Journal of endodontics · 2026
Automated Detection of Middle Mesial Canals in Mandibular Molars on CBCT using nnU-Net: A Retrospective Diagnostic Accuracy Study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Turp Şule Sezin, Er Kürşat, Çakmak Yunus Emre, Varol Nihatcan, Yilmaz Sevcihan Günen
Аннотация
BACKGROUND: This study aimed to develop and validate a fully three-dimensional (3D) convolutional neural network for automated detection of the mid-mesial canal (MMC) in mandibular molars on cone-beam computed tomography (CBCT).
METHODS: In this retrospective diagnostic accuracy study, 248 CBCT volumes were used for model development, with an independent test set of 28 cases (1.611 axial slices). Ground-truth MMC status was defined by endodontist-radiologist consensus; multiclass manual masks (dentin, mesiobuccal-, mesiolingual-, and mid-mesial-canal) were created in 3D Slicer. A 3D nnU-Net-based model was trained using canal-focused patch sampling and a combined Dice plus weighted cross-entropy loss. MMC detection was evaluated at slice and case levels using sensitivity, specificity, accuracy, positive predictive value/negative predictive value, and Cohen's κ; paired comparisons used McNemar's test.
RESULTS: Interobserver agreement for slice-level MMC labeling was excellent (κ = 0.892). For case-level MMC detection using a prespecified threshold (≥5 AI-positive slices), performance was sensitivity 94.7%, specificity 100%, accuracy 96.4%, and κ = 0.920. At slice level, agreement remained high versus individual observers (κ ≈ 0.80) and consensus references (κ = 0.812 for OR; κ = 0.792 for AND). No significant difference was observed between AI and the OR-consensus reference at slice level, nor between AI and the case-level ground truth.
CONCLUSIONS: A fully 3D nnU-Net framework achieved clinician-level MMC detection on CBCT and may support standardized, efficient identification of this frequently overlooked anatomical variation.
METHODS: In this retrospective diagnostic accuracy study, 248 CBCT volumes were used for model development, with an independent test set of 28 cases (1.611 axial slices). Ground-truth MMC status was defined by endodontist-radiologist consensus; multiclass manual masks (dentin, mesiobuccal-, mesiolingual-, and mid-mesial-canal) were created in 3D Slicer. A 3D nnU-Net-based model was trained using canal-focused patch sampling and a combined Dice plus weighted cross-entropy loss. MMC detection was evaluated at slice and case levels using sensitivity, specificity, accuracy, positive predictive value/negative predictive value, and Cohen's κ; paired comparisons used McNemar's test.
RESULTS: Interobserver agreement for slice-level MMC labeling was excellent (κ = 0.892). For case-level MMC detection using a prespecified threshold (≥5 AI-positive slices), performance was sensitivity 94.7%, specificity 100%, accuracy 96.4%, and κ = 0.920. At slice level, agreement remained high versus individual observers (κ ≈ 0.80) and consensus references (κ = 0.812 for OR; κ = 0.792 for AND). No significant difference was observed between AI and the OR-consensus reference at slice level, nor between AI and the case-level ground truth.
CONCLUSIONS: A fully 3D nnU-Net framework achieved clinician-level MMC detection on CBCT and may support standardized, efficient identification of this frequently overlooked anatomical variation.