Implant Site Reaction Clinical Trial
Official title:
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.
Status | Recruiting |
Enrollment | 1000 |
Est. completion date | December 31, 2025 |
Est. primary completion date | December 31, 2025 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - (1) Patients aged 18 years and above; (2) 1-5 years after implantation; (3) Implantation torque > 35N·cm; (4) Signed informed consent. Exclusion Criteria: - (1) Contraindications of general implantation surgery; (2) Have received head and neck radiation therapy; (3) Past or current treatment with bisphosphonates; (4) Do not cooperate with the interviewer. |
Country | Name | City | State |
---|---|---|---|
China | The Stomatologic Hospital, School of Medicine, Zhejiang University | Hangzhou | Zhejiang |
Lead Sponsor | Collaborator |
---|---|
The Dental Hospital of Zhejiang University School of Medicine |
China,
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. — View Citation
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. — View Citation
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. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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 |
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