Evidence map›Paper›PMID 38904996›Full record

ArticleJMIR medical informatics2024

Dermoscopy Differential Diagnosis Explorer (D3X) Ontology to Aggregate and Link Dermoscopic Patterns to Differential Diagnoses: Development and Usability Study.

Rebecca Z Lin, Muhammad Tuan Amith, Cynthia X Wang, John Strickley, Cui Tao

Abstract read
In one paragraph

Article in JMIR medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Rebecca Z LinDivision of Dermatology, Washington University School of Medicine, St. Louis, MO, United States.ORCID https://orcid.org/0000-0002-1469-0298
Muhammad Tuan AmithDepartment of Information Science, University of North Texas, Denton, TX, United States.ORCID https://orcid.org/0000-0003-4333-1857
Cynthia X WangDepartment of Dermatology, Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States.ORCID https://orcid.org/0000-0001-7664-6033
John StrickleyDivision of Dermatology, University of Louisville, Louisville, KY, United States.ORCID https://orcid.org/0000-0003-2567-7424
Cui TaoDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, United States.ORCID https://orcid.org/0000-0002-4267-1924

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
Advancing Cancer Pharmacoepidemiology Research Through EHRs and InformaticsU24CA194215 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI, YANG, PING · 2016 to 2020
$3.3M
NCI NIH HHS U24 CA194215NIAID NIH HHS R01 AI130460
6 · The paper itself

Abstract

backgroundDermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand.

objectiveIn this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses.

methodsExisting ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers.

resultsD3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain.

conclusionsThe D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice.

Indexed as

biomedical ontologydermatologydermoscopicdermoscopydermoscopy differential diagnosis explorerdiagnosesdiagnosisdiagnosticdifferential diagnosisinformation retrievalinformation storageinformation storage and retrievalmedical informaticsontologiesontologyOWLsemioticskinskin diseasesskin lesionvisualvisualizationvocabularyweb appweb applicationweb ontology language

Identifiers

PMID38904996
PMCPMC11226929

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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.