{"id":20713,"date":"2026-07-20T09:23:00","date_gmt":"2026-07-20T02:23:00","guid":{"rendered":"https:\/\/fkg.ugm.ac.id\/?p=20713"},"modified":"2026-07-20T09:23:03","modified_gmt":"2026-07-20T02:23:03","slug":"gamacomet-saat-kecerdasan-buatan-membaca-kerusakan-dna-dari-rongga-mulut-perokok","status":"publish","type":"post","link":"https:\/\/fkg.ugm.ac.id\/en\/gamacomet-saat-kecerdasan-buatan-membaca-kerusakan-dna-dari-rongga-mulut-perokok\/","title":{"rendered":"GamaComet: When Artificial Intelligence Reads DNA Damage in the Oral Cells of Smokers"},"content":{"rendered":"<p class=\"wp-block-paragraph\"><strong>Cells lining the inside of the mouth contain secrets that have long been difficult for machines to read. Not because the secrets are too complicated, but because images generated from laboratory tests of buccal mucosal cells are often too \u201cnoisy\u201d\u2014filled with artifacts, blurred features, and considerably more difficult to analyze than white blood cells, which are commonly used in DNA damage research.\nThis challenge prompted a multidisciplinary research team at Universitas Gadjah Mada to develop GamaComet, a deep learning-based tool capable of automatically detecting and classifying DNA damage from comet assay images of buccal mucosal cells.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph translation-block\">The results were published in the journal Diagnostics (MDPI) in August 2022, with one of the principal contributors from the Faculty of Dentistry, UGM: drg. Ryna Dwi Yanuaryska, Ph.D., a lecturer in the Department of Dentomaxillofacial Radiology.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Oral Cells, and Why Does It Matter?<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The comet assay, or single-cell gel electrophoresis (SCGE), is a long-established method for measuring DNA damage in individual cells. Conceptually, the method is simple: cells with damaged DNA form a comet-like pattern when subjected to an electric field in a gel. The comet head represents the cell nucleus, while the tail consists of fragmented DNA. The longer the tail, the more severe the damage.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Until now, blood lymphocytes have been the primary subjects for this type of testing. However, blood sampling is invasive and uncomfortable for patients. Buccal mucosal cells, which can be obtained simply by swabbing the inside of the cheek, offer a more patient-friendly alternative. These cells are also particularly relevant because they are located at the frontline of exposure to foreign substances from outside the body, including X-ray radiation from dental panoramic examinations, which drg. Ryna Dwi Yanuaryska, Ph.D., investigated in an earlier publication.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The problem is that comet assay images of buccal mucosal cells contain considerably more noise than images of lymphocytes. Existing software, such as OpenComet, was designed for cleaner cultured cells. When tested on buccal mucosal images, OpenComet achieved only 11.5% detection accuracy, with five times more false positives than the model proposed by the UGM research team.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How Does GamaComet Work?<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The researchers used the Faster R-CNN (Faster Region-Based Convolutional Neural Network) architecture, which was a state-of-the-art algorithm for object detection and classification at the time of the study.\nThe model was trained using 275 comet assay images collected from buccal swabs of 24 patients at Prof. Soedomo Dental Hospital, UGM, in 2018. The dataset contained 519 labeled comets divided into five damage classes, ranging from class 0 (lowest level of damage) to class 4 (highest level of damage).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The team faced two major challenges from the outset. First, the dataset was small: deep learning typically requires hundreds of thousands to millions of samples, whereas the researchers had only 275 images. Second, the class distribution was imbalanced, with only 19 class-4 comets compared with 197 class-1 comets.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To overcome these challenges, the team applied two strategies: transfer learning using a model pretrained on the COCO dataset (approximately 330,000 images of common objects), followed by fine-tuning with comet assay data; and data augmentation through image rotations of \u00b15 degrees. Rotation was selected because comet orientation affects the validity of the analysis, meaning that techniques such as image flipping could not be applied indiscriminately.