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All Journal Jurnal Respirologi Indonesia JIMKI: Jurnal Ilmiah Mahasiswa Kedokteran Indonesia
Duta, Teuku Fais
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Health Professions Medicine & Pharmacology Public Health

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Endoscope to Identify A Smoker's Oral Mucosa for Early Obstructive Airway Disease Detection Yanti, Budi; Muhamad, Zarfan Fawwaz; Duta, Teuku Fais; Maulana, Muhammad Iqbal; Irmayani, Irmayani; Ossa, Yuli Fatzia; Sherina, Sherina
Jurnal Respirologi Indonesia Vol 44, No 3 (2024)
Publisher : Perhimpunan Dokter Paru Indonesia (PDPI)/The Indonesian Society of Respirology (ISR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36497/jri.v44i3.640

Abstract

Background: The synergistic association between oral cavity disorders and airway disorders in smokers has long been recognized. Periodontal disease and airway obstruction are 20 times more likely in smokers. Smoking causes increased inflammatory cytokines in the oral mucosa; generally, airway obstruction has been associated with increased inflammatory markers in the airway mucosa. This study developed a prototype to visualize smokers' oral mucosa to identify potential airway obstruction disease.Methods: This study collected many types of oral mucosal lesions that are typically found in smokers, such as leukoplakia, nicotinic stomatitis, black hairy tongue, oral cancer, and smoker melanosis, from various literature and images of the mucosa of patients with a history of smoking who were treated at the hospital. The data is divided into a training, validation, and testing set and then using the PyTorch framework and the UltraLytics library.Results: This study created a prototype of an endoscope that can detect lesions on the oral mucosa-related airway obstruction disease. Sixty-three percent of the respondents who underwent prototype testing were between the ages of twenty-one and thirty. Of those who smoked, 86% had done so for five to ten years. Sixty percent of the respondents had no COPD diagnosis. The sensitivity of the prototype demonstrated a high rate of 84%. However, the specificity exhibited 57.14%.Conclusion: Endoscopic detection of the oral mucosa can be used for early screening of suspected obstructive airway disorders in smokers. This tool could enhance screening for smoking's effects on the mouth and prevent early obstructive airway diseases.
Endoscope to Identify A Smoker's Oral Mucosa for Early Obstructive Airway Disease Detection Yanti, Budi; Muhamad, Zarfan Fawwaz; Duta, Teuku Fais; Maulana, Muhammad Iqbal; Irmayani, Irmayani; Ossa, Yuli Fatzia; Sherina, Sherina
Jurnal Respirologi Indonesia Vol 44 No 3 (2024)
Publisher : Perhimpunan Dokter Paru Indonesia (PDPI)/The Indonesian Society of Respirology (ISR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36497/jri.v44i3.640

Abstract

Background: The synergistic association between oral cavity disorders and airway disorders in smokers has long been recognized. Periodontal disease and airway obstruction are 20 times more likely in smokers. Smoking causes increased inflammatory cytokines in the oral mucosa; generally, airway obstruction has been associated with increased inflammatory markers in the airway mucosa. This study developed a prototype to visualize smokers' oral mucosa to identify potential airway obstruction disease.Methods: This study collected many types of oral mucosal lesions that are typically found in smokers, such as leukoplakia, nicotinic stomatitis, black hairy tongue, oral cancer, and smoker melanosis, from various literature and images of the mucosa of patients with a history of smoking who were treated at the hospital. The data is divided into a training, validation, and testing set and then using the PyTorch framework and the UltraLytics library.Results: This study created a prototype of an endoscope that can detect lesions on the oral mucosa-related airway obstruction disease. Sixty-three percent of the respondents who underwent prototype testing were between the ages of twenty-one and thirty. Of those who smoked, 86% had done so for five to ten years. Sixty percent of the respondents had no COPD diagnosis. The sensitivity of the prototype demonstrated a high rate of 84%. However, the specificity exhibited 57.14%.Conclusion: Endoscopic detection of the oral mucosa can be used for early screening of suspected obstructive airway disorders in smokers. This tool could enhance screening for smoking's effects on the mouth and prevent early obstructive airway diseases.
Development of an Artificial Intelligence–Based Portable Prototype for Early Tuberculosis Detection Using Exhaled Breath Analysis and IoT Integration: A Feasibility Study Naufal, Muhammad Alif; Nusair, Rafie; Duta, Teuku Fais
JIMKI: Jurnal Ilmiah Mahasiswa Kedokteran Indonesia Book of Abstrack RCIMS 2025
Publisher : BAPIN-ISMKI (Badan Analisis Pengembangan Ilmiah Nasional - Ikatan Senat Mahasiswa Kedokteran Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53366/jimki.vi.978

Abstract

Introductions: Tuberculosis (TB) remains a major global health problem and has become one of the world’s leading infectious diseases, particularly affecting populations in low- and middle-income countries. Despite advancements in molecular testing, the accessibility, cost, and time requirements of conventional diagnostics limit early case detection. Exhaled breath analysis provides a promising non-invasive approach through the identification of volatile organic compounds (VOCs) produced during TB infection. This study aimed to develop and evaluate a portable diagnostic system that integrates VOC sensing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to enhance early TB screening in community and primary healthcare settings. Methods: A metal oxide semiconductor gas sensor array connected to an ESP32-S3 microcontroller was employed to capture VOC profiles from 33 participants (17 TB-confirmed patients and 16 healthy controls). The acquired data were preprocessed, reduced, and classified using Principal Component Analysis. Several machine learning algorithms, including Support Vector Machines (SVM), Random Forest, Gradient Boosting, and Artificial Neural Networks (ANN), were trained and validated to develop a TB recognition model. Results and Discussion: The ANN achieved the best performance, with an accuracy of 79%, sensitivity of 78%, specificity of 80%, and an AUC of 0.84. IoT integration enabled real-time data transfer and cloud-based visualization, demonstrating scalability and potential use in resource-limited settings. Conclusion: This portable AI-based breath analysis system offers a rapid, affordable, and non-invasive approach for early TB detection. With further validation, it might complement existing diagnostics and strengthen global TB elimination efforts.
Co-Authors Irmayani Irmayani Maulana, Muhammad Iqbal Muhamad, Zarfan Fawwaz Naufal, Muhammad Alif Nusair, Rafie Ossa, Yuli Fatzia Sherina Sherina, Sherina Yanti, Budi