Evidence map›Paper›PMID 38533395›Full record

ArticleMedical image computing and computer assisted intervention - MICCAI 2023 workshops : ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8-12, 2023, proceedings2023

Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models Using Biological Privileged Information.

Najla Al Turkestani, Lingrui Cai, Lucia Cevidanes, Jonas Bianchi, Winston Zhang, Marcela Gurgel, Maxime Gillot, Baptiste Baquero, Reza Soroushmehr

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image computing and computer assisted intervention - MICCAI 2023 workshops : ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8-12, 2023, proceedings, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

9 authors.

Najla Al TurkestaniDepartment of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA.
Lingrui CaiDepartment of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.
Lucia CevidanesDepartment of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA.
Jonas BianchiDepartment of Orthodontics, University of the Pacific, Arthur A. Dugoni School of Dentistry, 155 5th Street, San Francisco, CA 94103, USA.
Winston ZhangDepartment of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.
Marcela GurgelDepartment of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA.
Maxime GillotDepartment of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA.
Baptiste BaqueroDepartment of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA.
Reza SoroushmehrDepartment of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.

Funding

Quantification of 3D Bony Changes in Temporomandibular Joint OsteoarthritisR01DE024450 · NIDCR · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Lucia H Cevidanes · 2013 to 2026
$5.5M
NIDCR NIH HHS R01 DE024450
6 · The paper itself

Abstract

This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.

Indexed as

Feature selectionLearning using privileged informationMachine learningOsteoarthritisTemporomandibular joint

Identifiers

PMID38533395
PMCPMC10964798

What OpenQuestion holds

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Registered trials

None linked

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.