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Avram O, Durmus B, Rakocz N, Corradetti G, An U, Nittala MG, Terway P, Rudas A, Chen ZJ, Wakatsuki Y, Hirabayashi K, Velaga S, Tiosano L, Corvi F, Verma A, Karamat A, Lindenberg S, Oncel D, Almidani L, Hull V, Fasih-Ahmad S, Esmaeilkhanian H, Cannesson M, Wykoff CC, Rahmani E, Arnold CW, Zhou B, Zaitlen N, Gronau I, Sankararaman S, Chiang JN, Sadda SR, Halperin E. Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans. Nat Biomed Eng 2024:10.1038/s41551-024-01257-9. [PMID: 39354052 DOI: 10.1038/s41551-024-01257-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/23/2024] [Indexed: 10/03/2024]
Abstract
The application of machine learning to tasks involving volumetric biomedical imaging is constrained by the limited availability of annotated datasets of three-dimensional (3D) scans for model training. Here we report a deep-learning model pre-trained on 2D scans (for which annotated data are relatively abundant) that accurately predicts disease-risk factors from 3D medical-scan modalities. The model, which we named SLIViT (for 'slice integration by vision transformer'), preprocesses a given volumetric scan into 2D images, extracts their feature map and integrates it into a single prediction. We evaluated the model in eight different learning tasks, including classification and regression for six datasets involving four volumetric imaging modalities (computed tomography, magnetic resonance imaging, optical coherence tomography and ultrasound). SLIViT consistently outperformed domain-specific state-of-the-art models and was typically as accurate as clinical specialists who had spent considerable time manually annotating the analysed scans. Automating diagnosis tasks involving volumetric scans may save valuable clinician hours, reduce data acquisition costs and duration, and help expedite medical research and clinical applications.
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Affiliation(s)
- Oren Avram
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Berkin Durmus
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nadav Rakocz
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Giulia Corradetti
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
- Department of Ophthalmology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ulzee An
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Muneeswar G Nittala
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
- Department of Ophthalmology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Prerit Terway
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Akos Rudas
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zeyuan Johnson Chen
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yu Wakatsuki
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
| | | | - Swetha Velaga
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
| | - Liran Tiosano
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Federico Corvi
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
| | - Aditya Verma
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
- Department of Ophthalmology and Visual Sciences, University of Louisville, Louisville, KY, USA
| | - Ayesha Karamat
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
| | - Sophiana Lindenberg
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
| | - Deniz Oncel
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
| | - Louay Almidani
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
| | - Victoria Hull
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
| | - Sohaib Fasih-Ahmad
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA
| | | | - Maxime Cannesson
- Department of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Charles C Wykoff
- Retina Consultants of Texas, Retina Consultants of America, Houston, TX, USA
- Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Corey W Arnold
- Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bolei Zhou
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ilan Gronau
- School of Computer Science, Reichman University, Herzliya, Israel
| | - Sriram Sankararaman
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jeffrey N Chiang
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA
| | - Srinivas R Sadda
- Doheny Eye Institute, University of California, Los Angeles, Pasadena, CA, USA.
- Department of Ophthalmology, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Eran Halperin
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
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Reiter GS, Mai J, Riedl S, Birner K, Frank S, Bogunovic H, Schmidt-Erfurth U. AI in the clinical management of GA: A novel therapeutic universe requires novel tools. Prog Retin Eye Res 2024; 103:101305. [PMID: 39343193 DOI: 10.1016/j.preteyeres.2024.101305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 09/25/2024] [Accepted: 09/26/2024] [Indexed: 10/01/2024]
Abstract
Regulatory approval of the first two therapeutic substances for the management of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) is a major breakthrough following failure of numerous previous trials. However, in the absence of therapeutic standards, diagnostic tools are a key challenge as functional parameters in GA are hard to provide. The majority of anatomical biomarkers are subclinical, necessitating advanced and sensitive image analyses. In contrast to fundus autofluorescence (FAF), optical coherence tomography (OCT) provides high-resolution visualization of neurosensory layers, including photoreceptors, and other features that are beyond the scope of human expert assessment. Artificial intelligence (AI)-based methodology strongly enhances identification and quantification of clinically relevant GA-related sub-phenotypes. Introduction of OCT-based biomarker analysis provides novel insight into the pathomechanisms of disease progression and therapeutic, moving beyond the limitations of conventional descriptive assessment. Accordingly, the Food and Drug Administration (FDA) has provided a paradigm-shift in recognizing ellipsoid zone (EZ) attenuation as a primary outcome measure in GA clinical trials. In this review, the transition from previous to future GA classification and management is described. With the advent of AI tools, diagnostic and therapeutic concepts have changed substantially in monitoring and screening of GA disease. Novel technology combined with pathophysiological knowledge and understanding of the therapeutic response to GA treatments, is currently opening the path for an automated, efficient and individualized patient care with great potential to improve access to timely treatment and reduce health disparities.
