c5cd949fcb0acce55787b484fcd6ec04 healthcare-14-00334-v2.pdf b364d7edd45ed39c084d562f16a7e5096585b6e2 healthcare-14-00334-v2.pdf 3accea2a78204eaebbf2497251b73640ca3b5df2dc231a24496516b0ec7a5a13 healthcare-14-00334-v2.pdf Title: Diabetic Retinopathy Screening in Primary Care Real Practice: Study Procedures and Baseline Characteristics from the RETINAvalid Project Subject: Background/Objectives: With rising diabetes rates, early detection of complications such as diabetic retinopathy (DR), a leading cause of visual impairment, is crucial. Incorporating DR screening into primary care has shown positive results, and integrating technological advances and artificial intelligence (AI) into these processes offers promising potential. The overall study aims to evaluate the agreement between primary care physicians, ophthalmologists, and an AI system in DR screening and referral decisions within a real-world primary care setting. Methods: In this brief report, we present the study protocol and provide an initial overview and description of our sample. A total of 1517 retinographies, obtained by a non-mydriatic retinal camera, were retrospectively collected from 301 patients with diabetes. Results: Primary care physicians referred 34.5% of the patients to ophthalmology, primarily due to opacification, suspicion of DR, or other retinal diseases. Overall, 13.62% of the participants were suspected of having DR, with 9.63% having a definitive diagnosis. Conclusions: These initial descriptive findings will be further explored in the next phase of the study through the analysis of concordance between primary care physicians, the AI-based software, and ophthalmology specialists. Future results are expected to provide valuable insights into the reliability of DR screening across different evaluators and support the integration of effective DR screening strategies into real-world clinical practice. Keywords: diabetic retinopathy; screening; retinography; primary care; artificial intelligence Author: Víctor-Miguel López-Lifante, Maria Palau-Antoja, Noemí Lamonja-Vicente, Cecilia Herrero-Alonso, Josefina Sala-Leal, Rosa García-Sierra, Adrià Prior-Rovira, Marina Alventosa-Zaidin, Meritxell Carmona-Cervelló, Erik Isusquiza Garcia, Idoia Besada and Pere Torán-Monserrat Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25; modified using OpenPDF 1.4.2 CreationDate: Mon Feb 2 12:34:12 2026 CET ModDate: Tue Feb 3 04:52:13 2026 CET Custom Metadata: yes Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 13 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 296736 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- XCLQOM+URWPalladioL-Bold Type 1 Custom yes yes yes 107 0 AIRHKL+VnURWPalladioL Type 1 Custom yes yes yes 108 0 WNWTFR+VnURWPalladioL-Bold Type 1 Custom yes yes yes 109 0 OXJCBP+URWPalladioL-Roma Type 1 Custom yes yes yes 110 0 WIGWTA+URWPalladioL-Ital Type 1 Custom yes yes yes 111 0 MYDOFE+EURM10 Type 1 Builtin yes yes yes 163 0 TNBSFT+CMSY10 Type 1 Builtin yes yes yes 223 0 URYLRZ+URWPalladioL-BoldItal Type 1 Custom yes yes yes 239 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2026-03-14 03:08:21 CET RepresentationInformation: healthcare-14-00334-v2.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2026-03-13 13:21:06 CET Size: 296736 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 480 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: true CenterWindow: false DisplayDocTitle: false NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Item: Title: Materials and Methods Children: Item: Title: Study Design Item: Title: Participants Item: Title: Data Collection and Variables Item: Title: Ophthalmology Specialist and AI Software Assessments Item: Title: Sample Size Item: Title: Statistical Analysis Item: Title: Results Children: Item: Title: Participants’ Characteristics Item: Title: Screening and Referral to Ophthalmology Item: Title: Discussion Item: Title: Conclusions Item: Title: References Info: Title: Diabetic Retinopathy Screening in Primary Care Real Practice: Study Procedures and Baseline Characteristics from the RETINAvalid Project Author: Víctor-Miguel López-Lifante, Maria Palau-Antoja, Noemí Lamonja-Vicente, Cecilia Herrero-Alonso, Josefina Sala-Leal, Rosa García-Sierra, Adrià Prior-Rovira, Marina Alventosa-Zaidin, Meritxell Carmona-Cervelló, Erik Isusquiza Garcia, Idoia Besada and Pere Torán-Monserrat Subject: Background/Objectives: With rising diabetes rates, early detection of complications such as diabetic retinopathy (DR), a leading cause of visual impairment, is crucial. Incorporating DR screening into primary care has shown positive results, and integrating technological advances and artificial intelligence (AI) into these processes offers promising potential. The overall study aims to evaluate the agreement between primary care physicians, ophthalmologists, and an AI system in DR screening and referral decisions within a real-world primary care setting. Methods: In this brief report, we present the study protocol and provide an initial overview and description of our sample. A total of 1517 retinographies, obtained by a non-mydriatic retinal camera, were retrospectively collected from 301 patients with diabetes. Results: Primary care physicians referred 34.5% of the patients to ophthalmology, primarily due to opacification, suspicion of DR, or other retinal diseases. Overall, 13.62% of the participants were suspected of having DR, with 9.63% having a definitive diagnosis. Conclusions: These initial descriptive findings will be further explored in the next phase of the study through the analysis of concordance between primary care physicians, the AI-based software, and ophthalmology specialists. Future results are expected to provide valuable insights into the reliability of DR screening across different evaluators and support the integration of effective DR screening strategies into real-world clinical practice. 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