Enhanced Diagnostic Accuracy for Dental Caries and Anomalies in Panoramic Radiographs Using a Custom Deep Learning Model.
| Autor: | Bhat S; Electronics Engineering, Ramrao Adik Institute of Technology, DY Patil University, Navi Mumbai, IND.; Computer Engineering, Vidyalankar Institute of Technology, Mumbai, IND., Birajdar G; Electronics Engineering, Ramrao Adik Institute of Technology, DY Patil University, Navi Mumbai, IND., Patil M; Electronics and Telecommunication Engineering, Ramrao Adik Institute of Technology, DY Patil University, Navi Mumbai, IND. |
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| Jazyk: | angličtina |
| Zdroj: | Cureus [Cureus] 2024 Aug 20; Vol. 16 (8), pp. e67315. Date of Electronic Publication: 2024 Aug 20 (Print Publication: 2024). |
| DOI: | 10.7759/cureus.67315 |
| Abstrakt: | Background Dental caries is one of the most prevalent conditions in dentistry worldwide. Early identification and classification of dental caries are essential for effective prevention and treatment. Panoramic dental radiographs are commonly used to screen for overall oral health, including dental caries and tooth anomalies. However, manual interpretation of these radiographs can be time-consuming and prone to human error. Therefore, an automated classification system could help streamline diagnostic workflows and provide timely insights for clinicians. Methods This article presents a deep learning-based, custom-built model for the binary classification of panoramic dental radiographs. The use of histogram equalization and filtering methods as preprocessing techniques effectively addresses issues related to irregular illumination and contrast in dental radiographs, enhancing overall image quality. By incorporating three separate panoramic dental radiograph datasets, the model benefits from a diverse dataset that improves its training and evaluation process across a wide range of caries and abnormalities. Results The dental radiograph analysis model is designed for binary classification to detect the presence of dental caries, restorations, and periapical region abnormalities, achieving accuracies of 97.01%, 81.63%, and 77.53%, respectively. Conclusions The proposed algorithm extracts discriminative features from dental radiographs, detecting subtle patterns indicative of tooth caries, restorations, and region-based abnormalities. Automating this classification could assist dentists in the early detection of caries and anomalies, aid in treatment planning, and enhance the monitoring of dental diseases, ultimately improving and promoting patients' oral healthcare. Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work. (Copyright © 2024, Bhat et al.) |
| Databáze: | MEDLINE |
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