# Assessment of Deep Convolutional Neural Network Models for the Classification of Benign Fibro‐Osseous Lesions of the Jaws

## TL;DR

This study evaluates deep learning models for classifying benign jaw lesions and finds that AI can improve diagnostic accuracy and speed when used alongside pathologists.

## Contribution

The study introduces a deep learning approach to classify benign fibro-osseous lesions and demonstrates its potential as an auxiliary diagnostic tool for pathologists.

## Key findings

- The InceptionV3 model achieved 96.7% accuracy in classifying cemento-ossifying fibroma.
- Pathologists improved diagnostic performance when assisted by the DCNN model.
- AI assistance reduced average diagnosis time while increasing accuracy and specificity.

## Abstract

Benign 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.

Microscopic 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.

The 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.

The 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.