Evaluating the role of AI in predicting the success of dental implants based on preoperative CBCT images: A randomized controlled trial
Background: 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 prov...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2024-01-01
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| Series: | Journal of Pharmacy and Bioallied Sciences |
| Subjects: | |
| Online Access: | http://www.jpbsonline.org/article.asp?issn=0975-7406;year=2024;volume=16;issue=5;spage=886;epage=888;aulast=Senthil |
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| author | R S Senthil Rajan H S Kiran Kumar Anand Sekhar Davis Nadakkavukaran Shaikh M A. Feroz Praveen Gangadharappa |
| author_facet | R S Senthil Rajan H S Kiran Kumar Anand Sekhar Davis Nadakkavukaran Shaikh M A. Feroz Praveen Gangadharappa |
| author_sort | R S Senthil Rajan |
| collection | DOAJ |
| description | Background: 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. Materials and Methods: 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. Results: 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. Conclusion: The AI-assisted approach significantly improved implant success rates and reduced complications, underscoring the importance of incorporating AI into the dental implant planning process. |
| first_indexed | 2024-04-24T13:17:51Z |
| format | Article |
| id | doaj.art-0f0832fbbbf640709a376c8e09b76a4a |
| institution | Directory Open Access Journal |
| issn | 0975-7406 |
| language | English |
| last_indexed | 2024-04-24T13:17:51Z |
| publishDate | 2024-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Pharmacy and Bioallied Sciences |
| spelling | doaj.art-0f0832fbbbf640709a376c8e09b76a4a2024-04-04T16:37:41ZengWolters Kluwer Medknow PublicationsJournal of Pharmacy and Bioallied Sciences0975-74062024-01-0116588688810.4103/jpbs.jpbs_1117_23Evaluating the role of AI in predicting the success of dental implants based on preoperative CBCT images: A randomized controlled trialR S Senthil RajanH S Kiran KumarAnand SekharDavis NadakkavukaranShaikh M A. FerozPraveen GangadharappaBackground: 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. Materials and Methods: 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. Results: 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. Conclusion: The AI-assisted approach significantly improved implant success rates and reduced complications, underscoring the importance of incorporating AI into the dental implant planning process.http://www.jpbsonline.org/article.asp?issn=0975-7406;year=2024;volume=16;issue=5;spage=886;epage=888;aulast=Senthilartificial intelligence (ai)cone beam computed tomography (cbct)dental implantsimplant successpreoperative assessmentrandomized controlled trial (rct) |
| spellingShingle | R S Senthil Rajan H S Kiran Kumar Anand Sekhar Davis Nadakkavukaran Shaikh M A. Feroz Praveen Gangadharappa Evaluating the role of AI in predicting the success of dental implants based on preoperative CBCT images: A randomized controlled trial Journal of Pharmacy and Bioallied Sciences artificial intelligence (ai) cone beam computed tomography (cbct) dental implants implant success preoperative assessment randomized controlled trial (rct) |
| title | Evaluating the role of AI in predicting the success of dental implants based on preoperative CBCT images: A randomized controlled trial |
| title_full | Evaluating the role of AI in predicting the success of dental implants based on preoperative CBCT images: A randomized controlled trial |
| title_fullStr | Evaluating the role of AI in predicting the success of dental implants based on preoperative CBCT images: A randomized controlled trial |
| title_full_unstemmed | Evaluating the role of AI in predicting the success of dental implants based on preoperative CBCT images: A randomized controlled trial |
| title_short | Evaluating the role of AI in predicting the success of dental implants based on preoperative CBCT images: A randomized controlled trial |
| title_sort | evaluating the role of ai in predicting the success of dental implants based on preoperative cbct images a randomized controlled trial |
| topic | artificial intelligence (ai) cone beam computed tomography (cbct) dental implants implant success preoperative assessment randomized controlled trial (rct) |
| url | http://www.jpbsonline.org/article.asp?issn=0975-7406;year=2024;volume=16;issue=5;spage=886;epage=888;aulast=Senthil |
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