Dentists' perceptions and usability testing of the implant disease risk assessment IDRA, a tool for preventing peri­implant disease: A qualitative study.

INTRODUCTION: we aimed to explore dentists' perceptions toward the implementation of a dental informatics risk assessment tool which estimates the risk for a patient to develop peri­implantitis. MATERIALS AND METHODS: the Implant Disease Risk Assessment Tool (IDRA) was presented to a convenience sample of seven dentists working in a university clinic, whom were asked to use IDRA with the information of three clinical cases whilst thinking aloud and then fill the System Usability Scale (SUS). A semi-structured interview technique was used with audio record to allow free expression of participants' perceptions related to the IDRA. The interviews information was categorized and analyzed by the authors. RESULTS: to our knowledge, this is the first study conducted to develop a qualitative usability test of IDRA, evaluating the effectiveness, efficiency, and users' satisfaction. There were more variations in responses the greater the degree of complexity of the clinical case. Generally, the participants classified the tool as good, getting usability values of 77,2 (SD 19,8) and learnability 73,2 (SD 24,5). CONCLUSION: four additional factors should be considered to improve IDRA tool: 1) considering the relation between contour angle and peri-implant tissue height; 2) automatic periodontal classification in the IDRA tool after completing the periodontogram in the clinical software; 3) presentation of a flowchart to assist therapeutic decisions alongside the final score defined by the IDRA tool; 4) integrating of precision tests such as Implantsafe® DR… (dentognostics gmbh, Jena) and Oralyzer®(dentognostics gmbh, Jena). CLINICAL SIGNIFICANCE: etiology and pathogenesis of peri­implant diseases is multifactorial. These tools must follow a natural integration to be easily applied in a clinical setting. It is important to study their usability from the clinicians' point of view, evaluating the effectiveness, efficiency, and users' satisfaction.

Peri-implant diseases diagnosis, prognosis and dental implant monitoring: a narrative review of novel strategies and clinical impact.

BACKGROUND: The diagnosis of peri-implantar and periodontal relies mainly on a set of clinical measures and the evaluation of radiographic images. However, these clinical settings alone are not sufficient to determine, much less predict, periimplant bone loss or future implant failure. Early diagnosis of periimplant diseases and its rate of progress may be possible through biomarkers assessment. Once identified, biomarkers of peri-implant and periodontal tissue destruction may alert the clinicians before clinical signs show up. Therefore, it is important to consider developing chair-side diagnostic tests with specificity for a particular biomarker, indicating the current activity of the disease. METHODS: A search strategy was created at Pubmed and Web of Science to answer the question: "How the molecular point-of-care tests currently available can help in the early detection of peri-implant diseases and throws light on improvements in point of care diagnostics devices?" RESULTS: The PerioSafe® PRO DRS (dentognostics GmbH, Jena) and ImplantSafe® DR (dentognostics GmbH, Jena ORALyzer® test kits, already used clinically, can be a helpful adjunct tool in enhancing the diagnosis and prognosis of periodontal/peri-implantar diseases. With the advances of sensor technology, the biosensors can perform daily monitoring of dental implants or periodontal diseases, making contributions to personal healthcare and improve the current status quo of health management and human health. CONCLUSIONS: Based on the findings, more emphasis is given to the role of biomarkers in diagnosing and monitoring periodontal and peri-implant diseases. By combining these strategies with traditional protocols, professionals could increase the accuracy of early detection of peri-implant and periodontal diseases, predicting disease progression, and monitoring of treatment outcomes.

Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: A scoping review.

STATEMENT OF PROBLEM: The use of bioinformatic strategies is growing in dental implant protocols. The current expansion of Omics sciences and artificial intelligence (AI) algorithms in implant dentistry applications have not been documented and analyzed as a predictive tool for the success of dental implants. PURPOSE: The purpose of this scoping review was to analyze how artificial intelligence algorithms and Omics technologies are being applied in the field of oral implantology as a predictive tool for dental implant success. MATERIAL AND METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist was followed. A search strategy was created at PubMed and Web of Science to answer the question "How is bioinformatics being applied in the area of oral implantology as a predictive tool for implant success?" RESULTS: Thirteen articles were included in this review. Only 3 applied bioinformatic models combining AI algorithms and Omics technologies. These studies highlighted 2 key points for the creation of precision medicine: deep population phenotyping and the integration of Omics sciences in clinical protocols. Most of the studies identified applied AI only in the identification and classification of implant systems, quantification of peri-implant bone loss, and 3-dimensional bone analysis, planning implant placement. CONCLUSIONS: The conventional criteria currently used as a technique for the diagnosis and monitoring of dental implants are insufficient and have low accuracy. Models that apply AI algorithms combined with precision methodologies-biomarkers-are extremely useful in the creation of precision medicine, allowing medical dentists to forecast the success of the implant. Tools that integrate the different types of data, including imaging, molecular, risk factor, and implant characteristics, are needed to make a more accurate and personalized prediction of implant success.