{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:26:24Z","timestamp":1780316784261,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council (NSTC), Taiwan","award":["NSTC-112-2410-H-033-014"],"award-info":[{"award-number":["NSTC-112-2410-H-033-014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis delays treatment and may be accompanied by toothache. Therefore, using artificial intelligence to assist dentists in accurately diagnosing odontogenic sinusitis is crucial. This study introduces an innovative odontogenic sinusitis image processing technique, which is fused with common contrast limited adaptive histogram equalization, Min-Max normalization, and the RGB mapping method. Moreover, this study combined various deep learning models to enhance diagnostic accuracy. The YOLO 11n model was used to detect odontogenic sinusitis single tooth position in dental panoramic radiographs and achieved an accuracy of 98.2%. The YOLOv8n-cls model diagnosed odontogenic sinusitis with a final classification accuracy of 96.1%, achieving a 16.9% improvement over non-enhanced methods and outperforming recent studies by at least 4%. Additionally, in clinical applications, the classification accuracy for non-odontogenic sinusitis was 95.8%, while for odontogenic sinusitis it was 97.6%. The detection method developed in this study effectively reduces the radiation dose patients receive during CT imaging and serves as an auxiliary system, providing dentists with reliable support for the precise diagnosis of odontogenic sinusitis.<\/jats:p>","DOI":"10.3390\/bioengineering12020134","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T11:42:37Z","timestamp":1738237357000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs"],"prefix":"10.3390","volume":"12","author":[{"given":"Pei-Yi","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Periodontics, Division of Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333034, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2429-4143","authenticated-orcid":false,"given":"Yuan-Jin","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0550-9063","authenticated-orcid":false,"given":"Yu-Jen","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Tsun","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7605-5214","authenticated-orcid":false,"given":"Chiung-An","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0110-5491","authenticated-orcid":false,"given":"Kuo-Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Information Management, Chung Yuan Christian University, Taoyuan City 32023, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1134-5859","authenticated-orcid":false,"given":"Wei-Chen","family":"Tu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Cheng Kung University, Tainan City 701401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8848-6644","authenticated-orcid":false,"given":"Patricia Angela R.","family":"Abu","sequence":"additional","affiliation":[{"name":"Ateneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","article-title":"A deep convolutional neural network model to classify heartbeats","volume":"89","author":"Acharya","year":"2017","journal-title":"Comput. 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