Evidence map›Paper›PMID 37798955›Full record

ArticleJournal of diabetes science and technology2024

Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Healthcare System: Maximizing Workflow Efficiency Through Predictive Dilation.

Benjamin L Shou, Kesavan Venkatesh, Chang Chen, Ronel Ghidey, Jae Hyoung Lee, Jiangxia Wang, Roomasa Channa, Risa M Wolf, Michael D Abramoff, T Y Alvin Liu

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes science and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 21% of its field
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

3 citing papers in PubMed, 5 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Benjamin L ShouSchool of Medicine, The Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0003-2825-3301
Kesavan VenkateshWhiting School of Engineering, The Johns Hopkins University, Baltimore, MD, USA.
Chang ChenBloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA.
Ronel GhideyBloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA.
Jae Hyoung LeeBloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0002-6303-086X
Jiangxia WangBloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA.
Roomasa ChannaDepartment of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI, USA.
Risa M WolfDepartment of Pediatrics, Division of Pediatric Endocrinology, The Johns Hopkins University, Baltimore, MD, USA.
Michael D AbramoffDepartment of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA.ORCID 0000-0002-3490-0037
T Y Alvin LiuWilmer Eye Institute, The Johns Hopkins University, Baltimore, MD, USA.
Johns Hopkins University · USUniversity of Iowa · USUniversity of Wisconsin–Madison · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIn the pivotal clinical trial that led to Food and Drug Administration De Novo "approval" of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel

methodsRetrospective review of patients who were assessed with autonomous AI at Johns Hopkins Medicine (8/2020 to 5/2021). We constructed a multivariable logistic regression model for nondiagnostic results to compare characteristics of patients with and without diagnostic results, using adjusted odds ratio (aOR).

resultsOf 241 patients (59% female; median age = 59), 123 (51%) had nondiagnostic results. In multivariable analysis, type 1 diabetes (T1D, aOR = 5.82, 95% confidence interval [CI]: 1.45-23.40,

conclusionsWe used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated

Indexed as

Delivery of Health Care, IntegratedDiabetes Mellitus, Type 1Diabetic RetinopathyArtificial IntelligenceClinical Trials as TopicDilatationFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsUnited StatesWorkflowartificial intelligenceclinical decision supportdeep learningdiabetic retinal diseaseimplementation sciencemachine learningnondiagnostic examinationpupillary dilation

Identifiers

PMID37798955
PMCPMC10973867
OpenAlexW4387392205

What OpenQuestion holds

Textmetadata
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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.