Publications by authors named "Ibrahim Yucel Ozbek"
The detection of distomolar teeth on panoramic radiographs using different artificial intelligence models.
J Stomatol Oral Maxillofac Surg
November 2024
Purposes: One notable anomaly, presence of distomolars, arises beyond the typical sequence of the human dental system. In this study, convolutional neural networks (CNNs) based machine learning methods were employed to classify distomolar tooth existence using panoramic radiography (PR).
Methods: PRs dataset, composed of 117 subjects with distomolar teeth and 146 subjects without distomolar teeth, was constructed.
Article Synopsis
- The study is about using pictures of tongues to figure out different kinds of tongue problems using special computer programs called Deep Convolutional Neural Networks (DCNNs).
- Researchers looked at images from 623 patients to find five types of tongues: healthy, coated, geographical, fissured, and a specific glossitis type.
- The best results showed that the computer programs could correctly identify if a tongue was healthy or had an issue almost 95% of the time, suggesting this method might help doctors in the future.
Statement Of Problem: Determining the brand and angle of an implant clinically or radiographically can be challenging. Whether artificial intelligence can assist is unclear.
Purpose: The purpose of the present study was to determine the brand and angle of implants from panoramic radiographs with artificial intelligence.
The event related P300 potentials, positive waveforms in electroencephalography (EEG) signals, are often utilized in brain computer interfaces (BCI). Many studies have been carried out to improve the performance of P300 speller systems either by developing signal processing algorithms and classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this purpose.
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