ArticleDigital health
A low-cost artificial intelligence powered breath analyzer for early chronic obstructive pulmonary disease detection in resource-limited environment.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Adsorptive Gas Sensor Response Forecasting to Enable Breath-by-Breath Analysis.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Chronic Obstructive Pulmonary Disease (COPD) remains widely underdiagnosed in low- and middle-income countries due to reliance on costly, infrastructure-heavy diagnostic methods such as spirometry and radiographic imaging. This study aimed to design and validate a low-cost, artificial intelligence (AI)-powered Breath-Based Diagnostic (BBD) system for real-time COPD detection using exhaled volatile organic compounds (VOCs). Methods: The BBD system integrates an array of metal oxide semiconductor sensors with a lightweight one-dimensional convolutional neural network deployed on a Raspberry Pi 5 for edge-based inference. Diagnostic performance was evaluated using a publicly available VOC dataset and a custom dataset collected under controlled conditions with the prototype device. Data augmentation strategies simulated sensor drift and environmental variability to improve model robustness. System performance was assessed in terms of accuracy, precision, latency, power efficiency, cost trade-offs, and usability. A multilingual mobile interface and Retrieval-Augmented Generation chatbot were developed to support patient engagement, while adherence to HIPAA and FHIR standards ensured regulatory compliance. Results: The proposed system achieved 96.68% accuracy and 100% precision for COPD detection, with inference latency of 0.02 ms and power consumption below 2.5 W. A five-sensor configuration preserved 98% of diagnostic performance while reducing hardware cost by 30%. Usability testing with 31 participants yielded a System Usability Score of 86.3/100 and a chatbot trust rating of 4.4/5, confirming strong user acceptance. Conclusion: The study demonstrates the feasibility of deploying an explainable, low-cost, and energy-efficient BBD system for early COPD detection in resource-limited settings. By combining edge AI, affordable sensor arrays, and multilingual patient engagement, the BBD system offers a scalable and ethically grounded pathway for integration into national healthcare infrastructures and global respiratory health strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.