AI-Assisted OCT Clinical Phenotypes of Diabetic Macular Edema: A Large Cohort Clustering Study

<b>Purpose:</b> To characterize, using clustering analysis, the OCT morphological and clinical phenotypes of diabetic macular edema (DME) in a very large population (>2000 DME eyes) using standardized and validated OCT-based biomarkers. <b>Methods:</b> A cross-sectional study was conducted on OCT scans collected from 2355 eyes of 1688 patients with DME and performed during real-world clinical practice. OCT scans were automatically analyzed by a software able to automatically quantify OCT key biomarkers: intraretinal fluid (IRF), subretinal fluid (SRF), hyperreflective retinal foci (I-HRF), and external limiting membrane (ELM) and ellipsoid zone (EZ) interruption. Clustering analysis was performed using the above-mentioned biomarkers, including the distribution of IRF across the three ETDRS rings. <b>Results:</b> The overall population was predominantly composed of type 2 diabetes patients (89%), with a mean diabetes duration of 15.6 ± 10.7 years and mean best corrected visual acuity (BCVA) of 63 ± 18 ETDRS letters. Multivariate clustering identified four morphological phenotypes with distinct patterns of fluid distribution associated with different I-HRF counts, SRF volume, and percentages of ELM/EZ integrity (<i>p</i> < 0.0001). <b>Conclusions:</b> This large OCT analysis identified distinct morphological subtypes of DME, confirming the clinical relevance of key imaging biomarkers. The distribution and severity of DME features differ among clusters, supporting the importance of OCT-based phenotyping in tailoring treatment strategies and understanding disease evolution.

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Publication Details

Journal
Journal of Clinical Medicine
Published
2025-11-07
DOI
https://doi.org/10.3390/jcm14227893
Citations
4
Primary Topic
Retinal Diseases and Treatments
Type
article
Field-Weighted Citation Impact
6.83

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OCT
article

AI-Assisted OCT Clinical Phenotypes of Diabetic Macular Edema: A Large Cohort Clustering Study

Daniele Veritti, Leandro Inferrera, Giuseppe Covello, Giuseppe Giannaccare et al.
4 citations
Journal of Clinical Medicine
Retinal Diseases and Treatments
6.83
article

AI-Assisted OCT Clinical Phenotypes of Diabetic Macular Edema: A Large Cohort Clustering Study

Daniele Veritti, Leandro Inferrera, Giuseppe Covello, Giuseppe Giannaccare, Alessandro Invernizzi, Paolo Mora, Vincenzo Scorcia, Enrico Borrelli, Gilda Cennamo, Emilia Maggio, Roberto dell’Omo, F. Cappellani, Mariacristina Parravano, Ciro Costagliola, Adriano Carnevali, Nicola Valsecchi, Daniele Tognetto, Giulia Gregori, Paolo Lanzetta, Edoardo Midena, Valentina Sarao, Federico Ricci, Luca Rossetti, Luisa Frizziero, Marco Passamonti, Giulia Midena, Nicola Vito Lassandro, Francesco Viola, Maria Oliva Grassi, Tommaso Bacci, Chiara Maria Eandi, Elisabetta Pilotto, Paolo Melillo, Michele Reibaldi, Mary Romano, Rosangela Lattanzio, Donatella Musetti, Luca Danieli, Fabiano Cavarzeran, Lisa Toto, N Castellino, Marco Lombardo, Carlo Cagini, Michele Figus, Francesco Boscia, Marco Lupidi, Grazia Pertile, Marco Nassisi, Marzia Affatato, Michele Coppola, Settimio Rossi, Simone Donati, Maria Vittoria Cicinelli, Valentina Folegani
article en
4 citations

Abstract

<b>Purpose:</b> To characterize, using clustering analysis, the OCT morphological and clinical phenotypes of diabetic macular edema (DME) in a very large population (>2000 DME eyes) using standardized and validated OCT-based biomarkers. <b>Methods:</b> A cross-sectional study was conducted on OCT scans collected from 2355 eyes of 1688 patients with DME and performed during real-world clinical practice. OCT scans were automatically analyzed by a software able to automatically quantify OCT key biomarkers: intraretinal fluid (IRF), subretinal fluid (SRF), hyperreflective retinal foci (I-HRF), and external limiting membrane (ELM) and ellipsoid zone (EZ) interruption. Clustering analysis was performed using the above-mentioned biomarkers, including the distribution of IRF across the three ETDRS rings. <b>Results:</b> The overall population was predominantly composed of type 2 diabetes patients (89%), with a mean diabetes duration of 15.6 ± 10.7 years and mean best corrected visual acuity (BCVA) of 63 ± 18 ETDRS letters. Multivariate clustering identified four morphological phenotypes with distinct patterns of fluid distribution associated with different I-HRF counts, SRF volume, and percentages of ELM/EZ integrity (<i>p</i> < 0.0001). <b>Conclusions:</b> This large OCT analysis identified distinct morphological subtypes of DME, confirming the clinical relevance of key imaging biomarkers. The distribution and severity of DME features differ among clusters, supporting the importance of OCT-based phenotyping in tailoring treatment strategies and understanding disease evolution.

Journal of Clinical MedicineVol. 14(22)
University of Siena (IT), University of Pisa (IT), University of Insubria (IT), University of Rome Tor Vergata (IT), Marche Polytechnic University (IT), University of Parma (IT), University of Udine (IT), University of Molise (IT), AOL (United States) (US), University of Padua (IT), University of Trieste (IT), Vita-Salute San Raffaele University (IT), University of Cagliari (IT), University of Milan (IT), Università degli Studi di Enna Kore (IT), University of Perugia (IT), CTO Hospital (IT), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT), Magna Graecia University (IT), University of Catania (IT), University of Chieti-Pescara (IT), Luigi Sacco Hospital (IT), Saint Camillus International University of Health and Medical Sciences (IT), Fondazione G.B. Bietti (IT), Ospedale Sacro Cuore Don Calabria (IT), Azienda Ospedaliera San Gerardo (IT), Azienda USL di Bologna (IT), Istituto Europeo di Microchirurgia Oculare (IT), Fondation Asile des Aveugles (CH), University of Campania "Luigi Vanvitelli" (IT), Department of Medical Sciences (BY), Ospedale Policlinico San Martino (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), IRCCS Humanitas Research Hospital (IT), Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele (IT), University of Turin (IT), University of Bari Aldo Moro (IT), University of Naples Federico II (IT), University of Lausanne (CH)
Fondazione Roma
Openalex Percentile: Top 3%
Retinal Diseases and Treatments
6.83
DOIOpen Access PDFOpenAlex
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