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NCT06029751

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

Status unknown Last updated 19 November 2024
What this trial tests

trial testing No intervention in Implant Site Reaction in 1,000 participants. Status unknown.

Timeline
1 January 2017
Primary endpoint
31 December 2025
31 December 2025

Quick facts

Lead sponsorThe Dental Hospital of Zhejiang University School of Medicine
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment1,000
Start date1 January 2017
Primary completion31 December 2025
Estimated completion31 December 2025
Sites1 location across China

Drugs / interventions tested

Conditions studied

Sponsor

The Dental Hospital of Zhejiang University School of Medicine

Who can join

18 and older, any sex, with Implant Site Reaction. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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