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

MMPro-HIP: multimodal progressive fusion model for elderly HIP fracture risk prediction.

Frontiers in medicine·2026
Same author

Partial Multi-Label Feature Selection via Entropy-Weighted Multi-Scale Neighborhood Granular Label Distribution Learning.

Entropy (Basel, Switzerland)·2026
Same author

[Exploration and clinical application of the "digital and intelligent surgery" diagnosis and treatment workflow for oral and maxillofacial tumors].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences·2026
Same author

Interpreting Performance of Deep Neural Networks with Partial Information Decomposition.

Entropy (Basel, Switzerland)·2026
Same author

Accuracy of dynamic navigation for immediate and delayed anterior dental implant placement: a retrospective study.

BMC oral health·2025
Same author

Weighted Gene Networks Derived from Multi-Omics Reveal Core Cancer Genes in Lung Cancer.

Biology·2025
Same journal

Family cohesion and adaptability in heart failure: an APIMeM analysis of symptom perception and spousal caregiving on patient self-care.

Frontiers in medicine·2026
Same journal

Prognostic factors analysis of surgical resection after conversion therapy for isolated pleural metastatic lung cancer: a retrospective analysis.

Frontiers in medicine·2026
Same journal

Case Report: a novel non-canonical splice site variant in COL4A5 in a patient with Alport syndrome.

Frontiers in medicine·2026
Same journal

Minimally invasive percutaneous cannulated screw fixation for pelvic fractures: a retrospective case cohort study of clinical and radiological outcomes.

Frontiers in medicine·2026
Same journal

A case analysis of Gitelman syndrome complicated with Sjögren's disease.

Frontiers in medicine·2026
Same journal

Inflammatory phenotyping by latent class analysis and machine learning-based prediction of postoperative complications in pediatric appendicitis: a retrospective cohort study.

Frontiers in medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 10, 2026

The Establishment of a Murine Mandibular Molar Extraction Socket Healing Model
04:19

The Establishment of a Murine Mandibular Molar Extraction Socket Healing Model

Published on: January 13, 2023

5.4K

A data-driven method for surgeon-specific difficulty assessment in third molar extraction.

Chun Kang1,2, Ziyu Yan3, Xiya Xiong1,2

  • 1School of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China.

Frontiers in Medicine
|November 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a data-driven method to assess impacted wisdom teeth extraction difficulty for junior doctors. The model identifies key difficulty factors and learning curves, improving surgical training and assessment.

Keywords:
data-decouplingdifficulty assessmentimpacted mandibular third molarsmachine learningtooth extraction

More Related Videos

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
10:42

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

6.9K
Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities
07:14

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

5.0K

Related Experiment Videos

Last Updated: Jan 10, 2026

The Establishment of a Murine Mandibular Molar Extraction Socket Healing Model
04:19

The Establishment of a Murine Mandibular Molar Extraction Socket Healing Model

Published on: January 13, 2023

5.4K
A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
10:42

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

6.9K
Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities
07:14

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

5.0K

Area of Science:

  • Oral and Maxillofacial Surgery
  • Medical Education Technology
  • Data Science in Healthcare

Background:

  • Assessing surgical difficulty in impacted wisdom teeth extraction is crucial for training junior doctors.
  • Existing difficulty scales may not fully capture the nuances of procedural complexity.
  • Objective analysis of procedural difficulty can enhance surgical training effectiveness.

Purpose of the Study:

  • To develop a data-driven method for analyzing the time and factors influencing lower wisdom teeth extraction difficulty.
  • To establish a mathematical model for procedural difficulty and evaluate existing scales.
  • To provide difficulty indicators for impacted teeth extraction training for junior doctors.

Main Methods:

  • Collected surgical records from 419 lower impacted wisdom teeth extractions performed by 9 residents.
  • Proposed a data-driven surgeon-specific difficulty assessment (DDSS) method using Lasso regression.
  • Classified doctors into training grades and used pre-trained models for targeted difficulty prediction.

Main Results:

  • The DDSS method achieved 80% accuracy and 0.85 AUC with Support Vector Machines (SVM).
  • Inexperienced surgeons were influenced by more factors, while experienced surgeons focused on four key factors: Crown resistance, impaction type, mouth opening, and gender.
  • Learning curves indicated surgical proficiency is typically reached after 8 months of practice.

Conclusions:

  • A data-driven decoupling-prediction model enhances dental surgery difficulty assessment.
  • The proposed method offers a new perspective for surgical difficulty assessment and surgeon training.
  • The study provides reliable conclusions and learning curves for novice surgeons.