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Affiliation(s)
- Gregor S Reiter
- Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
| | - Julia Mai
- Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
| | - Sophie Riedl
- Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
| | - Klaudia Birner
- Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
| | - Sophie Frank
- Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
| | - Hrvoje Bogunovic
- Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
| | - Ursula Schmidt-Erfurth
- Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
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de Vente C, Valmaggia P, Hoyng CB, Holz FG, Islam MM, Klaver CCW, Boon CJF, Schmitz-Valckenberg S, Tufail A, Saßmannshausen M, Sánchez CI. Generalizable Deep Learning for the Detection of Incomplete and Complete Retinal Pigment Epithelium and Outer Retinal Atrophy: A MACUSTAR Report. Transl Vis Sci Technol 2024; 13:11. [PMID: 39235402 PMCID: PMC11379096 DOI: 10.1167/tvst.13.9.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024] Open
Abstract
Purpose The purpose of this study was to develop a deep learning algorithm for detecting and quantifying incomplete retinal pigment epithelium and outer retinal atrophy (iRORA) and complete retinal pigment epithelium and outer retinal atrophy (cRORA) in optical coherence tomography (OCT) that generalizes well to data from different devices and to validate in an intermediate age-related macular degeneration (iAMD) cohort. Methods The algorithm comprised a domain adaptation (DA) model, promoting generalization across devices, and a segmentation model for detecting granular biomarkers defining iRORA/cRORA, which are combined into iRORA/cRORA segmentations. Manual annotations of iRORA/cRORA in OCTs from different devices in the MACUSTAR study (168 patients with iAMD) were compared to the algorithm's output. Eye level classification metrics included sensitivity, specificity, and quadratic weighted Cohen's κ score (κw). Segmentation performance was assessed quantitatively using Bland-Altman plots and qualitatively. Results For ZEISS OCTs, sensitivity and specificity for iRORA/cRORA classification were 38.5% and 93.1%, respectively, and 60.0% and 96.4% for cRORA. For Spectralis OCTs, these were 84.0% and 93.7% for iRORA/cRORA, and 62.5% and 97.4% for cRORA. The κw scores for 3-way classification (none, iRORA, and cRORA) were 0.37 and 0.73 for ZEISS and Spectralis, respectively. Removing DA reduced κw from 0.73 to 0.63 for Spectralis. Conclusions The DA-enabled iRORA/cRORA segmentation algorithm showed superior consistency compared to human annotations, and good generalization across OCT devices. Translational Relevance The application of this algorithm may help toward precise and automated tracking of iAMD-related lesion changes, which is crucial in clinical settings and multicenter longitudinal studies on iAMD.
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Affiliation(s)
- Coen de Vente
- Quantitative Healthcare Analysis (qurAI) Group, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Biomedical Engineering and Physics, Amsterdam, The Netherlands
- Diagnostic Image Analysis Group (DIAG), Department of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, The Netherlands
| | - Philippe Valmaggia
- Department of Biomedical Engineering, Universität Basel, Basel, Basel-Stadt, Switzerland
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Basel-Stadt, Switzerland
| | - Carel B Hoyng
- Department of Ophthalmology, Radboudumc, Nijmegen, The Netherlands
| | - Frank G Holz
- Department of Ophthalmology and GRADE Reading Center, University Hospital Bonn, Germany
| | - Mohammad M Islam
- Quantitative Healthcare Analysis (qurAI) Group, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Caroline C W Klaver
- Department of Ophthalmology, Radboudumc, Nijmegen, The Netherlands
- Ophthalmology and Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Camiel J F Boon
- Department of Ophthalmology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Ophthalmology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Steffen Schmitz-Valckenberg
- Department of Ophthalmology and GRADE Reading Center, University Hospital Bonn, Germany
- John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA
| | - Adnan Tufail
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | - Clara I Sánchez
- Quantitative Healthcare Analysis (qurAI) Group, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Biomedical Engineering and Physics, Amsterdam, The Netherlands
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Mahmoudi A, Corradetti G, Emamverdi M, Lindenberg S, He Y, Oncel D, Santina A, Baek J, Kadomoto S, Nittala MG, Sadda SR. Atrophic Lesions Associated with Age-Related Macular Degeneration: High-Resolution versus Standard OCT. Ophthalmol Retina 2024; 8:367-375. [PMID: 37871680 DOI: 10.1016/j.oret.2023.10.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/12/2023] [Accepted: 10/16/2023] [Indexed: 10/25/2023]
Abstract
OBJECTIVE The objective of this study was to determine whether high-resolution OCT (HR-OCT) could enhance the identification and classification of atrophic features in age-related macular degeneration (AMD) compared with standard resolution OCT. DESIGN Prospective, observational, cross-sectional study. SUBJECTS The study included 60 eyes from 60 patients > 60 years of age with a diagnosis of AMD. METHODS The participants underwent volume OCT scanning using HR-OCT and standard resolution OCT devices. Trained graders reviewed and graded the scans, identifying specific regions of interest for subsequent analysis. MAIN OUTCOME MEASURES The study focused on identifying and classifying complete retinal pigment epithelium (RPE) and outer retinal atrophy (cRORA), incomplete RORA (iRORA), and other nonatrophic AMD features. Additionally, qualitative and quantitative features associated with atrophy were assessed. RESULTS The agreement among readers for classifying atrophic lesions was substantial to perfect for both HR-OCT (0.88) and standard resolution OCT(0.82). However, HR-OCT showed a higher accuracy in identifying iRORA lesions compared with standard OCT. Qualitative assessment of features demonstrated higher agreement for HR-OCT, particularly in identifying external limiting membrane (ELM) (0.95) and ellipsoid zone (EZ) disruption (0.94). