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Gudiño-Ochoa A, García-Rodríguez JA, Ochoa-Ornelas R, Cuevas-Chávez JI, Sánchez-Arias DA. Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose. SENSORS (BASEL, SWITZERLAND) 2024; 24:1294. [PMID: 38400451 PMCID: PMC10891698 DOI: 10.3390/s24041294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 02/25/2024]
Abstract
Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.
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Affiliation(s)
- Alberto Gudiño-Ochoa
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico; (A.G.-O.); (J.I.C.-C.); (D.A.S.-A.)
| | - Julio Alberto García-Rodríguez
- Centro Universitario del Sur, Departamento de Ciencias Computacionales e Innovación Tecnológica, Universidad de Guadalajara, Ciudad Guzmán 49000, Mexico
| | - Raquel Ochoa-Ornelas
- Systems and Computation Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico;
| | - Jorge Ivan Cuevas-Chávez
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico; (A.G.-O.); (J.I.C.-C.); (D.A.S.-A.)
| | - Daniel Alejandro Sánchez-Arias
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico; (A.G.-O.); (J.I.C.-C.); (D.A.S.-A.)
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Škapars R, Gašenko E, Broza YY, Sīviņš A, Poļaka I, Bogdanova I, Pčolkins A, Veliks V, Folkmanis V, Lesčinska A, Liepniece-Karele I, Haick H, Rumba-Rozenfelde I, Leja M. Breath Volatile Organic Compounds in Surveillance of Gastric Cancer Patients following Radical Surgical Management. Diagnostics (Basel) 2023; 13:diagnostics13101670. [PMID: 37238155 DOI: 10.3390/diagnostics13101670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/20/2023] [Accepted: 05/07/2023] [Indexed: 05/28/2023] Open
Abstract
As of today, there is a lack of a perfect non-invasive test for the surveillance of patients for potential relapse following curative treatment. Breath volatile organic compounds (VOCs) have been demonstrated to be an accurate diagnostic tool for gastric cancer (GC) detection; here, we aimed to prove the yield of the markers in surveillance, i.e., following curative surgical management. Patients were sampled in regular intervals before and within 3 years following curative surgery for GC; gas chromatography-mass spectrometry (GC-MS) and nanosensor technologies were used for the VOC assessment. GC-MS measurements revealed a single VOC (14b-Pregnane) that significantly decreased at 12 months, and three VOCs (Isochiapin B, Dotriacontane, Threitol, 2-O-octyl-) that decreased at 18 months following surgery. The nanomaterial-based sensors S9 and S14 revealed changes in the breath VOC content 9 months after surgery. Our study results confirm the cancer origin of the particular VOCs, as well as suggest the value of breath VOC testing for cancer patient surveillance, either during the treatment phase or thereafter, for potential relapse.
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Affiliation(s)
- Roberts Škapars
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Department of Abdominal and Soft Tissue Surgery, Oncology Center of Latvia, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Evita Gašenko
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Department of Abdominal and Soft Tissue Surgery, Oncology Center of Latvia, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Yoav Y Broza
- Department of Chemical Engineering and Russel Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Armands Sīviņš
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Department of Abdominal and Soft Tissue Surgery, Oncology Center of Latvia, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Inese Poļaka
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Inga Bogdanova
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Department of Abdominal and Soft Tissue Surgery, Oncology Center of Latvia, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Andrejs Pčolkins
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Department of Abdominal and Soft Tissue Surgery, Oncology Center of Latvia, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Viktors Veliks
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Valdis Folkmanis
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Anna Lesčinska
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Department of Abdominal and Soft Tissue Surgery, Oncology Center of Latvia, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Inta Liepniece-Karele
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Department of Abdominal and Soft Tissue Surgery, Oncology Center of Latvia, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Hossam Haick
- Department of Chemical Engineering and Russel Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Ingrīda Rumba-Rozenfelde
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Mārcis Leja
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Department of Abdominal and Soft Tissue Surgery, Oncology Center of Latvia, Riga East University Hospital, LV-1038 Riga, Latvia
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Bhandari MP, Polaka I, Vangravs R, Mezmale L, Veliks V, Kirshners A, Mochalski P, Dias-Neto E, Leja M. Volatile Markers for Cancer in Exhaled Breath-Could They Be the Signature of the Gut Microbiota? Molecules 2023; 28:molecules28083488. [PMID: 37110724 PMCID: PMC10141340 DOI: 10.3390/molecules28083488] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients (n = 16) and healthy controls (n = 33). Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography-mass spectrometry (GC-MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa (correlation of 0.891, p-value 0.045), whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa (correlation of 0.871, p-value 0.0007). This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.
