Related Experiment Video
Updated: Sep 23, 2026
Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
Artificial intelligence in radiographic quantification and severity assessment of peri-implant marginal bone loss: A
Hooman Khanzadeh1, Sanaz Azizigermi2, Aida Mokhlesi3
1Research Institute for Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Purpose:
To critically assess artificial intelligence (AI)-based radiographic models for quantitative measurement, localization/detection, segmentation/keypoints, diagnostic classification, and severity or morphology assessment of peri-implant marginal bone loss (MBL) and peri-implantitis-related bone defects.
Methods:
PubMed/MEDLINE, Scopus, Web of Science, Embase, the Cochrane Library, Google Scholar, and reference lists were searched from inception through August 11, 2026. Eligible original studies evaluated AI-based radiographic assessment of existing dental implants. Quality Assessment of Diagnostic Accuracy Studies-3 (QUADAS-3) was applied at the prespecified estimate level for diagnostic/image-analysis studies and PROBAST for the prediction-model study.
Results:
A total of 1485 records were identified, and 17 studies were included. Fourteen reported localization/detection outcomes, six segmentation/keypoint outcomes, 10 severity/morphology outcomes, 12 diagnostic/classification outcomes, and six direct AI-clinician comparisons; categories overlapped. Implant/peri-implant tissue detection reached precision of 0.977, recall of 0.992, F1 score of 0.984, and mean intersection over union (IoU) of 0.916. Implant segmentation achieved a Dice of 0.986 and IoU of 0.974, whereas downstream peri-implantitis classification precision was 0.777. Sensitivity across diagnostic/prediction tasks ranged from approximately 66% to 96%. No study reported the complete prespecified absolute MBL measurement-agreement outcome set. Six studies used explicitly independent multi-rater reference standards with consensus and/or reported reliability, and none underwent clearly traceable independent multicenter external validation.
Conclusions:
Reported performance is task-specific and is frequently derived from retrospectively selected, enriched, internally split, or augmented datasets. Current models may support research and carefully supervised radiographic image-analysis tasks, but none can be recommended for routine clinical use until independent multicenter external validation and prospective studies demonstrate clinically acceptable absolute measurement error and patient-relevant benefit.
