MedTrace Logo

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

NCT06029751 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2024-11-19

No results posted yet for this study

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

Interventions

OTHER

No intervention

No intervention

Sponsors & Collaborators

  • The Dental Hospital of Zhejiang University School of Medicine

    lead OTHER

Principal Investigators

  • Weida Li · Stomatological Hospital Affiliated to Zhejiang University School of Medicine

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2017-01-01
Primary Completion
2025-12-31
Completion
2025-12-31

Countries

  • China

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06029751 on ClinicalTrials.gov