Dataset Information

Feasibility of a Portable Electronic Nose for Detection of Oral Squamous Cell Carcinoma in Sudan.

ABSTRACT: Oral squamous cell carcinoma (OSCC) is increasing at an alarming rate particularly in low-income countries. This urges for research into noninvasive, user-friendly diagnostic tools that can be used in limited-resource settings. This study aims to test and validate the feasibility of e-nose technology for detecting OSCC in the limited-resource settings of the Sudanese population. Two e-nose devices (Aeonose™, eNose Company, Zutphen, The Netherlands) were used to collect breath samples from OSCC (n = 49) and control (n = 35) patients. Patients were divided into a training group for building an artificial neural network (ANN) model and a blinded control group for model validation. The Statistical Package for the Social Sciences (SPSS) software was used for the analysis of baseline characteristics and regression. Aethena proprietary software was used for data analysis using artificial neural networks based on patterns of volatile organic compounds. A diagnostic accuracy of 81% was observed, with 88% sensitivity and 71% specificity. This study demonstrates that e-nose is an efficient tool for OSCC detection in limited-resource settings, where it offers a valuable cost-effective strategy to tackle the burden posed by OSCC.

SUBMITTER: Mohamed N

PROVIDER: S-EPMC8147635 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Feasibility of a Portable Electronic Nose for Detection of Oral Squamous Cell Carcinoma in Sudan.

Mohamed Nazar N van de Goor Rens R El-Sheikh Mariam M Elrayah Osman O Osman Tarig T Nginamau Elisabeth Sivy ES Johannessen Anne Christine AC Suleiman Ahmed A Costea Daniela Elena DE Kross Kenneth W KW

Healthcare (Basel, Switzerland) 20210503 5

<h4>Background</h4>Oral squamous cell carcinoma (OSCC) is increasing at an alarming rate particularly in low-income countries. This urges for research into noninvasive, user-friendly diagnostic tools that can be used in limited-resource settings. This study aims to test and validate the feasibility of e-nose technology for detecting OSCC in the limited-resource settings of the Sudanese population.<h4>Methods</h4>Two e-nose devices (Aeonose™, eNose Company, Zutphen, The Netherlands) were used to ...[more]

PMID: 34063592

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Project description:IntroductionOral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model's objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization.MethodsThe research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies.ResultsThe customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model's efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC.DiscussionThis research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model's ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC.

Dataset Information

Feasibility of a Portable Electronic Nose for Detection of Oral Squamous Cell Carcinoma in Sudan.

Publications

Feasibility of a Portable Electronic Nose for Detection of Oral Squamous Cell Carcinoma in Sudan.

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