Dynamic Follow-up of Factors Influencing Implant Success and Models for Predicting Implant Outcomes

Nowadays, artificial intelligence technology with machine learning as the main means has been increasingly applied to the oral field, and has played an increasingly important role in the examination, diagnosis, treatment and prognosis assessment of oral diseases. Among them, machine learning is an important branch of artificial intelligence, which refers to the system learning specific statistical patterns in a given data set to predict the behavior of new data samples [8]. Machine learning is divided into two main categories: Supervised learning and Unsupervised learning. Whether there is supervision depends on whether the data entered is labeled or not. If the input data is labeled, it is supervised learning. Unlabeled learning is unsupervised. Supervised learning is a kind of learning algorithm when the correct output of the data set is known. Because the input and output are known, it means that there is a relationship between the input and output, and the supervised learning algorithm is to discover and summarize this "relationship". Unsupervised learning refers to a class of learning algorithms for unlabeled data. The absence of label information means that patterns or structures need to be discovered and summarized from the data set.

Study Overview

• Status: Recruiting
• Conditions: Implant Site Reaction
• Intervention / Treatment: Other: No intervention

Detailed Description

Starting from different data types, researchers built a variety of models to mine the data itself and predict the prognosis of the implant. Machine learning is often more impressive and intuitive in terms of images. In the field of oral implantology, researchers analyze preoperative image data based on machine learning to identify important anatomical structures (such as maxillary sinus, mandibular neural tube, etc.) and analyze alveolar bone quality. Large-scale imaging data is also used to identify the different implant systems on the market. Machine learning also plays an important role in the development of implant surgery plans, which is conducive to more accurate and efficient implantation surgery. The evaluation of implant retention rate and individual bone level is also one of the key clinical concerns. Most methods to study such issues are: Kaplan-Meier survival analysis, Cox survival analysis, etc., to study implant retention rate and influencing factors. Linear (mixed) model and multiple logistic regression were used to study the changes and influencing factors of bone absorption at implant edge. However, in daily clinical practice, there may be some practical problems such as lost follow-up and partial data missing. As the clinical scenarios of research become more and more clear, even partial data missing often leads to results that cannot be accurately evaluated and predicted. Therefore, in terms of supervised learning, this study aims to establish a predictive model of implant bone level change and evaluate the accuracy of the model through machine learning of implant edge bone level (MBL) with large amounts of data. In terms of unsupervised learning, the aim is to identify susceptibility phenotypes to implant failure through: clustering of individual-related information about implants.

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Yi Zhou; Phone Number: 0571 87217419; Email: zhouyizyzyzy@163.com
• Study Contact Backup: Name: Siyao Ma; Phone Number: 0571 87217419; Email: 1123348672@qq.com

Study Locations

• China
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310003
      • Recruiting
      • The Stomatologic Hospital, School of Medicine, Zhejiang University
      • Contact: Yi Zhou; Phone Number: 0571 87217419
      • Contact: Siyao Ma; Phone Number: 0571 87217419

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

This was a retrospective study. After searching the literature identified by the sample size of the combined model prediction method, it was determined that the initial data and follow-up data of 1000 patients were required.

Description

Inclusion Criteria:

• Patients aged 18 years and above;
• 1-5 years after implantation;
• Implantation torque > 35N·cm;
• Signed informed consent.

Exclusion Criteria:

• Contraindications of general implantation surgery;
• Have received head and neck radiation therapy;
• Past or current treatment with bisphosphonates;
• Do not cooperate with the interviewer.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Mean Bone Level of dental implant	The vertical distance between the implant and the first contact area of bone and the tip of the implant (mesial and distal)	1-7 years

Collaborators and Investigators

• Sponsor: The Dental Hospital of Zhejiang University School of Medicine
• Investigators: Principal Investigator: Weida Li, Stomatological Hospital Affiliated to Zhejiang University School of Medicine

Publications and helpful links

General Publications

• Papantonopoulos G, Gogos C, Housos E, Bountis T, Loos BG. Prediction of individual implant bone levels and the existence of implant "phenotypes". Clin Oral Implants Res. 2017 Jul;28(7):823-832. doi: 10.1111/clr.12887. Epub 2016 Jun 1.
• Raynaud M, Aubert O, Divard G, Reese PP, Kamar N, Yoo D, Chin CS, Bailly E, Buchler M, Ladriere M, Le Quintrec M, Delahousse M, Juric I, Basic-Jukic N, Crespo M, Silva HT Jr, Linhares K, Ribeiro de Castro MC, Soler Pujol G, Empana JP, Ulloa C, Akalin E, Bohmig G, Huang E, Stegall MD, Bentall AJ, Montgomery RA, Jordan SC, Oberbauer R, Segev DL, Friedewald JJ, Jouven X, Legendre C, Lefaucheur C, Loupy A. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study. Lancet Digit Health. 2021 Dec;3(12):e795-e805. doi: 10.1016/S2589-7500(21)00209-0. Epub 2021 Oct 28.
• Cetiner D, Isler SC, Bakirarar B, Uraz A. Identification of a Predictive Decision Model Using Different Data Mining Algorithms for Diagnosing Peri-implant Health and Disease: A Cross-Sectional Study. Int J Oral Maxillofac Implants. 2021 Sep-Oct;36(5):952-965. doi: 10.11607/jomi.8965.

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2017
• Primary Completion (Estimated): December 31, 2025
• Study Completion (Estimated): December 31, 2025

Study Registration Dates

• First Submitted: September 1, 2023
• First Submitted That Met QC Criteria: September 1, 2023
• First Posted (Actual): September 8, 2023

Study Record Updates

• Last Update Posted (Estimated): November 19, 2024
• Last Update Submitted That Met QC Criteria: November 16, 2024
• Last Verified: September 1, 2023

More Information

Terms related to this study

• Other Study ID Numbers: DHZhejiangU-2022(005)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No