Year: 2024
Type: article
Abstract: \nIn this paper, we present a crucial problem the public faces in maintaining dental health, specifically related to pulpitis. Pulpitis is an inflammation of the dental pulp tissue caused by various factors such as bacterial infection, trauma to the tooth, or tooth decay. We responded to this challenge by creating an innovative solution to detect and distinguish pulpitis from healthy teeth. This solution will help dental professionals diagnose and treat pulpitis more effectively. The method we applied in this research is pulpitis detection using audio signals with machine learning algorithms. In this study, we used a CNN1D model with the addition of MFCC as a feature extraction with the hyperparameters Adam optimizer, learning rate 0.001, batch size 32, and test size 0.2. The model evaluation used a confusion matrix to assess the model’s ability to predict based on sound. Implementing machine learning in pulpitis detection through audio signals can help health workers accurately diagnose the condition.\n
Institutions: Telkom University, Padjadjaran University
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6921932375
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