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Performance and clinical applicability of AI models for jawbone lesion classification: a systematic review with meta-analysis and introduction of a clinical interpretation score.

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第一作者： Jonas,Ver Berne

第一单位： OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Leuven 3000, Belgium.;Department of Pathology, University Hospitals Leuven, Leuven 3000, Belgium.

作者： Jonas,Ver Berne ^[1] ; Minh,Ton That ^[2] ; Reinhilde,Jacobs ^[3]

作者单位： OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Leuven 3000, Belgium.;Department of Pathology, University Hospitals Leuven, Leuven 3000, Belgium. ^[1] Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven 3000, Belgium. ^[2] OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Leuven 3000, Belgium.;Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven 3000, Belgium.;Department of Dentistry, Karolinska Institutet, Huddinge 141 52, Sweden. ^[3]

关键词 ameloblastoma artificial intelligence classification deep learning jawbone machine learning odontogenic keratocyst

医学主题词人类(Humans);锥束计算机体层摄影术(Cone-Beam Computed Tomography);放射摄影术, 全景(Radiography, Panoramic);人工智能(Artificial Intelligence);颌肿瘤(Jaw Neoplasms);颌囊肿(Jaw Cysts);敏感性与特异性(Sensitivity and Specificity)

DOI 10.1093/dmfr/twaf086

PMID 41345996

发布时间 2026-02-09

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