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Affiliation(s)
| | - Inese Polaka
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Reinis Vangravs
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Linda Mezmale
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia
- Riga East University Hospital, LV-1038 Riga, Latvia
- Faculty of Residency, Riga Stradins University, LV-1007 Riga, Latvia
| | - Viktors Veliks
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Arnis Kirshners
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Pawel Mochalski
- Institute of Chemistry, Jan Kochanowski University of Kielce, PL-25406 Kielce, Poland
- Institute for Breath Research, University of Innsbruck, A-6850 Dornbirn, Austria
| | - Emmanuel Dias-Neto
- Laboratory of Medical Genomics, A.C.Camargo Cancer Center, Sao Paulo 01508-010, Brazil
| | - Marcis Leja
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia
- Digestive Diseases Center GASTRO, LV-1079 Riga, Latvia
- Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
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GestroNet: A Framework of Saliency Estimation and Optimal Deep Learning Features Based Gastrointestinal Diseases Detection and Classification. Diagnostics (Basel) 2022; 12:diagnostics12112718. [DOI: 10.3390/diagnostics12112718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/23/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
In the last few years, artificial intelligence has shown a lot of promise in the medical domain for the diagnosis and classification of human infections. Several computerized techniques based on artificial intelligence (AI) have been introduced in the literature for gastrointestinal (GIT) diseases such as ulcer, bleeding, polyp, and a few others. Manual diagnosis of these infections is time consuming, expensive, and always requires an expert. As a result, computerized methods that can assist doctors as a second opinion in clinics are widely required. The key challenges of a computerized technique are accurate infected region segmentation because each infected region has a change of shape and location. Moreover, the inaccurate segmentation affects the accurate feature extraction that later impacts the classification accuracy. In this paper, we proposed an automated framework for GIT disease segmentation and classification based on deep saliency maps and Bayesian optimal deep learning feature selection. The proposed framework is made up of a few key steps, from preprocessing to classification. Original images are improved in the preprocessing step by employing a proposed contrast enhancement technique. In the following step, we proposed a deep saliency map for segmenting infected regions. The segmented regions are then used to train a pre-trained fine-tuned model called MobileNet-V2 using transfer learning. The fine-tuned model’s hyperparameters were initialized using Bayesian optimization (BO). The average pooling layer is then used to extract features. However, several redundant features are discovered during the analysis phase and must be removed. As a result, we proposed a hybrid whale optimization algorithm for selecting the best features. Finally, the selected features are classified using an extreme learning machine classifier. The experiment was carried out on three datasets: Kvasir 1, Kvasir 2, and CUI Wah. The proposed framework achieved accuracy of 98.20, 98.02, and 99.61% on these three datasets, respectively. When compared to other methods, the proposed framework shows an improvement in accuracy.
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Othman E, Mahmoud M, Dhahri H, Abdulkader H, Mahmood A, Ibrahim M. Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models. SENSORS (BASEL, SWITZERLAND) 2022; 22:5429. [PMID: 35891111 PMCID: PMC9322134 DOI: 10.3390/s22145429] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/02/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.
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Affiliation(s)
- Esam Othman
- Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia; (E.O.); (H.D.); (A.M.)
| | - Muhammad Mahmoud
- Department of Information Systems, Madina Higher Institute of Management and Technology, Shabramant 12947, Egypt;
| | - Habib Dhahri
- Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia; (E.O.); (H.D.); (A.M.)
| | - Hatem Abdulkader
- Department of Information Systems, Faculty of Computers and Information, Menoufia University, Shebin El-kom 32511, Menoufia, Egypt;
| | - Awais Mahmood
- Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia; (E.O.); (H.D.); (A.M.)
| | - Mina Ibrahim
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shebin El-kom 32511, Menoufia, Egypt
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