{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T11:03:51Z","timestamp":1791284631176,"version":"4.1.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ophthalmic Physiologic Optic"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The objective was to predict proliferative diabetic retinopathy (PDR) in non\u2010Hispanic Black (NHB) and Latino (LA) patients by applying machine learning algorithms to routinely collected blood and urine laboratory results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Electronic medical records of 1124 type 2 diabetes patients treated at the Bronxcare Hospital eye clinic between January and December 2019 were analysed. Data collected included demographic information (ethnicity, age and sex), blood (fasting glucose, haemoglobin A1C [HbA1c] high\u2010density lipoprotein [HDL], low\u2010density lipoprotein [LDL], serum creatinine and estimated glomerular filtration rate [eGFR]) and urine (albumin\u2010to\u2010creatinine ratio [ACR]) test results and the outcome measure of retinopathy status. The efficacy of different machine learning models was assessed and compared. SHapley Additive exPlanations (SHAP) analysis was employed to evaluate the contribution of each feature to the model's predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The balanced random forest model surpassed other models in predicting PDR for both NHB and LA cohorts, achieving an AUC (area under the curve) of 83%. Regarding sex, the model exhibited remarkable performance for the female LA demographic, with an AUC of 87%. The SHAP analysis revealed that PDR\u2010related factors influenced NHB and LA patients differently, with more pronounced disparity between sexes. Furthermore, the optimal cut\u2010off values for these factors showed variations based on sex and ethnicity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates the potential of machine learning in identifying individuals at higher risk for PDR by leveraging routine blood and urine test results. It allows clinicians to prioritise at\u2010risk individuals for timely evaluations. Furthermore, the findings emphasise the importance of accounting for both ethnicity and sex when analysing risk factors for PDR in type 2 diabetes individuals.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/opo.13363","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T04:30:24Z","timestamp":1720758624000},"page":"1549-1567","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of proliferative diabetic retinopathy using machine learning in Latino and non\u2010Hispanic black cohorts with routine blood and urine testing"],"prefix":"10.1007","volume":"45","author":[{"given":"Ayelet","family":"Goldstein","sequence":"first","affiliation":[{"name":"Department of Computer Science Hadassah Academic College  Jerusalem Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology Bronxcare Health Center  Bronx New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Onelys","family":"Carasquillo","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology Bronxcare Health Center  Bronx New York USA"},{"name":"Prado Vision  Tampa Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barton","family":"Levine","sequence":"additional","affiliation":[{"name":"Department of Nephrology West Los Angeles VA Medical Center  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aisha","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology Bronxcare Health Center  Bronx New York USA"},{"name":"Prism Vision Group  Paramus New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3805-5045","authenticated-orcid":false,"given":"Jonathan","family":"Levine","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology Bronxcare Health Center  Bronx New York USA"},{"name":"Department of Optometry Hadassah Academic College  Jerusalem Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"Diabetes. 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