Deep learning for diagnostic charting on pediatric panoramic radiographs

Name: Deep learning for diagnostic charting on pediatric panoramic radiographs
Author: Fehmi, Hasan, Aydin, K. C., Urkmez, E. S., Gunec, H. G., Kaya, E.

İsim	Deep learning for diagnostic charting on pediatric panoramic radiographs
Yazar	Fehmi, Hasan, Aydin, K. C., Urkmez, E. S., Gunec, H. G., Kaya, E.
Basım Tarihi:	2024-10-15
Basım Yeri	- Quintessenz Verlags-GmbH
Konu	Oral diagnosis, Panoramic radiography, Pediatric den- tistry, Deep learning, Artificial intelligence
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1463-4201
Kayıt Numarası	2bb0cd54-d614-4037-8488-e5e77b93051c
Lokasyon	Computer Science
Tarih	2024-10-15
Örnek Metin	Aim: Artificial intelligence (AI)-based systems are used in dentistry to ensure a more accurate and efficient diagnostic process. The objective of the present study was to evaluate the performance of a deep learning (DL) program for the detection and classification of dental structures and treatments on panoramic radiographs of pediatric patients. Materials and methods: In total, 4821 anonymized digital panoramic radiographs of children between 5 and 13 years of age were analyzed by YOLOv4, a CNN (Convolutional Neural Networks)-based object detection model. The ability to make a correct diagnosis was tested on samples from pediatric patients examined within the scope of the study. All statistical analyses were performed using SPSS version 26.0 software. Results: The YOLOv4 model diagnosed the primary teeth, permanent tooth germs, and brackets successfully, with high F1 scores of 0.95, 0.90, and 0.76, respectively. Although this model achieved promising results, there were certain limitations for some dental structures and treatments, including fillings, root canal treatments, and supernumerary teeth. The architecture of the present study achieved reliable results, with some specific limitations for detecting dental structures and treatments. Conclusion: The detection of certain dental structures and previous dental treatments on pediatric panoramic radiographs by using a DL-based approach may provide early diagnosis of some dental anomalies and help dental practitioners to find more accurate treatment options by saving time and effort.
DOI	10.3290/picd.b4200863
Cilt	27

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Deep learning for diagnostic charting on pediatric panoramic radiographs

Yazar Fehmi, Hasan, Aydin, K. C., Urkmez, E. S., Gunec, H. G., Kaya, E.

Basım Tarihi 2024-10-15

Basım Yeri - Quintessenz Verlags-GmbH

Konu Oral diagnosis, Panoramic radiography, Pediatric den- tistry, Deep learning, Artificial intelligence

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1463-4201

Kayıt Numarası 2bb0cd54-d614-4037-8488-e5e77b93051c

Lokasyon Computer Science

Tarih 2024-10-15

Örnek Metin Aim: Artificial intelligence (AI)-based systems are used in dentistry to ensure a more accurate and efficient diagnostic process. The objective of the present study was to evaluate the performance of a deep learning (DL) program for the detection and classification of dental structures and treatments on panoramic radiographs of pediatric patients. Materials and methods: In total, 4821 anonymized digital panoramic radiographs of children between 5 and 13 years of age were analyzed by YOLOv4, a CNN (Convolutional Neural Networks)-based object detection model. The ability to make a correct diagnosis was tested on samples from pediatric patients examined within the scope of the study. All statistical analyses were performed using SPSS version 26.0 software. Results: The YOLOv4 model diagnosed the primary teeth, permanent tooth germs, and brackets successfully, with high F1 scores of 0.95, 0.90, and 0.76, respectively. Although this model achieved promising results, there were certain limitations for some dental structures and treatments, including fillings, root canal treatments, and supernumerary teeth. The architecture of the present study achieved reliable results, with some specific limitations for detecting dental structures and treatments. Conclusion: The detection of certain dental structures and previous dental treatments on pediatric panoramic radiographs by using a DL-based approach may provide early diagnosis of some dental anomalies and help dental practitioners to find more accurate treatment options by saving time and effort.

DOI 10.3290/picd.b4200863

Cilt 27