The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography
Background: Panoramic radiography is a standard diagnostic imaging method for dentists. However, it is challenging to detect mandibular trauma and fractures in panoramic radiographs due to the superimposed facial skeleton structures. The objective of this study was to develop a deep learning algorit...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2023-01-01
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| Series: | Dental Research Journal |
| Subjects: | |
| Online Access: | http://www.drjjournal.net/article.asp?issn=1735-3327;year=2023;volume=20;issue=1;spage=27;epage=27;aulast=Shahnavazi |
| _version_ | 1797866718692900864 |
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| author | Maryam Shahnavazi Hosein Mohamadrahimi |
| author_facet | Maryam Shahnavazi Hosein Mohamadrahimi |
| author_sort | Maryam Shahnavazi |
| collection | DOAJ |
| description | Background: Panoramic radiography is a standard diagnostic imaging method for dentists. However, it is challenging to detect mandibular trauma and fractures in panoramic radiographs due to the superimposed facial skeleton structures. The objective of this study was to develop a deep learning algorithm that is capable of detecting mandibular fractures and trauma automatically and compare its performance with general dentists.
Materials and Methods: This is a retrospective diagnostic test accuracy study. This study used a two-stage deep learning framework. To train the model, 190 panoramic images were collected from four different sources. The mandible was first segmented using a U-net model. Then, to detect fractures, a model named Faster region-based convolutional neural network was applied. In the end, a comparison was made between the accuracy, specificity, and sensitivity of artificial intelligence and general dentists in trauma diagnosis.
Results: The mAP50 and mAP75 for object detection were 98.66% and 57.90%, respectively. The classification accuracy of the model was 91.67%. The sensitivity and specificity of the model were 100% and 83.33%, respectively. On the other hand, human-level diagnostic accuracy, sensitivity, and specificity were 87.22 ± 8.91, 82.22 ± 16.39, and 92.22 ± 6.33, respectively.
Conclusion: Our framework can provide a level of performance better than general dentists when it comes to diagnosing trauma or fractures. |
| first_indexed | 2024-04-09T23:28:44Z |
| format | Article |
| id | doaj.art-94a70c49e6b1493ebe442f2bd3c93ef8 |
| institution | Directory Open Access Journal |
| issn | 1735-3327 2008-0255 |
| language | English |
| last_indexed | 2024-04-09T23:28:44Z |
| publishDate | 2023-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Dental Research Journal |
| spelling | doaj.art-94a70c49e6b1493ebe442f2bd3c93ef82023-03-21T07:29:01ZengWolters Kluwer Medknow PublicationsDental Research Journal1735-33272008-02552023-01-01201272710.4103/1735-3327.369629The application of artificial neural networks in the detection of mandibular fractures using panoramic radiographyMaryam ShahnavaziHosein MohamadrahimiBackground: Panoramic radiography is a standard diagnostic imaging method for dentists. However, it is challenging to detect mandibular trauma and fractures in panoramic radiographs due to the superimposed facial skeleton structures. The objective of this study was to develop a deep learning algorithm that is capable of detecting mandibular fractures and trauma automatically and compare its performance with general dentists. Materials and Methods: This is a retrospective diagnostic test accuracy study. This study used a two-stage deep learning framework. To train the model, 190 panoramic images were collected from four different sources. The mandible was first segmented using a U-net model. Then, to detect fractures, a model named Faster region-based convolutional neural network was applied. In the end, a comparison was made between the accuracy, specificity, and sensitivity of artificial intelligence and general dentists in trauma diagnosis. Results: The mAP50 and mAP75 for object detection were 98.66% and 57.90%, respectively. The classification accuracy of the model was 91.67%. The sensitivity and specificity of the model were 100% and 83.33%, respectively. On the other hand, human-level diagnostic accuracy, sensitivity, and specificity were 87.22 ± 8.91, 82.22 ± 16.39, and 92.22 ± 6.33, respectively. Conclusion: Our framework can provide a level of performance better than general dentists when it comes to diagnosing trauma or fractures.http://www.drjjournal.net/article.asp?issn=1735-3327;year=2023;volume=20;issue=1;spage=27;epage=27;aulast=Shahnavazideep learningdental radiographymandibular fracturespanoramic radiography |
| spellingShingle | Maryam Shahnavazi Hosein Mohamadrahimi The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography Dental Research Journal deep learning dental radiography mandibular fractures panoramic radiography |
| title | The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography |
| title_full | The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography |
| title_fullStr | The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography |
| title_full_unstemmed | The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography |
| title_short | The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography |
| title_sort | application of artificial neural networks in the detection of mandibular fractures using panoramic radiography |
| topic | deep learning dental radiography mandibular fractures panoramic radiography |
| url | http://www.drjjournal.net/article.asp?issn=1735-3327;year=2023;volume=20;issue=1;spage=27;epage=27;aulast=Shahnavazi |
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