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In vitro evaluation of color stability of non-pigmented maxillofacial silicone elastomer immersed in condiments.

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Updated: May 3, 2026

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Automated design prediction for definitive obturator prostheses: A case-based reasoning study.

Islam E Ali1,2, Mariko Hattori1, Yuka Sumita3,4

  • 1Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan.

Journal of Prosthodontics : Official Journal of the American College of Prosthodontists
|January 4, 2025
PubMed
Summary

This study developed a case-based reasoning (CBR) system to predict obturator prosthesis designs for maxillectomy patients. The system accurately matched historical cases, simplifying the design process and reducing clinician workload.

Keywords:
artificial intelligenceclinical reasoningdenture designmaxillectomymaxillofacial prosthodonticsobturator prosthesis

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Area of Science:

  • Biomedical Engineering
  • Dental Prosthetics
  • Artificial Intelligence in Healthcare

Background:

  • Maxillectomy surgery often requires obturator prostheses to restore function and aesthetics.
  • Designing these prostheses can be complex and time-consuming for clinicians.
  • Predictive systems can aid in streamlining the design process.

Purpose of the Study:

  • To evaluate the effectiveness of a case-based reasoning (CBR) system for predicting obturator prosthesis designs.
  • To assess the system's ability to match new maxillectomy cases with historical data.
  • To determine the clinical utility of a CBR system in maxillofacial prosthodontics.

Main Methods:

  • A database of 209 maxillectomy cases was created using patient images and clinical data.
  • A CBR system was developed to identify similar past cases based on defect and prosthesis characteristics.
  • Clinicians evaluated the accuracy of prosthesis designs generated by the CBR system for 33 test cases.

Main Results:

  • A significant positive correlation (ρ = 0.84, p < 0.0001) was found between system confidence and design accuracy.
  • The system achieved a median precision of 0.8 at five cases, indicating effective retrieval of relevant designs.
  • Clinician assessments confirmed the system's ability to predict appropriate prosthesis designs.

Conclusions:

  • The developed CBR system effectively predicts obturator prosthesis designs for maxillectomy patients.
  • The system has the potential to reduce clinician workload and simplify the prosthetic design process.
  • Enhanced patient engagement is anticipated through faster insights into prosthetic design options.