AIMC Topic: Tooth Loss

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Impact of tooth loss and patient characteristics on coronary artery calcium score classification and prediction.

Scientific reports Nov 16, 2024
This study, for the first time, explores the integration of data science and machine learning for the classification and prediction of coronary artery calcium (CAC) scores. It focuses on tooth loss and patient characteristics as key input features to...
Vascular Calcification Tooth Loss Female Coronary Artery Disease Calcium Risk Factors Aged Coronary Vessels Adult Male Middle Aged Humans Machine Learning
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Feasibility of using two generative AI models for teeth reconstruction.

Journal of dentistry Oct 16, 2024
OBJECTIVES: This feasibility study investigates the application of artificial intelligence (AI) models, specifically transformer-based (TM) and diffusion-based (DM) models, for the reconstruction of single and multiple missing teeth.
Humans Tooth Models, Dental Tooth Loss Artificial Intelligence Image Processing, Computer-Assisted Feasibility Studies
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Exploring the accuracy of tooth loss prediction between a clinical periodontal prognostic system and a machine learning prognostic model.

Journal of clinical periodontology Aug 7, 2024
AIM: The aim of this analysis was to compare a clinical periodontal prognostic system and a developed and externally validated artificial intelligence (AI)-based model for the prediction of tooth loss in periodontitis patients under supportive period...
Prognosis Female Aged Humans Neural Networks, Computer Machine Learning Middle Aged Cohort Studies Logistic Models Adult Sensitivity and Specificity Male Tooth Loss Predictive Value of Tests Periodontitis
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Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning.

Scientific reports Jun 16, 2024
Recent studies have shown that dental implants have high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net m...
Cone-Beam Computed Tomography Imaging, Three-Dimensional Dental Implants Patient Care Planning Dental Implantation Tooth Loss Humans Deep Learning
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Classification of presence of missing teeth in each quadrant using deep learning artificial intelligence on panoramic radiographs of pediatric patients.

The Journal of clinical pediatric dentistry May 3, 2024
Early tooth loss in pediatric patients can lead to various complications, making quick and accurate diagnosis essential. This study aimed to develop a novel deep learning model for classification of missing teeth on panoramic radiographs in pediatric...
Tooth Loss Child Radiography, Panoramic Retrospective Studies Female Humans Sensitivity and Specificity Artificial Intelligence Male Deep Learning Adolescent
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Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach.

Journal of dentistry Mar 2, 2024
OBJECTIVES: This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting.
Adult Male Middle Aged Cohort Studies ROC Curve Periodontitis Female Risk Factors Aged Humans Furcation Defects Phenotype Area Under Curve Machine Learning Tooth Mobility Algorithms Electronic Health Records Tooth Loss
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Accuracy and safety of a haptic operated and machine vision controlled collaborative robot for dental implant placement: A translational study.

Clinical oral implants research Jun 12, 2023
OBJECTIVES: Multiple generations of medical robots have revolutionized surgery. Their application to dental implants is still in its infancy. Co-operating robots (cobots) have great potential to improve the accuracy of implant placement, overcoming t...
Surgery, Computer-Assisted Humans Dental Implants Computer-Aided Design Mouth, Edentulous Cone-Beam Computed Tomography Haptic Technology Imaging, Three-Dimensional Dental Implantation, Endosseous Robotics Tooth Loss
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Development and international validation of logistic regression and machine-learning models for the prediction of 10-year molar loss.

Journal of clinical periodontology Dec 11, 2022
AIM: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients.
Periodontitis Bayes Theorem Humans Artificial Intelligence Tooth Loss Neural Networks, Computer Molar Machine Learning Logistic Models
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