Clinical Evaluation of AI-Based Three-Dimensional Dental Implant Planning: A Multicenter Study.
Authors
Affiliations (8)
Affiliations (8)
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Dental Artificial Intelligence and Robotics R&D Center, Hallym University Medical Center, Anyang 14066, Republic of Korea. Electronic address: [email protected].
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Dental Artificial Intelligence and Robotics R&D Center, Hallym University Medical Center, Anyang 14066, Republic of Korea. Electronic address: [email protected].
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Dental Artificial Intelligence and Robotics R&D Center, Hallym University Medical Center, Anyang 14066, Republic of Korea. Electronic address: [email protected].
- Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Dental Artificial Intelligence and Robotics R&D Center, Hallym University Medical Center, Anyang 14066, Republic of Korea; Division of Oral and Maxillofacial Surgery, Hallym University Dongtan Sacred Heart Hospital, Hwaseong 18450, Republic of Korea. Electronic address: [email protected].
- Department of Oral and Maxillofacial Surgery, Korea University Guro Hospital, Seoul 08308, Republic of Korea. Electronic address: [email protected].
- Department of Oral and Maxillofacial Surgery, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea. Electronic address: [email protected].
- Department of Oral and Maxillofacial Surgery, College of Dentistry, Gangneung-Wonju National University, Gangneung 25457, Republic of Korea. Electronic address: [email protected].
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; Dental Artificial Intelligence and Robotics R&D Center, Hallym University Medical Center, Anyang 14066, Republic of Korea. Electronic address: [email protected].
Abstract
Dental implants have become more straightforward and convenient with advancements of digital technology in dentistry. Implant planning utilizing artificial intelligence (AI) has been attempted, yet its clinical efficacy remains underexplored. We aimed to assess the clinical applicability of AI-based implant planning software as a decision-support tool in comparison with those placed by clinicians which were clinically appropriate in their three-dimensional positions. Overall, 350 implants from 228 patients treated at four university hospitals were analyzed. The AI algorithm was developed using enhanced deep convolutional neural networks. Implant positions planned by the AI were compared with those placed freehand by clinicians. Three-dimensional deviations were measured and analyzed according to clinical factors, including the presence of opposing or contralateral teeth, jaw, and side. Independent sample t-test and two-way ANOVA were employed for statistical analysis. The mean coronal, apical, and angular deviations were 2.99 ± 1.56 mm, 3.66 ± 1.68 mm, and 7.56 ± 4.67°, respectively. Angular deviation was significantly greater in the absence of contralateral teeth (p=0.039), and apical deviation was significantly greater in the mandible (p<0.001). The AI-based 3D implant planning tool demonstrated potential as a decision-support system by providing valuable guidance in clinical scenarios. However, discrepancies between AI-generated and actual implant positions indicate that further research and development are needed to enhance its predictive accuracy. AI-based implant planning may serve as a supportive tool under clinician supervision, potentially improving workflow efficiency and contributing to more standardized implant treatment planning as the technology advances.