{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T23:07:16Z","timestamp":1787785636296,"version":"build-2784847793"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Natural Science Foundation of China","award":["L232029"],"award-info":[{"award-number":["L232029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Objectives: The differential diagnosis of ameloblastoma and odontogenic keratocyst is essential for surgical planning and patient counseling. While deep learning (DL)-based methods show promising potential in this domain, their clinical translation remains challenging due to insufficient interpretability. This study aims to introduce segmentation-guided preprocessing approaches to provide support for the clinical implementation of computer-aided diagnosis systems. Methods: This study evaluated the performance of an InceptionV3 model on 128 pathologically confirmed CBCT scans (AME: 64; OKC: 64) by 5-fold cross-validation. Four experimental inputs were compared: (1) Original slice; (2) Bounding-box ROI; (3) Precise segmentation ROI; and (4) Moderately expanded ROI. All models were trained under the same settings. Assessment was conducted on both the slice and patient levels, incorporating accuracy, recall, precision, F1-score, and the area under the receiver operating characteristic curve (AUC). Grad-CAM visualization and confidence curve analysis were employed to verify models\u2019 attention patterns and diagnostic confidence. Results: All models based on segmentation-guided ROI significantly outperformed models based on original slice. The moderately expanded ROI achieved optimal performance. The bounding-box ROI provided competitive performance with higher recall. Grad-CAM confirmed improved attention localization, while confidence curve analysis showed more consistent and reliable prediction patterns across slices. Conclusions: Segmentation-guided preprocessing represents an effective and clinically relevant approach for jaw lesion diagnosis and enhances interpretability.<\/jats:p>","DOI":"10.3390\/diagnostics16030416","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T12:49:44Z","timestamp":1770036584000},"page":"416","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Segmentation-Guided Preprocessing Improves Deep Learning Diagnostic Accuracy and Confidence of Ameloblastoma and Odontogenic Keratocyst in Cone Beam CT Images\u2014A Preliminary Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3205-9752","authenticated-orcid":false,"given":"Xinyue","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, No. 22, Zhongguancun South Avenue, Haidian District, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jupeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4642-2033","authenticated-orcid":false,"given":"Gang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, No. 22, Zhongguancun South Avenue, Haidian District, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20190107","DOI":"10.1259\/dmfr.20190107","article-title":"The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review","volume":"49","author":"Hung","year":"2020","journal-title":"Dentomaxillofac. 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