Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Multi-Modular Human-AI Workflow for LLM-Assisted Thematic Analysis: Application to COPD Telerehabilitation Interviews.

Studies in health technology and informatics·2026
Same author

Association of Remote Patient Monitoring with Care Utilization in Patients with Chronic Cardiopulmonary Conditions.

Studies in health technology and informatics·2026
Same author

Machine Learning Approaches for Mortality Prediction in ARDS.

Studies in health technology and informatics·2026
Same author

Estimating Mosaic Loss of the Y Chromosome in Male Bladder Cancer Participants Using "All of Us" Data.

Studies in health technology and informatics·2026
Same author

Early Prediction of Delirium in ICU Patients Using Machine Learning Analysis of Admission Data.

Studies in health technology and informatics·2026
Same author

Multimodule Human-Artificial Intelligence Collaboration Pipeline for Large Language Model-Assisted Thematic Analysis Across Digital Health Interview Studies: Comparative Evaluation Study.

JMIR medical informatics·2026

Related Experiment Video

Updated: Dec 30, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.5K

Implant Failure Prediction Using Discriminant Analysis.

In Cheol Jeong, Panos N Papapanou, Joseph Finkelstein

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Predicting dental implant failure is possible using electronic dental records (EDR). Key factors include implant type, pre-operative antibiotics, patient

    More Related Videos

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.3K
    Experimental Model of Ligature-Induced Peri-Implantitis in Mice
    05:37

    Experimental Model of Ligature-Induced Peri-Implantitis in Mice

    Published on: May 17, 2024

    3.1K

    Related Experiment Videos

    Last Updated: Dec 30, 2025

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.5K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.3K
    Experimental Model of Ligature-Induced Peri-Implantitis in Mice
    05:37

    Experimental Model of Ligature-Induced Peri-Implantitis in Mice

    Published on: May 17, 2024

    3.1K

    Area of Science:

    • Dentistry
    • Biostatistics
    • Health Informatics

    Background:

    • Electronic dental records (EDR) offer extensive clinical data for dental care analysis.
    • Analyzing implant survival and failure determinants is crucial for improving patient outcomes.

    Purpose of the Study:

    • To identify determinants of dental implant survival.
    • To develop an implant failure prediction model using EDR data.
    • To leverage routinely available clinical data for predictive modeling.

    Main Methods:

    • Retrospective analysis of 800 dental implants using discriminant analysis.
    • Compared intact and failed implant characteristics using statistical tests (Goodman and Kruskal's lambda, point-biserial, chi-square, ANOVA).
    • Stepwise discriminant analysis reduced 19 variables to a 4-feature model.

    Main Results:

    • The final model included: non-temporary implant, pre-op antibiotics, immunocompromised status, and gender.
    • The discriminant function correctly identified 72% of implant failures and 62% of intact implants.
    • Predictive features are readily available in EDR.

    Conclusions:

    • A predictive model for dental implant failure can be developed using EDR data.
    • The model identifies key patient and treatment characteristics associated with implant success or failure.
    • Integration into EDR can support clinical decision-making and personalized patient care.