{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:21:52Z","timestamp":1793319712802,"version":"4.5.0"},"reference-count":25,"publisher":"MJS Publishing, Medical Journals Sweden AB","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AOS"],"abstract":"<jats:p>Objectives: Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identification of approximal caries in pediatric patients aged 5\u201312 years.\nMaterials and methods: Pediatric patients\u2019 digital periapical radiographic images were collected to create a unique dataset. Various augmentation methods were used, and approximal caries in the augmented images were labeled by a pediatric dentist to minimize labeling errors. The dataset consisted of 830 data labeled for approximal caries on 415 images, which were divided into 80% training and 20% testing sets. After comparing 13 detection algorithms, including the latest YOLOv8, the most appropriate one was selected for the proposed system, which was then evaluated based on various performance metrics.\nResults: The proposed detection system achieved a precision of 91.2%, an accuracy of 90.8%, a recall of 89.3%, and an F1 value of 90.24% after 300 iterations, utilizing a learning rate of 0.01.\nConclusion: Approximal caries has been successfully detected with the developed system. Future efforts will focus on augmenting the dataset and expanding the sample size to enhance the efficacy of the system.<\/jats:p>","DOI":"10.2340\/aos.v84.42599","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T16:55:05Z","timestamp":1736182505000},"page":"18-25","source":"Crossref","is-referenced-by-count":12,"title":["Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study"],"prefix":"10.2340","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1275-0258","authenticated-orcid":false,"given":"Zeynep Seyda","family":"Yavsan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8760-914X","authenticated-orcid":false,"given":"Hediye","family":"Orhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6136-6140","authenticated-orcid":false,"given":"Enes","family":"Efe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9521-4500","authenticated-orcid":false,"given":"Emrehan","family":"Yavsan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1183","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"key":"93012","doi-asserted-by":"crossref","unstructured":"Featherstone JDB. 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