Zazo-Manzaneque, R.; Pons-Beltrán, V.; Vidaurre, A.; Santonja, A.; Sánchez-Diaz, C. (2022). Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System. Sensors. 22(14):1-18. https://doi.org/10.3390/s22145211
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/193908
Title:
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Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System
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Author:
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Zazo-Manzaneque, Roberto
Pons-Beltrán, Vicente
Vidaurre, Ana
Santonja, Alberto
Sánchez-Diaz, Carlos
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UPV Unit:
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Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny
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Issued date:
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Abstract:
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[EN] Current enteroscopy techniques present complications that are intended to be improved with the development of a new semi-automatic device called Endoworm. It consists of two different types of inflatable cavities. For ...[+]
[EN] Current enteroscopy techniques present complications that are intended to be improved with the development of a new semi-automatic device called Endoworm. It consists of two different types of inflatable cavities. For its correct operation, it is essential to detect in real time if the inflatable cavities are malfunctioning (presence of air leakage). Two classification predictive models were obtained, one for each cavity typology, which must discern between the ¿Right¿ or ¿Leak¿ states. The cavity pressure signals were digitally processed, from which a set of features were extracted and selected. The predictive models were obtained from the features, and a prior classification of the signals between the two possible states was used as input to different su-pervised machine learning algorithms. The accuracy obtained from the classification predictive model for cavities of the balloon-type was 99.62%, while that of the bellows-type was 100%, repre-senting an encouraging result. Once the models are validated with data generated in animal model tests and subsequently in exploratory clinical tests, their incorporation in the software device will ensure patient safety during small bowel exploration.
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Subjects:
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Classification predictive models
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Digital signal processing
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Enteroscopy
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Feature extraction
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Inflatable cavities
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Medical device
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Real-time detection system
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Soft robot
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Copyrigths:
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Reconocimiento (by)
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Source:
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Sensors. (eissn:
1424-8220
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DOI:
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10.3390/s22145211
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Publisher:
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MDPI AG
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Publisher version:
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https://doi.org/10.3390/s22145211
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Project ID:
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info:eu-repo/grantAgreement/Instituto de Salud Carlos III//PI18%2F01365//Optimización del dispositivo Endoworm de asistencia para la realización de enteroscopia/
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Thanks:
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The study was funded by the Spanish Ministry of Economy and Competitiveness through Project (PI18/01365) and by the UPV/IIS LA Fe through the (Endoworm 3.0) Project. CIBER-BBN is an initiative funded by the VI National ...[+]
The study was funded by the Spanish Ministry of Economy and Competitiveness through Project (PI18/01365) and by the UPV/IIS LA Fe through the (Endoworm 3.0) Project. CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with the assistance of the European Regional Development Fund.
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Type:
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Artículo
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