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Predicting Local Anesthetic Success in Symptomatic Irreversible Pulpitis: A Machine Learning Study

NCT07672678 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 4390

Last updated 2026-06-29

No results posted yet for this study

Summary

This study will develop and internally validate three machine learning models - logistic regression, random forest, and XGBoost - to predict local anesthetic (LA) success in patients undergoing endodontic treatment for symptomatic irreversible pulpitis (SIP). A large retrospective cohort of 4,390 consecutive adult patients treated at a single center (May 2014-October 2025) is being analyzed. The dataset was frozen in October 2025 for this analysis.

Conditions

  • Pulpitis
  • Nerve Block
  • Anesthesia, Local

Sponsors & Collaborators

  • Jamia Millia Islamia

    lead OTHER

Principal Investigators

  • Vivek Aggarwal · Jamia Millia Islamia

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2014-05-01
Primary Completion
2025-10-16
Completion
2025-12-16

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07672678 on ClinicalTrials.gov