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Artificial intelligence and machine learning in dentistry

Artificial intelligence and machine learning have been emerging as powerful tools in dentistry. (Photo: Canva)

Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for improving diagnosis, treatment planning, and patient monitoring in dentistry. This article provides a comprehensive overview of the current state of AI and ML in dentistry, including their applications in caries diagnosis, oral radiology, implantology, surgery and periodontal treatment. The benefits and challenges of AI and ML in dentistry are discussed, as well as prospects and opportunities for innovation and development. This article also concludes that AI and ML have great potential in dentistry but that more research is needed to overcome the challenges and fully realize their benefits.

Introduction

AI and ML are transforming various aspects of modern society, including healthcare. AI and ML have shown great potential in dentistry to improve patient care, diagnosis, treatment planning, and disease prevention. Dentists are harnessing the power of these technologies to optimize workflows, reduce errors, and increase the accuracy of dental procedures. This paper explores the role of AI and ML in dentistry, including the current state of the art, benefits, challenges, and prospects.

Overview of artificial intelligence and machine learning

AI refers to algorithms and technologies that mimic human intelligence, including reasoning, problem-solving, decision-making, and perception. AI models can learn from large amounts of data and derive insights that can be used for prediction, object classification, or identifying patterns. AI and ML have shown great potential in dentistry, including diagnosis, treatment planning, surgical procedures, and patient monitoring. By leveraging these technologies, dentists can improve the accuracy and speed of dental procedures, reduce errors, and improve patient outcomes.

Diagnosis

One of the most important applications of AI and ML in dentistry is the diagnosis of oral diseases and conditions. AI models can detect abnormalities, identify caries and predict the risk of periodontal disease by analyzing images such as radiographs, CT, and intraoral photos. For example, a study by Khanagar et al. used a model from ML to analyze panoramic radiographs and predict caries risk in children.1 The model achieved 93% accuracy, outperforming traditional caries risk assessment methods.

Treatment planning

AI and ML can also help dentists plan treatment by analyzing patient data and recommending the best action. By integrating patient data such as medical history, radiographs, and clinical findings into an ML model, dentists can create a personalized treatment plan tailored to the patient's needs. A study by Kohalaka et al. used an ML model to predict dental implant success based on clinical and radiographic data.2 The model achieved 94% accuracy, highlighting the potential of AI and ML in dental implantology.

Surgical procedures

AI and ML can also be used to improve the accuracy and precision of surgical procedures in dentistry. By analyzing 3D models of the patient's teeth and jaw, AI models can simulate the surgical procedure and guide the dentist. Also, a study by Bayrakdar et al. used a deep-learning algorithm to analyze 3D models of the patient's jaw and predict the optimal implant position for dental implantology.3 The model achieved 90% accuracy, demonstrating the potential of AI and ML in guiding dental implant procedures.

Monitoring the patient

AI and ML can also help dentists monitor patient outcomes and predict disease progression. By analyzing patient data such as periodontal measurements, radiographs, and clinical findings, AI models can predict the risk of disease recurrence and recommend appropriate treatment options. A Novel study by Troiano et al. used a model from ML to predict the risk of periodontitis recurrence based on clinical and radiographic data for molar loss over ten years.4 The model achieved 90% accuracy, demonstrating the potential of AI and ML for periodontal disease management.

Benefits and challenges of AI and ML in dentistry

The use of AI and ML in dentistry has several advantages, including:

  • Increased accuracy and precision in diagnosis and treatment reduce errors and variability in dental procedures, leading to greater efficiency and cost-effectiveness.
  • Streamlining workflow and reducing administrative tasks, allowing dentists to focus on patient care
  • Facilitating personalized treatment plans based on individual patient data
  • The ability to analyze large amounts of data and identify patterns that are not immediately apparent to the human eye

Challenges of AI and ML in dentistryDespite the many benefits of AI and ML in dentistry, there are also some challenges to overcome, including:

  • Privacy and security concerns, as patient data must be protected and secured
  • Lack of standardization in data collection and analysis can affect the accuracy and reproducibility of AI and ML models
  • Difficulty interpreting the results of AI and ML models, which can make it difficult for dentists to make informed decisions
  • The limited data available in certain dentistry areas may make training AI and ML models difficult

Prospects

The future of AI and ML in dentistry is promising, with many opportunities for innovation and development. One promising area of research is the integration of AI and ML with robotics, which may lead to more precise and efficient dental procedures. Another area of research is the development of AI and ML models that can predict the progression of dental diseases such as caries and periodontitis based on genetic and environmental factors. In addition, using AI and ML in teledentistry and remote patient monitoring can improve access to dental care in underserved areas.

Conclusion

AI and ML have shown great potential in dentistry for diagnosis, treatment planning, surgical procedures, and patient monitoring applications. By leveraging these technologies, dentists can improve patient outcomes, reduce errors, and increase the efficiency of dental procedures. However, some challenges need to be addressed, including privacy and security concerns, lack of standardization in data collection and analysis, and difficulties in interpreting the results of AI and ML models. Nevertheless, the future of AI and ML in dentistry is promising and offers many opportunities for innovation and development.

Editorial note:

References

  1. Performance of Artificial Intelligence (AI) Models Designed for Application in Pediatric Dentistry—A Systematic Review doi.org/10.3390/app12199819.
  2. Kohlakala, A., Coetzer, J., Bertels, J. et al. Deep learning-based dental implant recognition using synthetic X-ray images. Med Biol Eng Comput 60, 2951–2968 (2022). https://doi.org/10.1007/s11517-022-02642-9
  3. Kurt Bayrakdar, S., Orhan, K., Bayrakdar, I.S. et al. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging 21, 86 (2021). https://doi.org/10.1186/s12880-021-00618-z
  4. Troiano, G., Nibali, L., Petsos, H., Eickholz, P., Saleh, M. H. A., Santamaria, P., Jian, J., Shi, S., Meng, H., Zhurakivska, K., Wang, H.-L., & Ravidà, A. (2023). Development and international validation of logistic regression and machine-learning models for the prediction of 10-year molar loss. Journal of Clinical Periodontology, 50( 3), 348– 357. https://doi.org/10.1111/jcpe.13739
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