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

NCT06029751 | Unknown

• 1 other identifier: DHZhejiangU-2022(005)
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Implant Site Reaction

Outcome Measures

Primary Outcomes (1)

• 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

Interventions

No intervention OTHER

No intervention

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• The Dental Hospital of Zhejiang University School of Medicine: lead

Study Sites (1)

The Stomatologic Hospital, School of Medicine, Zhejiang University

Hangzhou, Zhejiang, 310003, China

RECRUITING

Related Publications (3)

• 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.

  PMID: 27252014 BACKGROUND

• 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.

  PMID: 34756569 BACKGROUND

• 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.

  PMID: 34698722 BACKGROUND

Study Officials

• Weida Li

  Stomatological Hospital Affiliated to Zhejiang University School of Medicine

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Yi Zhou

CONTACT

87217419; zhouyizyzyzy@163.com

Siyao Ma

CONTACT

87217419; 1123348672@qq.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Deputy Chief Physician, Deputy Director of Dental Implant Department

Study Record Dates

• First Submitted: September 1, 2023
• First Posted: September 8, 2023
• Study Start: January 1, 2017
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: November 19, 2024

Record last verified: 2023-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)