Performance Evaluation of Deep Learning Models for Dental Caries Classification via Panoramic Radiograph Images

Omid Mırzaeı; Bülent Bilgehan; Mohamad Abduljalil; Mhammad Saleh; Ammar Kayssoun; Ahmet İlhan

Makale detayları

Performance Evaluation of Deep Learning Models for Dental Caries Classification via Panoramic Radiograph Images

Yazarlar:

Omid Mırzaeı | Bülent Bilgehan | Mohamad Abduljalil | Mhammad Saleh | Ammar Kayssoun | Ahmet İlhan

Yayıncı:

Marmara University

Ana Başlıklar:

Endodontics | Dentistry (Other)

Dergi:

Clinical and Experimental Health Sciences Vol.14 Issue 4, pp.868-875

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329779

pISSN:

2459-1459

Anahtar kelimeler:

Özet:

 Objective: The purpose of this study is to evaluate the ability of deep learning models to classify mandibular molar teeth according to the presence and proximity of caries to the dental pulp. This research summarizes the progress of artificial intelligence and potential dental problems in diagnosis, treatment, and disease prediction in medicine. It discusses data limitations, computational power, ethical considerations, and their implications for dentists. This can lay the groundwork for future research in this rapidly expanding field. Methods: The dataset used in this study consists of 1200 panoramic radiographs, which have been evaluated and classified into three categories: free of dental caries, coded as H; enamel-dentin caries lesions treated with restorative filling, coded as R; and deep dental caries that underwent root canal treatment, coded as E. The images are prepared for the training-testing process using the k-fold crossevaluation technique and then fed into the pre-trained deep learning models for classification. Results: The VGG-19 model achieved superior results compared to the other models, with macro-average scores of 0.9111 for precision, 0.9127 for recall, and 0.9115 for f1-score, respectively. Conclusion: The promising results obtained in this study give confidence in endorsing the use of deep learning models in the dental treatments sector.

 Objective: The purpose of this study is to evaluate the ability of deep learning models to classify mandibular molar teeth according to the presence and proximity of caries to the dental pulp. This research summarizes the progress of artificial intelligence and potential dental problems in diagnosis, treatment, and disease prediction in medicine. It discusses data limitations, computational power, ethical considerations, and their implications for dentists. This can lay the groundwork for future research in this rapidly expanding field. Methods: The dataset used in this study consists of 1200 panoramic radiographs, which have been evaluated and classified into three categories: free of dental caries, coded as H; enamel-dentin caries lesions treated with restorative filling, coded as R; and deep dental caries that underwent root canal treatment, coded as E. The images are prepared for the training-testing process using the k-fold crossevaluation technique and then fed into the pre-trained deep learning models for classification. Results: The VGG-19 model achieved superior results compared to the other models, with macro-average scores of 0.9111 for precision, 0.9127 for recall, and 0.9115 for f1-score, respectively. Conclusion: The promising results obtained in this study give confidence in endorsing the use of deep learning models in the dental treatments sector.

 Objective: The purpose of this study is to evaluate the ability of deep learning models to classify mandibular molar teeth according to the presence and proximity of caries to the dental pulp. This research summarizes the progress of artificial intelligence and potential dental problems in diagnosis, treatment, and disease prediction in medicine. It discusses data limitations, computational power, ethical considerations, and their implications for dentists. This can lay the groundwork for future research in this rapidly expanding field. Methods: The dataset used in this study consists of 1200 panoramic radiographs, which have been evaluated and classified into three categories: free of dental caries, coded as H; enamel-dentin caries lesions treated with restorative filling, coded as R; and deep dental caries that underwent root canal treatment, coded as E. The images are prepared for the training-testing process using the k-fold crossevaluation technique and then fed into the pre-trained deep learning models for classification. Results: The VGG-19 model achieved superior results compared to the other models, with macro-average scores of 0.9111 for precision, 0.9127 for recall, and 0.9115 for f1-score, respectively. Conclusion: The promising results obtained in this study give confidence in endorsing the use of deep learning models in the dental treatments sector.

 Objective: The purpose of this study is to evaluate the ability of deep learning models to classify mandibular molar teeth according to the presence and proximity of caries to the dental pulp. This research summarizes the progress of artificial intelligence and potential dental problems in diagnosis, treatment, and disease prediction in medicine. It discusses data limitations, computational power, ethical considerations, and their implications for dentists. This can lay the groundwork for future research in this rapidly expanding field. Methods: The dataset used in this study consists of 1200 panoramic radiographs, which have been evaluated and classified into three categories: free of dental caries, coded as H; enamel-dentin caries lesions treated with restorative filling, coded as R; and deep dental caries that underwent root canal treatment, coded as E. The images are prepared for the training-testing process using the k-fold crossevaluation technique and then fed into the pre-trained deep learning models for classification. Results: The VGG-19 model achieved superior results compared to the other models, with macro-average scores of 0.9111 for precision, 0.9127 for recall, and 0.9115 for f1-score, respectively. Conclusion: The promising results obtained in this study give confidence in endorsing the use of deep learning models in the dental treatments sector.

Makale detayları

Performance Evaluation of Deep Learning Models for Dental Caries Classification via Panoramic Radiograph Images

Yazarlar:

Omid Mırzaeı | Bülent Bilgehan | Mohamad Abduljalil | Mhammad Saleh | Ammar Kayssoun | Ahmet İlhan

Yayıncı:

Marmara University

Ana Başlıklar:

Endodontics | Dentistry (Other)

Dergi:

Clinical and Experimental Health Sciences Vol.14 Issue 4, pp.868-875

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329779

pISSN:

2459-1459

Artificial intelligence

deep learning

dental caries

panoramic radiology

Sayı

Makale detayları

Performance Evaluation of Deep Learning Models for Dental Caries Classification via Panoramic Radiograph Images

Yazarlar: Omid Mırzaeı | Bülent Bilgehan | Mohamad Abduljalil | Mhammad Saleh | Ammar Kayssoun | Ahmet İlhan

Yayıncı: Marmara University

Ana Başlıklar: Endodontics | Dentistry (Other)

Dergi: Clinical and Experimental Health Sciences Vol.14 Issue 4, pp.868-875

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001329779

pISSN: 2459-1459

Artificial intelligence

deep learning

dental caries

panoramic radiology

Sayı

Yazarlar:

Omid Mırzaeı | Bülent Bilgehan | Mohamad Abduljalil | Mhammad Saleh | Ammar Kayssoun | Ahmet İlhan

Yayıncı:

Marmara University

Ana Başlıklar:

Endodontics | Dentistry (Other)

Dergi:

Clinical and Experimental Health Sciences Vol.14 Issue 4, pp.868-875

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329779

pISSN:

2459-1459