Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance
Mandibular fractures are the most common fractures in dentistry. Since diagnosing a mandibular fracture is difficult when only panoramic radiographic images are used, most doctors use cone beam computed tomography (CBCT) to identify the patient’s fracture location. In this study, considering the dia...
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MDPI AG
2022-10-01
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| author | Dong-Min Son Yeong-Ah Yoon Hyuk-Ju Kwon Sung-Hak Lee |
| author_facet | Dong-Min Son Yeong-Ah Yoon Hyuk-Ju Kwon Sung-Hak Lee |
| author_sort | Dong-Min Son |
| collection | DOAJ |
| description | Mandibular fractures are the most common fractures in dentistry. Since diagnosing a mandibular fracture is difficult when only panoramic radiographic images are used, most doctors use cone beam computed tomography (CBCT) to identify the patient’s fracture location. In this study, considering the diagnosis of mandibular fractures using the combined deep learning technique, YOLO and U-Net were used as auxiliary diagnostic methods to detect the location of mandibular fractures based on panoramic images without CBCT. In a previous study, mandibular fracture diagnosis was performed using YOLO learning; in the detection performance result of the YOLOv4-based mandibular fracture diagnosis module, the precision score was approximately 97%, indicating that there was almost no misdiagnosis. In particular, fractures in the symphysis, body, angle, and ramus tend to be distributed in the middle of the mandible. Owing to the irregular fracture types and overlapping location information, the recall score was approximately 79%, which increased the detection of undiagnosed fractures. In many cases, fractures that are clearly visible to the human eye cannot be grasped. To overcome these shortcomings, the number of undiagnosed fractures can be reduced using a combination of the U-Net and YOLOv4 learning modules. U-Net is advantageous for the segmentation of fractures spread over a wide area because it performs semantic segmentation. Consequently, the undiagnosed case in the middle of the mandible, where YOLO was weak, was somewhat supplemented by the U-Net module. The precision score of the combined module was 95%, similar to that of the previous method, and the recall score improved to 87%, as the number of undiagnosed cases was reduced. Through this study, the performance of a deep learning method that can be used for the diagnosis of the mandibular bone has been improved, and it is anticipated that as an auxiliary diagnostic inspection device, it will assist dentists in making diagnoses. |
| first_indexed | 2024-03-09T18:55:55Z |
| format | Article |
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| last_indexed | 2024-03-09T18:55:55Z |
| publishDate | 2022-10-01 |
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| spelling | doaj.art-966bcf7090394d208c28b84ed69e1af72023-11-24T05:29:42ZengMDPI AGLife2075-17292022-10-011211171110.3390/life12111711Combined Deep Learning Techniques for Mandibular Fracture Diagnosis AssistanceDong-Min Son0Yeong-Ah Yoon1Hyuk-Ju Kwon2Sung-Hak Lee3School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehakro, Buk-gu, Daegu 41566, KoreaSchool of Dentistry, Kyungpook National University, 2177 Dalgubeol-daero, Jung-gu, Daegu 41940, KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehakro, Buk-gu, Daegu 41566, KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehakro, Buk-gu, Daegu 41566, KoreaMandibular fractures are the most common fractures in dentistry. Since diagnosing a mandibular fracture is difficult when only panoramic radiographic images are used, most doctors use cone beam computed tomography (CBCT) to identify the patient’s fracture location. In this study, considering the diagnosis of mandibular fractures using the combined deep learning technique, YOLO and U-Net were used as auxiliary diagnostic methods to detect the location of mandibular fractures based on panoramic images without CBCT. In a previous study, mandibular fracture diagnosis was performed using YOLO learning; in the detection performance result of the YOLOv4-based mandibular fracture diagnosis module, the precision score was approximately 97%, indicating that there was almost no misdiagnosis. In particular, fractures in the symphysis, body, angle, and ramus tend to be distributed in the middle of the mandible. Owing to the irregular fracture types and overlapping location information, the recall score was approximately 79%, which increased the detection of undiagnosed fractures. In many cases, fractures that are clearly visible to the human eye cannot be grasped. To overcome these shortcomings, the number of undiagnosed fractures can be reduced using a combination of the U-Net and YOLOv4 learning modules. U-Net is advantageous for the segmentation of fractures spread over a wide area because it performs semantic segmentation. Consequently, the undiagnosed case in the middle of the mandible, where YOLO was weak, was somewhat supplemented by the U-Net module. The precision score of the combined module was 95%, similar to that of the previous method, and the recall score improved to 87%, as the number of undiagnosed cases was reduced. Through this study, the performance of a deep learning method that can be used for the diagnosis of the mandibular bone has been improved, and it is anticipated that as an auxiliary diagnostic inspection device, it will assist dentists in making diagnoses.https://www.mdpi.com/2075-1729/12/11/1711mandibular fracturepanoramic radiographyimage processingobject detectiondeep learningYOLO |
| spellingShingle | Dong-Min Son Yeong-Ah Yoon Hyuk-Ju Kwon Sung-Hak Lee Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance Life mandibular fracture panoramic radiography image processing object detection deep learning YOLO |
| title | Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance |
| title_full | Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance |
| title_fullStr | Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance |
| title_full_unstemmed | Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance |
| title_short | Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance |
| title_sort | combined deep learning techniques for mandibular fracture diagnosis assistance |
| topic | mandibular fracture panoramic radiography image processing object detection deep learning YOLO |
| url | https://www.mdpi.com/2075-1729/12/11/1711 |
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