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Machine Learning-Assisted Portable Electrical Impedance Tomography Enables Accurate Lung Function Assessment.

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Bibliographic Details
Title: Machine Learning-Assisted Portable Electrical Impedance Tomography Enables Accurate Lung Function Assessment.
Authors: Li JHW, Lawson M, Lui H, Huen V, Wong EC, Zhou IY, Kwok WC, Chan RW
Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2025 Jul; Vol. 2025, pp. 1-4.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: [IEEE] Country of Publication: United States NLM ID: 101763872 Publication Model: Print Cited Medium: Internet ISSN: 2694-0604 (Electronic) Linking ISSN: 23757477 NLM ISO Abbreviation: Annu Int Conf IEEE Eng Med Biol Soc Subsets: MEDLINE
Imprint Name(s): Original Publication: [Piscataway, NJ] : [IEEE], [2007]-
MeSH Terms: Machine Learning* , Tomography*/methods , Tomography*/instrumentation , Respiratory Function Tests*/methods , Respiratory Function Tests*/instrumentation , Lung*/physiopathology, Humans ; Electric Impedance ; Male ; Pulmonary Disease, Chronic Obstructive/diagnosis ; Pulmonary Disease, Chronic Obstructive/physiopathology ; Female ; Middle Aged
Abstract: Respiratory diseases have led to millions of deaths in the world (e.g., 3.5 million for chronic obstructive pulmonary disease (COPD)). While early detection can significantly improve patient outcome, choices for wide-spread, affordable screening tools are limited. Current clinical standard of lung function assessment relies on in-hospital spirometry, yet portable spirometers can only provide limited knowledge for the localization of respiratory pathological conditions which is crucial for personalized assessments and treatment. Meanwhile, electrical impedance tomography (EIT) has gained traction due to its affordability, safety, and portability. However, application in assessing lung function has traditionally been limited by heavy and bulk machinery. In this study, we present a novel system for evaluating lung function using a portable EIT system. Combined with machine learning, the system offers the ability to monitor lung function caused by conditions such as asthma, COPD, and other respiratory disorders. We also leveraged the power of machine learning and EIT to offer a clinically valuable alternative for affordable lung regional-based assessment. Our findings demonstrate good correlation with pulmonary functional test (R 2 = 0.619 (FEV1); 0.646 (FVC); 0.538 (FEV1/FVC), all p < 0.01) while providing interpretability into global and regional lung function prediction and potentially diagnosed COPD as in a clinical setting (sensitivity = 82%; specificity= 73%), highlighting the potential as an affordable, non-invasive tool for both global and regional respiratory assessment.Clinical RelevancePortable, affordable EIT system aided by machine learning, enables home and community-based monitoring and screening for multiple respiratory conditions and provide insights to regional pulmonary characteristics.
Entry Date(s): Date Created: 20251203 Date Completed: 20251203 Latest Revision: 20251203
Update Code: 20251204
DOI: 10.1109/EMBC58623.2025.11251726
PMID: 41336486
Database: MEDLINE
Description
Abstract:Respiratory diseases have led to millions of deaths in the world (e.g., 3.5 million for chronic obstructive pulmonary disease (COPD)). While early detection can significantly improve patient outcome, choices for wide-spread, affordable screening tools are limited. Current clinical standard of lung function assessment relies on in-hospital spirometry, yet portable spirometers can only provide limited knowledge for the localization of respiratory pathological conditions which is crucial for personalized assessments and treatment. Meanwhile, electrical impedance tomography (EIT) has gained traction due to its affordability, safety, and portability. However, application in assessing lung function has traditionally been limited by heavy and bulk machinery. In this study, we present a novel system for evaluating lung function using a portable EIT system. Combined with machine learning, the system offers the ability to monitor lung function caused by conditions such as asthma, COPD, and other respiratory disorders. We also leveraged the power of machine learning and EIT to offer a clinically valuable alternative for affordable lung regional-based assessment. Our findings demonstrate good correlation with pulmonary functional test (R <sup>2</sup> = 0.619 (FEV1); 0.646 (FVC); 0.538 (FEV1/FVC), all p &lt; 0.01) while providing interpretability into global and regional lung function prediction and potentially diagnosed COPD as in a clinical setting (sensitivity = 82%; specificity= 73%), highlighting the potential as an affordable, non-invasive tool for both global and regional respiratory assessment.Clinical RelevancePortable, affordable EIT system aided by machine learning, enables home and community-based monitoring and screening for multiple respiratory conditions and provide insights to regional pulmonary characteristics.
ISSN:2694-0604
DOI:10.1109/EMBC58623.2025.11251726