Evaluating the Role of AI in Predicting the Success of Dental Implants Based on Preoperative CBCT Images: A Randomized Controlled Trial

Dental implant surgery has become a widely accepted method for replacing missing teeth. However, the success of dental implant procedures can be influenced by various factors, including the quality of preoperative planning and assessment. Cone beam computed tomography (CBCT) imaging provides valuabl...

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Bibliographic Details
Published in:	Journal of pharmacy & bioallied science Vol. 16; no. Suppl 1; pp. S886 - S888
Main Authors:	Rajan, R S Senthil, Kumar, H S Kiran, Sekhar, Anand, Nadakkavukaran, Davis, Feroz, Shaikh M A, Gangadharappa, Praveen
Format:	Journal Article
Language:	English
Published:	India Medknow Publications and Media Pvt. Ltd 01-02-2024 Medknow Publications & Media Pvt. Ltd Wolters Kluwer - Medknow Wolters Kluwer Medknow Publications
Subjects:	Artificial intelligence artificial intelligence (ai) Bone density Bone implants Clinical trials Complications Computed tomography cone beam computed tomography (cbct) Dental implants Dental prosthetics Implant dentures implant success Original Postoperative preoperative assessment Probability learning randomized controlled trial (rct) Success Artificial intelligence (AI) cone beam computed tomography (CBCT) randomized controlled trial (RCT) dental implants preoperative assessment implant success
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Summary:	Dental implant surgery has become a widely accepted method for replacing missing teeth. However, the success of dental implant procedures can be influenced by various factors, including the quality of preoperative planning and assessment. Cone beam computed tomography (CBCT) imaging provides valuable insights into a patient's oral anatomy, but accurately predicting implant success remains a challenge. In this randomized controlled trial (RCT), a cohort of 150 patients requiring dental implants was randomly divided into two groups: an artificial intelligence (AI)-assisted group and a traditional assessment group. Preoperative CBCT images of all patients were acquired and processed. The AI-assisted group utilized a machine learning model trained on historical data to assess implant success probability based on CBCT images, while the traditional assessment group relied on conventional methods and clinician expertise. Key parameters such as bone density, bone quality, and anatomical features were considered in the AI model. After the completion of the study, the AI-assisted group demonstrated a significantly higher implant success rate, with 92% of implants successfully integrating into the bone compared to 78% in the traditional assessment group. The AI model showed an accuracy of 87% in predicting implant success, whereas traditional assessment methods achieved an accuracy of 71%. Additionally, the AI-assisted group had a lower rate of complications and required fewer postoperative interventions compared to the traditional assessment group. The AI-assisted approach significantly improved implant success rates and reduced complications, underscoring the importance of incorporating AI into the dental implant planning process.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0976-4879 0975-7406
DOI:	10.4103/jpbs.jpbs_1117_23