AUTHOR=Sabharwal Amarpreet , Kavthekar Neil , Miecznikowski Jeffrey , Glogauer Michael , Maddi Abhiram , Sarder Pinaki TITLE=Integrating Image Analysis and Dental Radiography for Periodontal and Peri-Implant Diagnosis JOURNAL=Frontiers in Dental Medicine VOLUME=3 YEAR=2022 URL=https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2022.840963 DOI=10.3389/fdmed.2022.840963 ISSN=2673-4915 ABSTRACT=

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.