Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System
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 ca...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5211 |
| _version_ | 1797433126718275584 |
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| author | Roberto Zazo-Manzaneque Vicente Pons-Beltrán Ana Vidaurre Alberto Santonja Carlos Sánchez-Díaz |
| author_facet | Roberto Zazo-Manzaneque Vicente Pons-Beltrán Ana Vidaurre Alberto Santonja Carlos Sánchez-Díaz |
| author_sort | Roberto Zazo-Manzaneque |
| collection | DOAJ |
| description | 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 “<i>Right</i>” or “<i>Leak</i>” 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 supervised machine learning algorithms. The accuracy obtained from the classification predictive model for cavities of the <i>balloon-type</i> was 99.62%, while that of the <i>bellows-type</i> was 100%, representing 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. |
| first_indexed | 2024-03-09T10:12:41Z |
| format | Article |
| id | doaj.art-9e629c2825d4479c86f83d069dd4bb51 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:12:41Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9e629c2825d4479c86f83d069dd4bb512023-12-01T22:40:02ZengMDPI AGSensors1424-82202022-07-012214521110.3390/s22145211Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy SystemRoberto Zazo-Manzaneque0Vicente Pons-Beltrán1Ana Vidaurre2Alberto Santonja3Carlos Sánchez-Díaz4Centre for Biomaterials and Tissue Engineering (CBIT), Universitat Politècnica de València, 46022 Valencia, SpainDigestive Endoscopy Unit, Digestive Diseases Department, La Fe Polytechnic Univesity Hospital, 46026 Valencia, SpainCentre for Biomaterials and Tissue Engineering (CBIT), Universitat Politècnica de València, 46022 Valencia, SpainSchool of Design Engineering (ETSID), Universitat Politècnica de València, 46022 Valencia, SpainDepartment of Electronic Engineering, Universitat Politècnica de València, 46022 Valencia, SpainCurrent 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 “<i>Right</i>” or “<i>Leak</i>” 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 supervised machine learning algorithms. The accuracy obtained from the classification predictive model for cavities of the <i>balloon-type</i> was 99.62%, while that of the <i>bellows-type</i> was 100%, representing 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.https://www.mdpi.com/1424-8220/22/14/5211classification predictive modelsdigital signal processingenteroscopyfeature extractioninflatable cavitiesmedical device |
| spellingShingle | Roberto Zazo-Manzaneque Vicente Pons-Beltrán Ana Vidaurre Alberto Santonja Carlos Sánchez-Díaz Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System Sensors classification predictive models digital signal processing enteroscopy feature extraction inflatable cavities medical device |
| title | Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System |
| title_full | Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System |
| title_fullStr | Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System |
| title_full_unstemmed | Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System |
| title_short | Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System |
| title_sort | classification predictive model for air leak detection in endoworm enteroscopy system |
| topic | classification predictive models digital signal processing enteroscopy feature extraction inflatable cavities medical device |
| url | https://www.mdpi.com/1424-8220/22/14/5211 |
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