Implant Site ReactionNCT06029751
Dynamic Follow-up of Factors Influencing Implant Success and Models for Predicting Implant Outcomes
An observational study of Implant Site Reaction, run by The Dental Hospital of Zhejiang University School of Medicine at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Implant Site Reaction
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2017to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Patients aged 18 years and above;
- 1-5 years after implantation;
- Implantation torque > 35N·cm;
- Signed informed consent.
Who cannot join
- Contraindications of general implantation surgery;
- Have received head and neck radiation therapy;
- Past or current treatment with bisphosphonates;
- Do not cooperate with the interviewer.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
The Stomatologic Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, 310003
Yi Zhou, 87217419 ext. 0571
Siyao Ma, 87217419 ext. 0571
About this study
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.
Read the 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.
- Conditions
- Implant Site Reaction
- Being tested
- No intervention
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
No intervention
What is being measured
The primary outcome is the main question the study is designed to answer.
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)
Measured: 1-7 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Sep 2023
- Study start1 Jan 2017
- Primary completion31 Dec 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Implant Site ReactionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Dental Hospital of Zhejiang University School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yi Zhou, The Dental Hospital of Zhejiang University School of Medicine
- Investigators
- Weida Li, Principal investigator, Stomatological Hospital Affiliated to Zhejiang University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06029751
- Sponsor's protocol ID
- DHZhejiangU-2022(005)
- Status last verified
- Sep 2023
- First submitted
- 1 Sep 2023
- Last updated
- 19 Nov 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 27252014Raynaud 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.
PubMed 34756569Cetiner 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.
PubMed 34698722
Study at a glance
From the registry record, last updated by the sponsor on 19 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Dynamic Follow-up of Factors Influencing Implant Success and Models for Predicting Implant Outcomes
- ClinicalTrials.gov ID
- NCT06029751
- Acronym / sponsor ID
- DHZhejiangU-2022(005)
- Conditions
- Implant Site Reaction
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- The Dental Hospital of Zhejiang University School of MedicineUniversity, hospital or other
- Study start
- 1 Jan 2017 (actual)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 8 Sep 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- Patients aged 18 years and above;
- 1-5 years after implantation;
- Implantation torque > 35N·cm;
- Signed informed consent.
Key exclusion criteria (4 listed)
- Contraindications of general implantation surgery;
- Have received head and neck radiation therapy;
- Past or current treatment with bisphosphonates;
- Do not cooperate with the interviewer.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionNo intervention | Other | – |
Primary outcome
Mean Bone Level of dental implant
Time frame: 1-7 years
The vertical distance between the implant and the first contact area of bone and the tip of the implant (mesial and distal)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Nov 2024.
- Background: PubMed 27252014Papantonopoulos 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.
- Background: PubMed 34756569Raynaud 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.
- Background: PubMed 34698722Cetiner 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Weida Li principal investigator
Stomatological Hospital Affiliated to Zhejiang University School of Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Implant Site Reaction, run by The Dental Hospital of Zhejiang University School of Medicine at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 19 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yi Zhou (87217419 ext. 0571, zhouyizyzyzy@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mean Bone Level of dental implant (time frame: 1-7 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06029751, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Nov 2024.