Predictive modeling for peri-implantitis by using machine learning techniques. Sci Rep. 2021;11:11090. [PMID: 34045590 DOI: 10.1038/s41598-021-90642-4]

Diagnostic measures for monitoring and follow-up in periodontology and implant dentistry

This review discusses the role of diagnostic measures in the lifelong management of periodontal disease and peri-implant complications. After active treatment, these conditions require regular monitoring of the supporting structures of teeth and dental implants to assess bone and soft tissue health over time. Several clinical measures have been developed for the routine assessment of periodontal and peri-implant tissues, including periodontal and peri-implant probing, bleeding on probing, intraoral radiography, biomarker analysis, and microbiological testing. This review highlights the evolution of diagnostic practices, integrating traditional methods with emerging technologies such as resonance frequency analysis and ultrasound imaging to provide a holistic view of peri-implant health assessment. In addition to objective measurements, patient risk factors are considered. The goals of periodontal and peri-implant maintenance are to control disease activity and stabilize tissues through supportive care, which includes diagnostic measures at follow-up visits. This enables clinicians to monitor treatment outcomes, assess health status, and detect recurrence or progression early through routine evaluation, allowing additional interventions, including adjustment of supportive therapy intervals, to further improve and maintain periodontal and peri-implant stability over time.

Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

Application of artificial intelligence in dental implant prognosis: A scoping review

OBJECTIVES

The purpose of this scoping review was to evaluate the performance of artificial intelligence (AI) in the prognosis of dental implants.

DATA

Studies that analyzed the performance of AI models in the prediction of implant prognosis based on medical records or radiographic images. Quality assessment was conducted using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies.

SOURCES

This scoping review included studies published in English up to October 2023 in MEDLINE/PubMed, Embase, Cochrane Library, and Scopus. A manual search was also performed.

STUDY SELECTION

Of 892 studies, full-text analysis was conducted in 36 studies. Twelve studies met the inclusion criteria. Eight used deep learning models, 3 applied traditional machine learning algorithms, and 1 study combined both types. The performance was quantified using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic area under curves (ROC AUC). The prognostic accuracy was analyzed and ranged from 70 % to 96.13 %.

CONCLUSIONS

AI is a promising tool in evaluating implant prognosis, but further enhancements are required. Additional radiographic and clinical data are needed to improve AI performance in implant prognosis.

CLINICAL SIGNIFICANCE

AI can predict the prognosis of dental implants based on radiographic images or medical records. As a result, clinicians can receive predicted implant prognosis with the assistance of AI before implant placement and make informed decisions.

Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review

Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.

Establishing a novel deep learning model for detecting peri-implantiti

BACKGROUND/PURPOSE

The diagnosis of peri-implantitis using periapical radiographs is crucial. Recently, artificial intelligence may apply in radiographic image analysis effectively. The aim of this study was to differentiate the degree of marginal bone loss of an implant, and also to classify the severity of peri-implantitis using a deep learning model.

MATERIALS AND METHODS

A dataset of 800 periapical radiographic images were divided into training (n = 600), validation (n = 100), and test (n = 100) datasets with implants used for deep learning. An object detection algorithm (YOLOv7) was used to identify peri-implantitis. The classification performance of this model was evaluated using metrics, including the specificity, precision, recall, and F1 score.

RESULTS

Considering the classification performance, the specificity was 100%, precision was 100%, recall was 94.44%, and F1 score was 97.10%.

CONCLUSION

Results of this study suggested that implants can be identified from periapical radiographic images using deep learning-based object detection. This identification system could help dentists and patients suffering from implant problems. However, more images of other implant systems are needed to increase the learning performance to apply this system in clinical practice.

Early diagnosis for the onset of peri-implantitis based on artificial neural network

The aim of this study is to construct an artificial neural network (ANN) based on bioinformatic analysis to enable early diagnosis of peri-implantitis (PI). PI-related datasets were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and functional enrichment analyses were performed between PI and the control group. Furthermore, the infiltration of 22 immune cells in PI was analyzed using CIBERSORT. Hub genes were identified with random forest (RF) classification. The ANN model was then constructed for early diagnosis of PI. A total of 1,380 DEGs were identified. Enrichment analysis revealed the involvement of neutrophil-mediated immunity and the NF-kappa B signaling pathway in PI. Additionally, higher proportion of naive B cells, activated memory CD4 T cells, activated NK cells, M0 macrophages, M1 macrophages, and neutrophils were observed in the soft tissues surrounding PI. From the RF analysis, 13 hub genes (ST6GALNAC4, MTMR11, SKAP2, AKR1B1, PTGS2, CHP2, CPEB2, SYT17, GRIP1, IL10, RAB8B, ABHD5, and IGSF6) were selected. Subsequently, the ANN model for early diagnosis of PI was constructed with high performance. We identified 13 hub genes and developed an ANN model that accurately enables early diagnosis of PI.

Deep learning-based prediction of osseointegration for dental implant using plain radiography

BACKGROUND

In this study, we investigated whether deep learning-based prediction of osseointegration of dental implants using plain radiography is possible.

METHODS

Panoramic and periapical radiographs of 580 patients (1,206 dental implants) were used to train and test a deep learning model. Group 1 (338 patients, 591 dental implants) included implants that were radiographed immediately after implant placement, that is, when osseointegration had not yet occurred. Group 2 (242 patients, 615 dental implants) included implants radiographed after confirming successful osseointegration. A dataset was extracted using random sampling and was composed of training, validation, and test sets. For osseointegration prediction, we employed seven different deep learning models. Each deep-learning model was built by performing the experiment 10 times. For each experiment, the dataset was randomly separated in a 60:20:20 ratio. For model evaluation, the specificity, sensitivity, accuracy, and AUROC (Area under the receiver operating characteristic curve) of the models was calculated.

RESULTS

The mean specificity, sensitivity, and accuracy of the deep learning models were 0.780-0.857, 0.811-0.833, and 0.799-0.836, respectively. Furthermore, the mean AUROC values ranged from to 0.890-0.922. The best model yields an accuracy of 0.896, and the worst model yields an accuracy of 0.702.

CONCLUSION

This study found that osseointegration of dental implants can be predicted to some extent through deep learning using plain radiography. This is expected to complement the evaluation methods of dental implant osseointegration that are currently widely used.

Does implant location influence the risk of peri-implantitis?

Peri-implantitis is characterized by nonreversible and progressive loss of supporting bone and is associated with bleeding and/or suppuration on probing. Peri-implant disease is considered as the main etiologic factor related to implant failure. Peri-implant disease has a pathogenesis similar to that of periodontal disease, both being triggered by an inflammatory response to the biofilm accumulation. Although the prevalence of peri-implantitis has been evaluated by several clinical studies with different follow-ups, there are currently little data on the impact of implant location and the prevalence of peri-implantitis. The aim of this review, therefore, was to summarize the evidence concerning the prevalence of peri-implantitis in relation to implant location and associated risk predictors. Even though most studies evaluating the prevalence of peri-implantitis in relation to implant location are cross-sectional or retrospective, they suggest that the occurrence of peri-implantitis is most prevalent in the anterior regions of the maxilla and mandible. Moreover, it seems that there is a higher prevalence of peri-implantitis in the maxilla than in the mandible.

Application of machine learning in oral and maxillofacial surgery

Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.