Artificial Intelligence-Based Clinical Decision Support Systems in Endodontics: Integrating Machine Learning Signal Processing, EEG-Based Pattern Recognition, and CBCT Imaging Analysis.
Keywords:
Artificial intelligence, clinical decision support systems, endodontics, machine learning, EEG signal processing, CBCT imaging, deep learning, healthcare innovation, diagnostic systems, predictive modelingAbstract
Clinical Decision Support Systems based on artificial intelligence have become revolutionary tools in contemporary endodontics, allowing increased diagnostic accuracy, predictive analytics, and individualized treatment planning. This paper discusses the combination of machine learning signal processing, EEG-based pattern recognition, and cone-beam computed tomography imaging in a single AI-driven clinical decision-making framework in endodontic practice.
The study develops a multi-modal framework that utilizes deep learning algorithms to understand complex imaging data and signal patterns to enhance the accuracy of diagnoses and treatment outcomes. Evaluation of the system is based on various performance metrics such as accuracy, precision, recall, and F1-score, and it is shown that the system performs significantly better than traditional diagnostic processes.
The authors highlight the potential of integrating neuro-signal analysis with high-quality imaging to develop intelligent, adaptive, and reliable clinical support systems. This paper contributes to the literature on AI-focused healthcare by offering a scalable and interpretable model to improve clinical procedures, reduce diagnostic errors, and promote evidence-based decision-making in endodontics
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