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The results were encouraging. The best-performing model, Faster R-CNN with a ResNet50 backbone trained on the augmented dataset, achieved a 95.85% detection accuracy on the validation data.\nOn an independent test dataset collected from seven different patients under slightly different image-acquisition conditions, GamaComet still achieved 81.34% detection accuracy and 66.67% classification accuracy.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Smokers and Greater DNA Damage<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Behind these numbers lies a finding that speaks more directly to us as human beings. Of the 24 patients whose data were used to train the model, six were smokers. They were all patients numbered 19 through 24 in the dataset.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>\u201cPatients whose comet assay images contained class 3 and class 4 comets were found to be smokers, meaning that they had more cells with high levels of DNA damage.\u201d<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This finding is more than simply confirmation of what has long been suspected. It suggests that GamaComet could become a reliable downstream analytical tool: from a simple cheek swab, the model can identify patterns of genetic damage associated with patients\u2019 lifestyles.\nIt represents a bridge between dental clinics and molecular biology, built from data obtained from Indonesian patients.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>A Free Tool for Open Collaboration<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">GamaComet is now available free of charge for academic purposes through a web platform built using Django and Nginx. Users simply upload comet assay images, and the cloud-based server automatically performs detection and classification.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The study resulted from a collaboration between the Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, UGM; the Department of Dentomaxillofacial Radiology, Faculty of Dentistry, UGM; and the Nara Institute of Science and Technology in Japan.\nTwo ethical approvals were obtained: one from the Ethics Committee of the Faculty of Medicine, Public Health, and Nursing, UGM\/Dr. Sardjito General Hospital (2018), and another from the Ethics Committee of the Faculty of Dentistry, UGM (2016).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The researchers acknowledge that GamaComet\u2019s classification accuracy, which remains below 70%, still needs improvement. Future plans include expanding the dataset, exploring more diverse augmentation methods such as image mixing and adversarial training, and investigating meta-learning approaches.\nEvery cheek-swab image entered into the system ultimately represents one step closer to understanding what is truly happening at the cellular level\u2014long before any symptoms appear on the surface.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Source DOI: <a href=\"https:\/\/doi.org\/10.3390\/diagnostics12082002\">https:\/\/doi.org\/10.3390\/diagnostics12082002<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Authors: Anny Anggraini; drg. Achmad Zam Zam Aghasy, M.Kes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Photo: Pexels <\/p>","protected":false},"excerpt":{"rendered":"<p>Sel-sel di lapisan dalam mulut menyimpan rahasia yang selama ini sulit dibaca mesin. Bukan karena rahasianya terlalu rumit, melainkan karena gambar yang dihasilkan dari uji laboratorium sel mukosa bukal terlalu &#8220;berisik&#8221; \u2014 penuh artefak, kabur, dan jauh lebih sulit dianalisis dibanding sel darah putih yang biasa dipakai dalam penelitian kerusakan DNA. Tantangan itulah yang mendorong [&hellip;]<\/p>\n","protected":false},"author":615,"featured_media":20714,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[359,136],"tags":[298,307,289,297],"class_list":["post-20713","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artikel","category-berita-terbaru","tag-sdg-10-mengurangi-ketidaksetaraan","tag-sdg-17-kemitraan-untuk-mencapai-tujuan","tag-sdg-3-kesehatan-dan-kesejahteraan-yang-baik","tag-sdg-9-industri-inovasi-dan-infrastruktur"],"gutentor_comment":0,"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>GamaComet: Saat Kecerdasan Buatan Membaca Kerusakan DNA dari Rongga Mulut Perokok - Fakultas Kedokteran Gigi<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fkg.ugm.ac.id\/en\/gamacomet-saat-kecerdasan-buatan-membaca-kerusakan-dna-dari-rongga-mulut-perokok\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GamaComet: Saat Kecerdasan Buatan Membaca Kerusakan DNA dari Rongga Mulut Perokok - Fakultas Kedokteran Gigi\" \/>\n<meta property=\"og:description\" content=\"Sel-sel di lapisan dalam mulut menyimpan rahasia yang selama ini sulit dibaca mesin. 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