{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T13:50:29Z","timestamp":1777557029436,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T00:00:00Z","timestamp":1696464000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>\n                      In the pivotal clinical trial that led to Food and Drug Administration De Novo \u201capproval\u201d 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\n                      <jats:italic toggle=\"yes\">predictive dilation<\/jats:italic>\n                      workflow, where patients most likely to benefit from pharmacologic dilation are dilated\n                      <jats:italic toggle=\"yes\">a priori<\/jats:italic>\n                      to maximize efficiency and patient satisfaction.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      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).\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .05 was considered statistically significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      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,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .01), smoking (aOR = 2.86, 95% CI: 1.36-5.99,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .005), and age (every 10-year increase, aOR = 2.12, 95% CI: 1.62-2.77,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001) were associated with nondiagnostic results. Following feature elimination, a predictive model was created using T1D, smoking, age, race, sex, and hypertension as inputs. The model showed an area under the receiver-operator characteristics curve of 0.76 in five-fold cross-validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic toggle=\"yes\">a priori<\/jats:italic>\n                      . This new workflow has the potential to be more efficient than reflexive dilation, thus maximizing the number of at-risk patients receiving their diabetic retinal examinations.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968231201654","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T02:22:31Z","timestamp":1696558951000},"page":"302-308","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["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"],"prefix":"10.1177","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2825-3301","authenticated-orcid":false,"given":"Benjamin L.","family":"Shou","sequence":"first","affiliation":[{"name":"School of Medicine, The Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kesavan","family":"Venkatesh","sequence":"additional","affiliation":[{"name":"Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Chen","sequence":"additional","affiliation":[{"name":"Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronel","family":"Ghidey","sequence":"additional","affiliation":[{"name":"Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6303-086X","authenticated-orcid":false,"given":"Jae Hyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangxia","family":"Wang","sequence":"additional","affiliation":[{"name":"Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roomasa","family":"Channa","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Risa M.","family":"Wolf","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Division of Pediatric Endocrinology, The Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3490-0037","authenticated-orcid":false,"given":"Michael D.","family":"Abramoff","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T. Y. Alvin","family":"Liu","sequence":"additional","affiliation":[{"name":"Wilmer Eye Institute, The Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2023,10,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.2337\/DIACARE.26.1.226"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.OPHTHA.2021.04.027"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1001\/ARCHOPHTHALMOL.2010.319"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1136\/BMJDRC-2016-000333"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.AMEPRE.2005.08.010"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0040-6"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1111\/AOS.14132"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.AJO.2005.05.051"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1080\/09286586.2016.1274039"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1111\/AOS.13613"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.2337\/DC18-0148"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1177\/1932296820906212"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/JCM10112352"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376718"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0002-9394(14)72724-2"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1258\/JRSM.96.6.266"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.88.2.562"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.28.10.2448"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1097\/OPX.0000000000000893"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.2337\/DC20-1671"}],"container-title":["Journal of Diabetes Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968231201654","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/19322968231201654","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968231201654","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T18:16:39Z","timestamp":1777400199000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/19322968231201654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,5]]},"references-count":20,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.1177\/19322968231201654"],"URL":"https:\/\/doi.org\/10.1177\/19322968231201654","relation":{},"ISSN":["1932-2968","1932-2968"],"issn-type":[{"value":"1932-2968","type":"print"},{"value":"1932-2968","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,5]]}}}