scholarly journals Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?

2021 ◽  
Vol 12 ◽  
Author(s):  
Paresh C. Giri ◽  
Anand M. Chowdhury ◽  
Armando Bedoya ◽  
Hengji Chen ◽  
Hyun Suk Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pulmonary Function ◽  
Clinical Medicine ◽  
Pulmonary Function Tests ◽  
New Developments ◽  
Lung Physiology ◽  
Information Technologists ◽  
Impact Patient Care ◽  
Numeric Data ◽  
Washout Curves

Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert’s pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual’s clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.

scholarly journals Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 33 ◽  
Author(s):  
Joshua Gawlitza ◽  
Timo Sturm ◽  
Kai Spohrer ◽  
Thomas Henzler ◽  
Ibrahim Akin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Function ◽  
Pulmonary Function ◽  
Polynomial Regression ◽  
Pulmonary Function Tests ◽  
Quantitative Computed Tomography ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Function Tests

Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.

scholarly journals A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1880
Author(s):  
Giuseppe Murdaca ◽  
Simone Caprioli ◽  
Alessandro Tonacci ◽  
Lucia Billeci ◽  
Monica Greco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Pulmonary Function ◽  
Small Sample Size ◽  
Pulmonary Function Tests ◽  
Pulmonary Involvement ◽  
Small Sample ◽  
Lung Involvement ◽  
High Resolution Computed Tomography ◽  
Function Tests

Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations; however, this would lead to a risk of overtesting, with considerable costs for the health system and an unnecessary burden for the patients. To this extent, Machine Learning (ML) algorithms could represent a useful add-on to the current clinical practice for diagnostic purposes and could help retrieve the most useful exams to be carried out for diagnostic purposes. Method: Here, we retrospectively collected high resolution computed tomography, pulmonary function tests, esophageal pH impedance tests, esophageal manometry and reflux disease questionnaires of 38 patients with SSc, applying, with R, different supervised ML algorithms, including lasso, ridge, elastic net, classification and regression trees (CART) and random forest to estimate the most important predictors for pulmonary involvement from such data. Results: In terms of performance, the random forest algorithm outperformed the other classifiers, with an estimated root-mean-square error (RMSE) of 0.810. However, this algorithm was seen to be computationally intensive, leaving room for the usefulness of other classifiers when a shorter response time is needed. Conclusions: Despite the notably small sample size, that could have prevented obtaining fully reliable data, the powerful tools available for ML can be useful for predicting early lung involvement in SSc patients. The use of predictors coming from spirometry and pH impedentiometry together might perform optimally for predicting early lung involvement in SSc.

Study of pulmonary function tests in tobacco factory workers

2018 ◽  
Vol 6 (3) ◽  
pp. 16-19
Author(s):  
Gajanan V Patil ◽  
◽  
Atish Pagar ◽  
U S Patil ◽  
M K Parekh ◽  
...  
Keyword(s):  
Pulmonary Function ◽  
Pulmonary Function Tests ◽  
Factory Workers ◽  
Function Tests

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

Pulmonary Function Tests in European Birth Cohorts

2013 ◽  
Vol 9 (1) ◽  
pp. 3-10
Author(s):  
Linus Grabenhenrich ◽  
Cynthia Hohmann ◽  
Remy Slama ◽  
Joachim Heinrich ◽  
Magnus Wickman ◽  
...  
Keyword(s):  
Pulmonary Function ◽  
Pulmonary Function Tests ◽  
Birth Cohorts ◽  
Function Tests

Pulmonary Function Tests Fail to Predict Exercise Intolerance in Sheep with Emphysema

2005 ◽  
Vol 37 (4) ◽  
pp. 550-556
Author(s):  
MELISSA R. MAZAN ◽  
EDWARD P. INGENITO ◽  
LARRY TSAI ◽  
ANDREW HOFFMAN
Keyword(s):  
Pulmonary Function ◽  
Pulmonary Function Tests ◽  
Exercise Intolerance ◽  
Function Tests

scholarly journals Pulmonary Function Testing in Work-Related Asthma: An Overview from Spirometry to Specific Inhalation Challenge

2021 ◽  
Vol 18 (5) ◽  
pp. 2325
Author(s):  
Mathias Poussel ◽  
Isabelle Thaon ◽  
Emmanuelle Penven ◽  
Angelica I. Tiotiu
Keyword(s):  
Pulmonary Function ◽  
Pulmonary Function Tests ◽  
Objective Evaluation ◽  
Pulmonary Function Testing ◽  
Initial Assessment ◽  
Work Related ◽  
Specific Inhalation Challenge ◽  
Occupational Setting ◽  
Inhalation Challenge ◽  
Function Testing

Work-related asthma (WRA) is a very frequent condition in the occupational setting, and refers either to asthma induced (occupational asthma, OA) or worsened (work-exacerbated asthma, WEA) by exposure to allergens (or other sensitizing agents) or to irritant agents at work. Diagnosis of WRA is frequently missed and should take into account clinical features and objective evaluation of lung function. The aim of this overview on pulmonary function testing in the field of WRA is to summarize the different available tests that should be considered in order to accurately diagnose WRA. When WRA is suspected, initial assessment should be carried out with spirometry and bronchodilator responsiveness testing coupled with first-step bronchial provocation testing to assess non-specific bronchial hyper-responsiveness (NSBHR). Further investigations should then refer to specialists with specific functional respiratory tests aiming to consolidate WRA diagnosis and helping to differentiate OA from WEA. Serial peak expiratory flow (PEF) with calculation of the occupation asthma system (OASYS) score as well as serial NSBHR challenge during the working period compared to the off work period are highly informative in the management of WRA. Finally, specific inhalation challenge (SIC) is considered as the reference standard and represents the best way to confirm the specific cause of WRA. Overall, clinicians should be aware that all pulmonary function tests should be standardized in accordance with current guidelines.

Sign in / Sign up

Export Citation Format

Share Document