Determination of the stage and grade of periodontitis according to the current classification of periodontal and peri-implant diseases and conditions (2018) using machine learning algorithms

Abstract

Purpose The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.

Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study

“…According to the current classification of periodontal and peri-implant diseases and conditions developed in 2018, the detection of periodontal bone loss patterns is very important for determining disease stages [ 16 ]. In this classification system, Stage I and II periodontitis are associated with the presence of horizontal bone loss, while the presence of vertical bone losses and furcation defects in Stage III and IV periodontitis is noted [ 16 , 17 ]. Current academic studies using image processing and machine learning technologies and aiming at automatic periodontal disease classification have pointed out the determination of periodontal bone loss patterns as one of the main classification criteria [ 17 , 18 ].…”

“…In this classification system, Stage I and II periodontitis are associated with the presence of horizontal bone loss, while the presence of vertical bone losses and furcation defects in Stage III and IV periodontitis is noted [ 16 , 17 ]. Current academic studies using image processing and machine learning technologies and aiming at automatic periodontal disease classification have pointed out the determination of periodontal bone loss patterns as one of the main classification criteria [ 17 , 18 ]. Therefore, the determination of periodontal bone loss pattern is of clinical importance in making the current periodontal disease classification complete and accurate [ 17 , 18 ].…”

“…Current academic studies using image processing and machine learning technologies and aiming at automatic periodontal disease classification have pointed out the determination of periodontal bone loss patterns as one of the main classification criteria [ 17 , 18 ]. Therefore, the determination of periodontal bone loss pattern is of clinical importance in making the current periodontal disease classification complete and accurate [ 17 , 18 ].…”

Biocompatible Nanostructured Silver-Incorporated Implant Surfaces Show Effective Antibacterial, Osteogenic, and Anti-Inflammatory Effects in vitro and in Rat Model

“…4 Recent consensus reports identify bacterial biofilm as the primary cause of peri-implant mucositis and peri-implantitis, characterized by chronic inflammation, bone loss, and eventual loss of implant osteointegration. [5][6][7] Therefore, conferring Ti-based implant surfaces with desirable antibacterial activity is imperative to optimizing clinical outcomes and prognosis in implant therapy. 8 Antibiotic and antimicrobial peptides (AMPs) have been functionalized as antibacterial agents on implant surfaces to prevent bacterial adhesion and biofilm formation.…”

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

“…Moreover, whilst most research has been using only statistical methods to tackle the topic, machine learning (ML) methodologies in artificial intelligence have recently been utilized to choose the most important variables (namely the feature selection/feature importance) in the identification of caries [ 23 ], and periodontitis [ 24 ] including our publication which studied the associations between BMI and dental caries using ML and statistical models [ 25 ]. To the best of our knowledge, no previous research has been published using statistical and machine learning models to study the associations between OSA and dental status in the context of metabolic dysfunction.…”