ArticleClinical and experimental dental research2025
Assessment of Deep Convolutional Neural Network Models for the Classification of Benign Fibro-Osseous Lesions of the Jaws.
Article in Clinical and experimental dental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Florid Cemento-Osseous Dysplasia and Simple Bone Cyst Association: Co-Occurrence or Evolution of the Disease? Two Case Reports and Literature Review.Clinical and experimental dental research · 2026Review
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Authors and funding
6 authors.
Funding
Abstract
objectivesBenign fibro-osseous lesions (BFOL) constitute a group of pathologic entities with marked overlapping histopathologic features but are diverse in nature and clinical behaviors. Accurate diagnoses of BFOLs necessitate clinical-pathological correlations, which are paramount for their appropriate management. Recent research indicates the potential utility of artificial intelligence in diagnostic pathology. Here, we aimed to assess the performance of the deep convolutional neural network (DCNN) models for BFOL classification and investigate its impact on the diagnostic performance of oral pathologists. MATERIAL AND
methodsMicroscopic slides from 68 patients diagnosed with cemento-ossifying fibroma (COF), fibrous dysplasia (FD), and cemento-osseous dysplasia (COD) were collected. The image patches from each slide were processed, augmented, and used to train and validate the five pre-trained DCNN models for BFOL classification. The best-performing model was selected to evaluate its diagnostic performance on the testing data set, compared with experienced oral pathologists.
resultsThe InceptionV3 model showed the highest and most balanced overall performance in BFOL classification. It demonstrated the highest accuracy (96.7%) in classifying COF, followed by COD (83.3%), and FD (80.0%), respectively. The model accuracy in identifying COF was greater than the average performance of pathologists (90.0%). However, pathologists performed better in classifying COD (87.2%) and FD (95.0%). With DCNN assistance, pathologists significantly improved the accuracy, sensitivity, and specificity in distinguishing BFOLs while reducing the average diagnosis time.
conclusionsThe DCNN model has the potential to be developed as an auxiliary tool, assisting pathologists in diagnosing BFOLs. Through ongoing refinements, artificial intelligence assistance can aid pathologists in enhancing the accuracy and efficiency of BFOL diagnosis.
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