Evidence map›Paper›PMID 39754714›Full record

ArticleJournal of prosthodontics : official journal of the American College of Prosthodontists2025

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

Islam E Ali, Mariko Hattori, Yuka Sumita, Noriyuki Wakabayashi

Abstract read
In one paragraph

Article in Journal of prosthodontics : official journal of the American College of Prosthodontists, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Automated design prediction for definitive obturator prostheses: A case-based reasoning study.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Islam E AliDepartment of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0001-5247-1769
Mariko HattoriDepartment of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0003-2802-8016
Yuka SumitaDepartment of Partial and Complete Denture, School of Life Dentistry, The Nippon Dental University, Tokyo, Japan.ORCID https://orcid.org/0000-0003-3982-8369
Noriyuki WakabayashiDepartment of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0002-0517-6756

Funding

Tokyo Metropolitan Government 2023-2025
6 · The paper itself

Abstract

purposeThis study aims to evaluate the effectiveness of a case-based reasoning (CBR) system in predicting the design of definitive obturator prostheses for maxillectomy patients. MATERIALS AND

methodsData from 209 maxillectomy cases, including extraoral images of obturator prostheses and occlusal images of maxillectomy defects, were collected from Institute of Science Tokyo Hospital. These cases were organized into a structured database using Python's pandas library. The CBR system was designed to match new cases with similar historical cases based on specific attributes such as aramany class, abutment details, defect extension, and oronasal connection size. The system's performance was evaluated by clinicians who assessed the accuracy of prosthesis designs generated for 33 test cases.

resultsA correlation analysis demonstrated a significant positive relationship (ρ = 0.84, p < 0.0001) between the CBR system's confidence scores and the number of correct prosthesis designs identified by clinicians. The median precision at five cases was 0.8, indicating that the system effectively retrieved relevant designs for new cases.

conclusionsThe study shows that the developed CBR system effectively predicts the design of obturator prostheses for maxillectomy patients. Clinically, the system is expected to reduce clinician workload, simplify the design process, and enhance patient engagement by providing prompt insights into their final prosthetic design.

Indexed as

Dental Prosthesis DesignPalatal ObturatorsProsthesis DesignHumansMaxillaartificial intelligenceclinical reasoningdenture designmaxillectomymaxillofacial prosthodonticsobturator prosthesis

Identifiers

PMID39754714
PMCPMC12147418

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.