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We develop an AI-based algorithm (AI-Plaque) to automatically segment dental plaque areas in intraoral images, enabling automatic plaque detection and quantitative severity assessment. To mitigate the limited segmentation capabilities of existing models and dataset limitations, we introduce a data preprocessing procedure, a specialized fine-tuning approach, and a novel self-training pipeline that leverages synthesized plaque images. Our approach demonstrates significant improvements in plaque segmentation and high effectiveness in plaque assessment, over several baseline methods, as evaluated by both image segmentation metrics and a custom-designed quantitative Visual Plaque Index. The AI-Plaque algorithm could empower individuals to receive timely, personalized feedback on their oral hygiene practices for dental plaque control, such as brushing and flossing, and ultimately reduce the risk of developing oral diseases.<\/jats:p>","DOI":"10.1145\/3788680","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:37:56Z","timestamp":1768833476000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Detection and Quantitative Assessment of Dental Plaque in Intraoral Images"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7870-1985","authenticated-orcid":false,"given":"Ziyun","family":"Zeng","sequence":"first","affiliation":[{"name":"University of Rochester, Rochester, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-6045","authenticated-orcid":false,"given":"Junyu","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7604-4227","authenticated-orcid":false,"given":"Noha","family":"Rashwan","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center, Rochester, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5990-8123","authenticated-orcid":false,"given":"Nisreen Al","family":"Jallad","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center, Rochester, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8776-2520","authenticated-orcid":false,"given":"Jin","family":"Xiao","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center, Rochester, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4516-9729","authenticated-orcid":false,"given":"Jiebo","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2023. 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