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

artificial intelligence clinical decision support deep learning diabetic retinal disease implementation science machine learning nondiagnostic examination pupillary dilation

Journal

Journal of diabetes science and technology
ISSN: 1932-2968
Titre abrégé: J Diabetes Sci Technol
Pays: United States
ID NLM: 101306166

Informations de publication

Date de publication:
05 Oct 2023
Historique:
medline: 6 10 2023
pubmed: 6 10 2023
entrez: 6 10 2023
Statut: aheadofprint

Résumé

In 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 Retrospective 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). Of 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, We used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated

Identifiants

pubmed: 37798955
doi: 10.1177/19322968231201654
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

19322968231201654

Déclaration de conflit d'intérêts

Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RMW receives research funding from Dexcom, Inc. and Boehringer Ingelheim, unrelated to this research. MDA reports the following conflicts of interest: Investor, Director, Consultant, and Digital Diagnostics; patents and patent applications assigned to the University of Iowa and Digital Diagnostics that are relevant to the subject matter of this article; Chair Health care AI Coalition, Washington DC; member, American Academy of Ophthalmology (AAO) AI Committee; member, AI Workgroup Digital Medicine Payment Advisory Group (DMPAG). The remaining authors declare no competing financial or nonfinancial interests.

Auteurs

Benjamin L Shou (BL)

School of Medicine, The Johns Hopkins University, Baltimore, MD, USA.

Kesavan Venkatesh (K)

Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD, USA.

Chang Chen (C)

Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA.

Ronel Ghidey (R)

Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA.

Jae Hyoung Lee (JH)

Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA.

Jiangxia Wang (J)

Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA.

Roomasa Channa (R)

Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI, USA.

Risa M Wolf (RM)

Department of Pediatrics, Division of Pediatric Endocrinology, The Johns Hopkins University, Baltimore, MD, USA.

Michael D Abramoff (MD)

Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA.

T Y Alvin Liu (TYA)

Wilmer Eye Institute, The Johns Hopkins University, Baltimore, MD, USA.

Classifications MeSH