Breath Testing for Barrett’s Esophagus Using Exhaled Volatile Organic Compound Profiling With an Electronic Nose Device

Chan, Daniel K.; Zakko, Liam; Visrodia, Kavel; Leggett, Cadman L.; Lutzke, Lori S.; Clemens, Magdalen A.; Jg, Allen; Anderson, Marlys; Wang, Kenneth K.

doi:10.1053/j.gastro.2016.11.001

Cited by 63 publications

(35 citation statements)

References 7 publications

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“…Chan et al [30] used an e-nose, a device that utilizes chemical-to-electrical interfaces, to measure the volatile organic compounds (VOC) profiles of disease states. When paired with a machine learning platform, an e-nose can be trained as a canine to serve as a tool for noninvasive diagnostic testing.…”

Section: Neural Network-based Barrett's Esophagus Recognitionmentioning

confidence: 99%

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A survey on Barrett's esophagus analysis using machine learning

Souza

Palm

Mendel

et al. 2018

Computers in Biology and Medicine

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This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.

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Section: Neural Network-based Barrett's Esophagus Recognitionmentioning

confidence: 99%

“…The results achieved by the authors showed the proposed method can be more effective than the conventional supervised learning with an accuracy of around 90%. Chan et al [30] CNN 66 VOCs of patients presenting BE and 56 VOCs of patients without BE…”

Section: -Fold Crossvalidationmentioning

confidence: 99%

A survey on Barrett's esophagus analysis using machine learning

Souza

Palm

Mendel

et al. 2018

Computers in Biology and Medicine

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“…46 These devices are available in portable format for application in even non-medical facilities. 47 Although the standard practice for surveillance of Barrett's esophagus still involves the use of 4-quadrant biopsies taken every 1À2 cm, recent analysis of published literature has established that narrow-band imaging (NBI), laser confocal endomicroscopy, and volume laser endomicroscopy performed by experts have met the 90% accuracy threshold for detection of dysplasia in Barrett's esophagus established as significant for clinically implementation. 48 This brings in the era of targeted biopsies rather than multiple random biopsies, although it is not clear if this can be translated into the community setting at this point.…”

Section: Screening Surveillance and Treatment For Esophageal Cancermentioning

confidence: 99%

Recent Advances in Endoscopy

et al. 2017

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In this narrative review, invited by the Editors of Gastroenterology, we summarize recent advances in the field of gastrointestinal endoscopy. We have chosen articles published primarily in the past 2-3 years. Although a thorough literature review was performed for each topic, the nature of the article is subjective and systematic and is based on the authors' experience and expertise regarding articles we believed were most likely to be of high clinical and scientific importance.

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“…The major advances regarding the identification of human volatile compounds have been done in medical environment. In this regarding remarkable advances have been done in the application of electronic devices for the rapid and noninvasive detection of respiratory diseases as infections [108,109], asthma [110] and lung cancer [111] and gastrointestinal track diseases as colorectal cancer [112,113]. …”

Section: Electronic Nose Applicationsmentioning

confidence: 99%

Metal Oxide Gas Sensors, a Survey of Selectivity Issues Addressed at the SENSOR Lab, Brescia (Italy)

Ponzoni

Baratto

Cattabiani

et al. 2017

Sensors

150

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This work reports the recent results achieved at the SENSOR Lab, Brescia (Italy) to address the selectivity of metal oxide based gas sensors. In particular, two main strategies are being developed for this purpose: (i) investigating different sensing mechanisms featuring different response spectra that may be potentially integrated in a single device; (ii) exploiting the electronic nose (EN) approach. The former has been addressed only recently and activities are mainly focused on determining the most suitable configuration and measurements to exploit the novel mechanism. Devices suitable to exploit optical (photoluminescence), magnetic (magneto-optical Kerr effect) and surface ionization in addition to the traditional chemiresistor device are here discussed together with the sensing performance measured so far. The electronic nose is a much more consolidated technology, and results are shown concerning its suitability to respond to industrial and societal needs in the fields of food quality control and detection of microbial activity in human sweat.

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Breath Testing for Barrett’s Esophagus Using Exhaled Volatile Organic Compound Profiling With an Electronic Nose Device

Cited by 63 publications

References 7 publications

A survey on Barrett's esophagus analysis using machine learning

A survey on Barrett's esophagus analysis using machine learning

Recent Advances in Endoscopy

Metal Oxide Gas Sensors, a Survey of Selectivity Issues Addressed at the SENSOR Lab, Brescia (Italy)

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