A technique to fabricate a custom CAD-CAM periapical radiographic film holder for implant assessment

Efficacy of a radiographic film holder adapter in generating radiographs of dental implants with improved geometric accuracy and sharpness

STATEMENT OF PROBLEM

Some radiographic film holders produce radiographs with geometric distortion that may interfere with diagnosis. However, whether the distortion can be corrected by adjusting the design of the radiographic film holder is unclear.

PURPOSE

The purpose of the study was to develop an adapter for a radiographic film holder model aiming to generate radiographs with greater sharpness and a more accurate geometric representation of dental implants.

MATERIAL AND METHODS

The 2-piece adapter was designed using the SketchUp software program and was 3-dimensionally (3D) printed. Implants with internal conical connections were installed in 19 maxillary prototypes in the central incisor region. Five dentists obtained 285 digital periapical radiographs with 3 different radiographic film holders: standard Cone Indicator, Rinn XCP, and adapted Cone Indicator. They then evaluated the radiographic sharpness of the implants threads and their dimensions using the ImageJ software program. The data were analyzed using the Friedman test with the Durbin-Conover post hoc test and MANOVA with the Tukey post hoc test (α=.05).

RESULTS

On the mesial surface of the implants, the threads were sharper for the adapted than for the standard Cone Indicator radiographic film holder (P<.05). The adapted Cone Indicator showed a smaller difference between the radiographic and actual implant diameters compared with the Rinn XCP and standard Cone Indicator radiographic film holders (P<.05).

CONCLUSIONS

The developed adapter provided radiographs of dental implants with improved sharpness and geometric accuracy.

Integrating Image Analysis and Dental Radiography for Periodontal and Peri-Implant Diagnosis

The recent change in classification of periodontal and peri-implant diseases includes objective evaluation of intra-oral radiographs and quantification of bone loss for disease staging and grading. Assessment of the progression of periodontal disease requires deduction of bone loss longitudinally, and its interpretation as (1) a percentage in relation to tooth root and (2) as a function of the patient's age. Similarly, bone loss around dental implants, after accounting for initial remodeling, is central for determining diagnosis, severity, and progression of peri-implantitis. Bone gain secondary to periodontal regeneration can be measured using standardized dental radiographs and compared to baseline morphology to determine treatment success. Computational image analysis, including machine learning (ML), has the potential to develop and automate quantitative measures of tooth, implant, bone volumes, and predict disease progression. The developed algorithms need to be standardized while considering pre-analytic, analytic, and post-analytic factors for successful translation to clinic. This review will introduce image analysis and machine learning in the context of dental radiography, and expand on the potential for integration of image analysis for assisted diagnosis of periodontitis and peri-implantitis.