Artificial intelligence prediction of implant placement difficulty based on bone morphology and anatomical constraints.
Authors
Affiliations (10)
Affiliations (10)
- Department of Prosthodontics and Crown & Bridge, Army College of Dental Sciences, Hyderabad, India.
- Department of Oral Medicine and Radiology, Government Dental College, Thiruvananthapuram, India.
- Oral & Maxillofacial Surgery, YMT Dental College, India.
- Faculty of Dental Science, Nadiad, Gujarat, India.
- Department of Oral Medicine and Radiology, India.
- Department of Conservative Dentistry & Endodontics, Rama Dental College Hospital & Research Centre, Kanpur, Uttar Pradesh, India.
- Consultant Prosthodontist and Implantologist, India.
- Department of Oral Medicine and Radiology, Faculty of Dental Science, Nadiad, Gujarat, India.
- Research Faculty, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
- Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, Rajnandgaon, Chhattisgarh, India.
Abstract
Accurate preoperative assessment of implant placement difficulty is essential for effective surgical planning and minimizing complications. Conventional evaluation using cone-beam computed tomography (CBCT) relies heavily on clinician interpretation and is often subjective and operator-dependent. Artificial intelligence (AI) offers a promising approach for objective and reproducible prediction of surgical complexity using quantitative imaging parameters. To develop and evaluate an AI-based model for predicting implant placement difficulty based on bone morphology and anatomical constraints derived from CBCT imaging. A prospective observational study was conducted on 250 implant sites. Bone morphology parameters-including bone height, width, cortical thickness, and density-along with anatomical constraints such as proximity to vital structures, were assessed using standardized CBCT analysis. Implant placement difficulty was categorized intraoperatively as low, moderate, or high. Machine learning models, including logistic regression, random forest, and XGBoost, were developed using Python libraries. Model performance was evaluated using receiver operating characteristic (ROC) analysis, area under the curve (AUC), sensitivity, specificity, and calibration metrics such as the Brier score. Moderate difficulty cases were most common (41.6%), followed by low (36.8%) and high (21.6%) difficulty. Reduced bone dimensions and density were significantly associated with increased difficulty (p < 0.001). XGBoost showed the best performance (AUC 0.91, accuracy 87.6%, sensitivity 0.85, specificity 0.89, Brier score 0.108). AI models, particularly XGBoost, can reliably predict implant placement difficulty and enhance clinical decision-making and treatment planning.