Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles
The use of dry powder inhalers (DPIs) represents a cornerstone in the treatment of chronic pulmonary diseases. However, suboptimal inhalation techniques, including inadequate airflow rates, have been a persistent concern for achieving effective therapeutic outcomes, as many patients remain unaware o...
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| Format: | Article |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4402 |
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| author | Ziyi Fan Yuqing Ye Jiale Chen Ying Ma Jesse Zhu |
| author_facet | Ziyi Fan Yuqing Ye Jiale Chen Ying Ma Jesse Zhu |
| author_sort | Ziyi Fan |
| collection | DOAJ |
| description | The use of dry powder inhalers (DPIs) represents a cornerstone in the treatment of chronic pulmonary diseases. However, suboptimal inhalation techniques, including inadequate airflow rates, have been a persistent concern for achieving effective therapeutic outcomes, as many patients remain unaware of their insufficient inhalation performance. As an effective strategy, a digital monitoring system, coupled with dry powder inhalers (DPIs), has emerged to estimate flow profiles and provide inhalation information. The estimation could be further facilitated by the application of artificial intelligence (AI). In this work, a novel digital system to primarily monitor pressure during DPI usage was successfully designed, and advanced machine learning (ML) techniques were then employed to estimate inhalation flow profiles based on the captured data. Four optimal machine learning models were selected for subsequent inhalation parameter prediction, given their superior generalization ability. By using these models, inhalation flow profiles could be successfully estimated, with an excellent accuracy of 97.7% for Peak Inspiratory Flow Rate (PIFR) and 95.2% for inspiratory capacity (IC). In summary, the pressure-based digital monitoring system empowered by AI techniques could be successfully applied to assess inhalation flow profiles with excellent accuracy. |
| format | Article |
| id | doaj-art-92f6a76f64fd4ec38d24bb03424f5a92 |
| institution | Matheson Library |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-92f6a76f64fd4ec38d24bb03424f5a922025-07-25T13:36:13ZengMDPI AGSensors1424-82202025-07-012514440210.3390/s25144402Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow ProfilesZiyi Fan0Yuqing Ye1Jiale Chen2Ying Ma3Jesse Zhu4Department of Biomedical Engineering, University of Western Ontario, London, ON N6A 3K7, CanadaDepartment of Biomedical Engineering, University of Western Ontario, London, ON N6A 3K7, CanadaDepartment of Chemical Engineering, Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, The University of Nottingham Ningbo China, Ningbo 315100, ChinaDepartment of Biomedical Engineering, University of Western Ontario, London, ON N6A 3K7, CanadaDepartment of Biomedical Engineering, University of Western Ontario, London, ON N6A 3K7, CanadaThe use of dry powder inhalers (DPIs) represents a cornerstone in the treatment of chronic pulmonary diseases. However, suboptimal inhalation techniques, including inadequate airflow rates, have been a persistent concern for achieving effective therapeutic outcomes, as many patients remain unaware of their insufficient inhalation performance. As an effective strategy, a digital monitoring system, coupled with dry powder inhalers (DPIs), has emerged to estimate flow profiles and provide inhalation information. The estimation could be further facilitated by the application of artificial intelligence (AI). In this work, a novel digital system to primarily monitor pressure during DPI usage was successfully designed, and advanced machine learning (ML) techniques were then employed to estimate inhalation flow profiles based on the captured data. Four optimal machine learning models were selected for subsequent inhalation parameter prediction, given their superior generalization ability. By using these models, inhalation flow profiles could be successfully estimated, with an excellent accuracy of 97.7% for Peak Inspiratory Flow Rate (PIFR) and 95.2% for inspiratory capacity (IC). In summary, the pressure-based digital monitoring system empowered by AI techniques could be successfully applied to assess inhalation flow profiles with excellent accuracy.https://www.mdpi.com/1424-8220/25/14/4402dry powder inhalersdigital monitoring systemmachine learningflowrate estimationinhalation flow profile |
| spellingShingle | Ziyi Fan Yuqing Ye Jiale Chen Ying Ma Jesse Zhu Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles Sensors dry powder inhalers digital monitoring system machine learning flowrate estimation inhalation flow profile |
| title | Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles |
| title_full | Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles |
| title_fullStr | Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles |
| title_full_unstemmed | Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles |
| title_short | Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles |
| title_sort | development of an ai empowered novel digital monitoring system for inhalation flow profiles |
| topic | dry powder inhalers digital monitoring system machine learning flowrate estimation inhalation flow profile |
| url | https://www.mdpi.com/1424-8220/25/14/4402 |
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