دورية أكاديمية
Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumes.
| العنوان: | Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumes. |
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| المؤلفون: | Huang YS; Oral and Maxillofacial Radiology, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA. Electronic address: yiingshiuanhuang@gmail.com., Iakubovskii P; Denti.AI Technology Inc., Toronto, Ontario, Canada., Lim LZ; Oral and Maxillofacial Radiology, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA; Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, National University of Singapore, Singapore., Mol A; Oral and Maxillofacial Radiology, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA., Tyndall DA; Oral and Maxillofacial Radiology, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA. |
| المصدر: | Oral surgery, oral medicine, oral pathology and oral radiology [Oral Surg Oral Med Oral Pathol Oral Radiol] 2024 Jul; Vol. 138 (1), pp. 173-183. Date of Electronic Publication: 2023 Oct 16. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: United States NLM ID: 101576782 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2212-4411 (Electronic) NLM ISO Abbreviation: Oral Surg Oral Med Oral Pathol Oral Radiol Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New York, NY : Elsevier |
| مواضيع طبية MeSH: | Cone-Beam Computed Tomography*/methods , Deep Learning* , Algorithms*, Humans ; Sensitivity and Specificity ; Radiographic Image Interpretation, Computer-Assisted ; Jaw Diseases/diagnostic imaging ; Software ; Predictive Value of Tests ; Jaw Neoplasms/diagnostic imaging |
| مستخلص: | Objective: The study aim was to develop and assess the performance of a deep learning (DL) algorithm in the detection of radiolucent intraosseous jaw lesions in cone beam computed tomography (CBCT) volumes. Study Design: A total of 290 CBCT volumes from more than 12 different scanners were acquired. Fields of view ranged from 6 × 6 × 6 cm to 18 × 18 × 16 cm. CBCT volumes contained either zero or at least one biopsy-confirmed intraosseous lesion. 80 volumes with no intraosseous lesions were included as controls and were not annotated. 210 volumes with intraosseous lesions were manually annotated using ITK-Snap 3.8.0. 150 volumes (10 control, 140 positive) were presented to the DL software for training. Validation was performed using 60 volumes (30 control, 30 positive). Testing was performed using the remaining 80 volumes (40 control, 40 positive). Results: The DL algorithm obtained an adjusted sensitivity by case, specificity by case, positive predictive value by case, and negative predictive value by case of 0.975, 0.825, 0.848, and 0.971, respectively. Conclusions: A DL algorithm showed moderate success at lesion detection in their correct locations, as well as recognition of lesion shape and extent. This study demonstrated the potential of DL methods for intraosseous lesion detection in CBCT volumes. (Copyright © 2023 Elsevier Inc. All rights reserved.) |
| تواريخ الأحداث: | Date Created: 20231228 Date Completed: 20240621 Latest Revision: 20240805 |
| رمز التحديث: | 20240806 |
| DOI: | 10.1016/j.oooo.2023.09.011 |
| PMID: | 38155015 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 2212-4411 |
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| DOI: | 10.1016/j.oooo.2023.09.011 |