Artificial Intelligence in Dentistry: Past, Present, and Future

Paridhi, A. and Pradnya, N., 2022. Artificial Intelligence in Dentistry: Past, Present, and Future. Cureus, 14(7).

The ever-evolving landscape of dentistry is witnessing a remarkable surge in the integration of artificial intelligence (AI), particularly in the specialized field of endodontics. This summary delves into the progressive role that AI plays in reshaping treatment planning and disease diagnosis within dental care.

Commencing our exploration is the nuanced capacity of AI to discern imperceptible changes at the pixel level, a feat often overlooked by the human eye. This transformative technology finds applications across diverse facets of endodontics, including the detection of periapical lesions, identification of root fractures, determination of working length, and the assessment of root and canal system morphology.

Periapical lesions, a challenging diagnosis for clinicians, see traditional methods like Intraoral Periapical Radiography (IOPA) and Orthopantomogram (OPG) facing limitations due to their two-dimensional representation of three-dimensional anatomy. The emergence of three-dimensional imaging, notably Cone Beam Computed Tomography (CBCT), proves more accurate, with AI models showcasing impressive precision in detecting periapical pathology and categorizing its severity.

In the realm of identifying root fractures, AI, particularly in the form of Convolutional Neural Networks (CNN), demonstrates efficacy on panoramic radiographs, surpassing traditional radiography in terms of specificity, accuracy, and sensitivity.

The pivotal task of determining working length in root canal treatments benefits significantly from AI, specifically Artificial Neural Networks (ANNs), which outperform traditional methods. Additionally, the morphology assessment of root and canal systems witnesses a leap forward with the application of AI, incorporating deep learning algorithms and CNN for enhanced precision and efficiency.

AI’s predictive capabilities extend into the prognosis of retreatment outcomes, employing case-based reasoning to offer valuable insights into the potential success of nonsurgical retreatment.

In conclusion, this exploration illuminates the multifaceted applications of AI in endodontics, pointing toward a future where diagnostics, treatment planning, and predictive capacities are elevated, revolutionizing the very fabric of dental care.

Research Summary Written By: Hameed Rahimi, King’s College London – BDS4

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