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Amir Yari: Artificial Intelligence in Oral and maxillofacial surgery

Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence

Amir Yari, Paniz Fasih, Mohammad Hosseini Hooshiar, Ali Goodarzi, Seyedeh Farnaz Fattahi

This manuscript has been published by the Oxford University Press

Purpose

This study aimed to assess the performance of a deep learning algorithm (YOLOv5) in detecting different mandibular fracture types in panoramic images.

Methods

This study utilized a dataset of panoramic radiographic images with mandibular fractures. The dataset was divided into training, validation, and testing sets, with 60%, 20%, and 20% of the images, respectively. An equal number of control panoramic radiographs, which did not contain any fractures, were also randomly distributed among the three sets. The YOLOv5 deep learning model was trained to detect six fracture types in the mandible based on the anatomical location including symphysis, body, angle, ramus, condylar neck, and condylar head. Performance metrics of accuracy, precision, sensitivity (recall), specificity, dice coefficient (F1 score), and area under the curve (AUC) were calculated for each class.

Amir Yari: Artificial Intelligence in Oral and maxillofacial surgery
Amir Yari: Artificial Intelligence and deep learning in Oral and maxillofacial surgery

Results

A total of 498 panoramic images containing 673 fractures were collected. The accuracy was highest in detecting body (96.21%) and symphysis (95.87%), and was lowest in angle (90.51%) fractures. The highest and lowest precision values were observed in detecting symphysis (95.45%) and condylar head (63.16%) fractures, respectively. The sensitivity was highest in the body (96.67%) fractures and was lowest in the condylar head (80.00%) and condylar neck (81.25%) fractures. The highest specificity was noted in symphysis (98.96%), body (96.08%), and ramus (96.04%) fractures, respectively. The dice coefficient and AUC were highest in detecting body fractures (0.921 and 0.942, respectively), and were lowest in detecting condylar head fractures (0.706 and .812, respectively).

Conclusion

The trained algorithm achieved promising performance metrics for the automated detection of most fracture types, with the highest performance observed in detecting body and symphysis fractures. Machine learning can provide a potential tool for assisting clinicians in mandibular fracture diagnosis.

Link to the original research: https://academic.oup.com/dmfr/advance-article-abstract/doi/10.1093/dmfr/twae018/7656767

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