Academic Journal
Extracting ventilatory waveforms from screen recordings: a validated image processing methodology and its application to predictive modelling.
| Τίτλος: | Extracting ventilatory waveforms from screen recordings: a validated image processing methodology and its application to predictive modelling. |
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| Συγγραφείς: | Ruiz I; Universidad Santiago de Cali, Grupo de Investigación GIEIAM, Cali, Valle, Colombia.; Universidad del Valle, Research Team IMPETUS INDOMITUS, Cali, Valle, Colombia.; Universidad del Valle, Grupo de Investigación Bionovo, Cali, Valle, Colombia., Jaramillo G; Universidad del Valle, Research Team IMPETUS INDOMITUS, Cali, Valle, Colombia., García JI; Universidad del Valle, Grupo de Investigación Bionovo, Cali, Valle, Colombia., Valencia A; Universidad del Valle, Grupo de Investigación GUIA, Cali, Valle, Colombia., Segura A; Universidad Santiago de Cali, Grupo de Investigación Salud y Movimiento, Cali, Valle, Colombia., Caballero-Lozada AF; Universidad del Valle, Grupo de Investigación INVANEST, Cali, Valle, Colombia.; Departamento de Anestesiología y Medicina Crítica y Cuidado Intensivo, Hospital Universitario del Valle, Cali, Valle, Colombia.; Departamento de Anestesiología y Medicina Crítica y Cuidado Intensivo, Hospital San Jose de Buga, Buga, Valle, Colombia. |
| Πηγή: | Biomedical physics & engineering express [Biomed Phys Eng Express] 2026 Jul 27; Vol. 12 (4). Date of Electronic Publication: 2026 Jul 27. |
| Τύπος έκδοσης: | Journal Article; Validation Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol : IOP Publishing Ltd., [2015]- |
| Ιατρικοί όροι (MeSH): | Image Processing, Computer-Assisted*/methods , Respiratory Mechanics*/physiology , Models, Biological*, Humans ; Reproducibility of Results ; Algorithms ; Airway Resistance |
| Περίληψη: | Access to high-fidelity ventilatory waveform data remains a significant challenge in respiratory mechanics research, particularly in resource-constrained environments. This study introduces and validates a novel, accessible framework for acquiring ventilatory data by applying image processing techniques to ventilator screen recordings. The accuracy of the framework was evaluated, demonstrating high fidelity (> 0.99) when compared with sensor-derived data from a laboratory emulator. Its reliability was further confirmed by demonstrating that image-derived patient data reproduced established correlations in respiratory mechanics previously obtained from sensor-based measurements. As a proof of concept for the utility of this validated framework, a novel predictive single compartment model was developed to estimate expiratory parameters from inspiratory phase data. By introducing two auxiliary parameters (and), this model established the first predictive relationship for expiratory airway resistance and reference pressure, addressing a key limitation of previous approaches. The predictive model performed consistently in sedated patients but as expected, its linear nature was unable to reproduce the complex dynamics of spontaneous breathing and asynchrony. Overall, this study establishes image processing as a reliable and accessible method for ventilatory data acquisition, warranting further validation across diverse technical conditions and patient cohorts. It demonstrates how this data can support new predictive tools and concludes that while linear models fail to capture asynchrony, this predictive failure itself shows promise as a potential non-invasive indicator for its detection. (© 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) |
| Contributed Indexing: | Keywords: expiratory parameters; image processing; inverse modelling; model-based methods; single compartment model |
| Entry Date(s): | Date Created: 20260715 Date Completed: 20260727 Latest Revision: 20260727 |
| Update Code: | 20260727 |
| DOI: | 10.1088/2057-1976/ae8af9 |
| PMID: | 42456710 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2057-1976 |
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| DOI: | 10.1088/2057-1976/ae8af9 |