obstructive Pulmonary Disease (COPD) ranks fourth as the leading cause of death in the world. In fact, Indonesian people still lack awareness of COPD. However, some conditions of body organs such as lungs can be diagnosed through sounds. Although pulmonary auscultation has been widely used, technological advancement in the field of digital lung sound analysis and classification provide new possibilities that need to be explored. ANFIS is a popular branch of AI science and is widely applied for classification, prediction, and image processing. ANFIS is essentially able to implement human expertise used in a decision-making process. The objectives of this study are (1) to develop a smart system for the early detection of lung diseases using ANFIS, and (2) to determine smart system accuracy. The smart system was developed using a Waterfall model in sequence starting from analysis, design, coding, and testing. ANFIS that had been built consisted of four inputs and one output, with 81 rules. At the ANFIS development stage, several ANFIS architectures with MF type and different optimization methods had been tested. The smallest RSME value was 4.5357e-06, which was obtained from the training data testing using MF trapezoid (trapmf), hybrid optimization method, error tolerance of 0.0001, and 30 epochs.

Effect on attendance by including focused information on spirometry in preventive health checks: study protocol for a randomized controlled trial.

BackgroundEarly detection of lung diseases can help to reduce their severity. Lung diseases are among the most frequently occurring and serious diseases worldwide; nonetheless, many patients remain undiagnosed. Preventive health checks including spirometry can detect lung diseases at early stages; however, recruitment for health checks remains a challenge, and little is known about what motivates the attendance.The aim of the study is to examine whether focused information on spirometry in the invitation compared to general information will impact the attendance rate in preventive health checks.Methods/designThis randomized, controlled trial tests the effect of information on spirometry embedded in the Check your Health Preventive Program (CHPP). The CHPP is an open-label, household cluster-randomized, controlled trial offering a preventive health check to 30- to –49-year-olds in a Danish municipality from 2012 to 2017 (n = 26,216). During 2015–2016, 4356 citizens aged 30–49 years will be randomized into two groups. The intervention group receives an invitation which highlights the value and contents of spirometry as part of a health check and information about lung diseases. The comparison group receives a standard invitation containing practical information and specifies the contents of the general health check.Outcomes are (1) differences in attendance rates measured by the proportion of citizens attending each of the two study groups and (2) proportion of persons at risk defined by smoking status and self-reported lung symptoms in the study groups. The proportion of participants with abnormal spirometry assessed at the preventive health check will be compared between the two study groups.DiscussionThe results from the present study will inform future recruitment strategies to health checks. The developed material on content, value, and information about lung disease is feasible and transferable to other populations, making it easy to implement if effective.Trial registrationClinicalTrials.gov: NCT02615769. Registered on 25 November 2015.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-016-1704-7) contains supplementary material, which is available to authorized users.

폐질환 조기 검출을 위한 결합 히스토그램 기반의 통계적 특징 인자에 대한 연구

폐질환 조기 검출을 위한 결합 히스토그램 기반의 통계적 특징 인자에 대한 연구

Exercise responses are related to structural lung damage in CF pulmonary disease.

Early detection of lung disease is a primary objective in monitoring patients with Cystic Fibrosis (CF); High-Resolution-Computed-Tomography (HRCT) assesses structural damage. Spirometry and cardiopulmonary exercise testing are used for functional evaluation of CF lung disease. To evaluate the deterioration of exercise testing parameters over a 2-year period compared to the change of spirometry and HRCT parameters among CF patients. Twenty-eight CF patients were evaluated with HRCT, spirometry, and exercise testing; 15 had two assessments with an interval of 2 years. Correlation analyses between Bhalla score parameters and functional measures were performed. Twenty-eight patients with CF (mean age 14.9 years, mean forced expiratory volume in 1 sec [FEV1 ] 83.2%) were evaluated. FEV1 was not found to change significantly in the 2-year period (P = 0.612). Both mean Bhalla score and mean peak oxygen consumption (VO2 peak %) deteriorated significantly (P = 0.014 and P = 0.026, respectively). VO2 peak and respiratory equivalents for O2 and CO2 at peak exercise were found to be significant predictors of Bhalla score (r = -0.477, P = 0.010; r = 0.461, P = 0.018; r = 0.402; P = 0.042, respectively). Anaerobic threshold was associated with changes in Bhalla score over the following 2 years. Exercise testing is more sensitive than spirometry to detect structural changes in CF lungs. Pediatr Pulmonol. 2016; 51:914-920. © 2016 Wiley Periodicals, Inc.

Assessment and Detection of Early Lung Disease in Cystic Fibrosis.

It is now widely accepted that cystic fibrosis (CF) lung disease begins in infancy and early childhood, with evidence of structural, functional, and inflammatory changes present even in asymptomatic children. This realization, coupled with broad adoption of CF newborn screening in the United States, Canada, Europe, and Australia, has raised the possibility for primary prevention of CF lung disease. To intervene before respiratory symptoms develop in infants and preschool children with CF, sensitive testing modalities are necessary to define and follow mild CF lung disease, both for research and for clinical purposes. Ideal testing would be sensitive to early heterogeneous disease located in the peripheral airways, able to detect small changes in disease (either progression or therapeutic response), easy to perform without sedation, and safe for repeated measures. This article will review existing and novel modalities for identification of CF lung disease in infancy and early childhood, focusing on pulmonary function testing, structural imaging, identification of infectious pathogens, and detection of inflammation.