Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O’Leary Index
<b>Background</b>: Oral diseases such as caries, gingivitis, and periodontitis are highly prevalent worldwide and often arise from plaque. This study focuses on detecting three plaque stages—new, mature, and over-mature—using state-of-the-art YOLO architectures to enhance early intervent...
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MDPI AG
2025-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/2/231 |
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| author | Alfonso Ramírez-Pedraza Sebastián Salazar-Colores Crystel Cardenas-Valle Juan Terven José-Joel González-Barbosa Francisco-Javier Ornelas-Rodriguez Juan-Bautista Hurtado-Ramos Raymundo Ramirez-Pedraza Diana-Margarita Córdova-Esparza Julio-Alejandro Romero-González |
| author_facet | Alfonso Ramírez-Pedraza Sebastián Salazar-Colores Crystel Cardenas-Valle Juan Terven José-Joel González-Barbosa Francisco-Javier Ornelas-Rodriguez Juan-Bautista Hurtado-Ramos Raymundo Ramirez-Pedraza Diana-Margarita Córdova-Esparza Julio-Alejandro Romero-González |
| author_sort | Alfonso Ramírez-Pedraza |
| collection | DOAJ |
| description | <b>Background</b>: Oral diseases such as caries, gingivitis, and periodontitis are highly prevalent worldwide and often arise from plaque. This study focuses on detecting three plaque stages—new, mature, and over-mature—using state-of-the-art YOLO architectures to enhance early intervention and reduce reliance on manual visual assessments. <b>Methods</b>: We compiled a dataset of 531 RGB images from 177 individuals, captured via multiple mobile devices. Each sample was treated with disclosing gel to highlight plaque types, then preprocessed for lighting and color normalization. YOLOv9, YOLOv10, and YOLOv11, in various scales, were trained to detect plaque categories, and their performance was evaluated using precision, recall, and mean Average Precision (mAP@50). <b>Results</b>: Among the tested models, YOLOv11m achieved the highest mAP@50 (0.713), displaying superior detection of over-mature plaque. Across all YOLO variants, older plaque was generally easier to detect than newer plaque, which can blend with gingival tissue. Applying the O’Leary index indicated that over half of the study population exhibited severe plaque levels. <b>Conclusions</b>: Our findings demonstrate the feasibility of automated plaque detection with advanced YOLO models in varied imaging conditions. This approach offers potential to optimize clinical workflows, support early diagnoses, and mitigate oral health burdens in low-resource communities. |
| format | Article |
| id | doaj-art-22f8c8be31f84413af9333e42d1841fb |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-22f8c8be31f84413af9333e42d1841fb2025-01-24T13:29:12ZengMDPI AGDiagnostics2075-44182025-01-0115223110.3390/diagnostics15020231Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O’Leary IndexAlfonso Ramírez-Pedraza0Sebastián Salazar-Colores1Crystel Cardenas-Valle2Juan Terven3José-Joel González-Barbosa4Francisco-Javier Ornelas-Rodriguez5Juan-Bautista Hurtado-Ramos6Raymundo Ramirez-Pedraza7Diana-Margarita Córdova-Esparza8Julio-Alejandro Romero-González9Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, MexicoIA, Centro de Investigaciones en Óptica A.C., Loma del Bosque 115, León 37150, MexicoClinica Lurviva, I. Allende, Apaseo el Grande 38160, MexicoCentro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, MexicoCentro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, MexicoCentro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, MexicoCentro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, MexicoFacultad de Contaduria y Administración, Universidad Autónoma de Querétaro, Querétaro 76017, MexicoFacultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, MexicoFacultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, Mexico<b>Background</b>: Oral diseases such as caries, gingivitis, and periodontitis are highly prevalent worldwide and often arise from plaque. This study focuses on detecting three plaque stages—new, mature, and over-mature—using state-of-the-art YOLO architectures to enhance early intervention and reduce reliance on manual visual assessments. <b>Methods</b>: We compiled a dataset of 531 RGB images from 177 individuals, captured via multiple mobile devices. Each sample was treated with disclosing gel to highlight plaque types, then preprocessed for lighting and color normalization. YOLOv9, YOLOv10, and YOLOv11, in various scales, were trained to detect plaque categories, and their performance was evaluated using precision, recall, and mean Average Precision (mAP@50). <b>Results</b>: Among the tested models, YOLOv11m achieved the highest mAP@50 (0.713), displaying superior detection of over-mature plaque. Across all YOLO variants, older plaque was generally easier to detect than newer plaque, which can blend with gingival tissue. Applying the O’Leary index indicated that over half of the study population exhibited severe plaque levels. <b>Conclusions</b>: Our findings demonstrate the feasibility of automated plaque detection with advanced YOLO models in varied imaging conditions. This approach offers potential to optimize clinical workflows, support early diagnoses, and mitigate oral health burdens in low-resource communities.https://www.mdpi.com/2075-4418/15/2/231deep learningmodelplaquedentobacteriaO’Leary index |
| spellingShingle | Alfonso Ramírez-Pedraza Sebastián Salazar-Colores Crystel Cardenas-Valle Juan Terven José-Joel González-Barbosa Francisco-Javier Ornelas-Rodriguez Juan-Bautista Hurtado-Ramos Raymundo Ramirez-Pedraza Diana-Margarita Córdova-Esparza Julio-Alejandro Romero-González Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O’Leary Index Diagnostics deep learning model plaque dentobacteria O’Leary index |
| title | Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O’Leary Index |
| title_full | Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O’Leary Index |
| title_fullStr | Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O’Leary Index |
| title_full_unstemmed | Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O’Leary Index |
| title_short | Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O’Leary Index |
| title_sort | deep learning in oral hygiene automated dental plaque detection via yolo frameworks and quantification using the o leary index |
| topic | deep learning model plaque dentobacteria O’Leary index |
| url | https://www.mdpi.com/2075-4418/15/2/231 |
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