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.
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. ;
Status | Clinical Trial | Phase | |
---|---|---|---|
Not yet recruiting |
NCT04092920 -
Implant Stability of Laser vs SLA Surface Treated Implants Placed in Fresh Extraction Sockets
|
N/A | |
Not yet recruiting |
NCT05973357 -
The Influence of Vertical Implant Position With Immediate Provisionalization on the Marginal Bone Loss.
|
N/A | |
Enrolling by invitation |
NCT05675241 -
Characterizing the Inflammation Around Dental Implants
|
||
Not yet recruiting |
NCT03598049 -
Assessment of Dental Implants Placed in Posterior Maxillary Ridge Using Densah Burs Versus Standard Drills
|
N/A | |
Completed |
NCT05730400 -
Histological Assessment of BMAC Utilized in Sinus Lift
|
N/A | |
Completed |
NCT05999760 -
Retention, Chewing Efficiency and Masticatory Performance of Partial Dentures Opposing Implant Retained Prosthesis.
|
N/A | |
Active, not recruiting |
NCT06020040 -
Bone Particles Sizes and the Stability of Soft and Hard Tissue in Aesthetic Area
|
N/A | |
Completed |
NCT06468592 -
Implantation in Posterior Maxilla in Cases With Insufficient Bone
|
N/A | |
Recruiting |
NCT06022042 -
Clinical Comparison of On1 Two-stage Abutment With One-stage Abutment on Different Implant Neck Design
|
N/A | |
Completed |
NCT06146244 -
Comparison of ISQ in Implants Placed in Antral Area on Native Bone vs Regenerated Bone
|
||
Completed |
NCT05936775 -
Assessment of Osseointegration Properties of Nano-Hydroxy Apatite Coated Titanium Implant
|
N/A | |
Recruiting |
NCT06164353 -
the Peri-implant Tissue Changes Around Implants in the Esthetic Zone Using Demineralized Dentin Graft vs Xenograft
|
N/A | |
Not yet recruiting |
NCT06446687 -
Radiographic Assessment of Bone Gain Following Sinus Lifting With Simultaneous Implant Placement Using Crestal Approach With Membrane Control Technique for Bone Augmentation of Atrophied Maxillary Posterior Ridge
|
N/A | |
Completed |
NCT05187143 -
Results of a New Fully Tapered Implant at One Year
|
||
Recruiting |
NCT06043037 -
Elamrousy Modified Approach for Socket Shield Technique
|
N/A | |
Recruiting |
NCT05817526 -
BMAC Loaded Collagen Jumping the Gap Around Immediate Implants
|
N/A | |
Completed |
NCT04230837 -
Marginal Bone Level Around Implants With Definitive Abutments
|
N/A | |
Completed |
NCT03305679 -
Clinical Efficacy of the Immediate Implant Loading
|
N/A | |
Completed |
NCT04332185 -
Vestibular Socket Therapy in Compromised Sockets
|
N/A | |
Recruiting |
NCT05057143 -
3D Printed Implants for the Defect Reconstruction in Patients With Chest Wall Tumors
|
N/A |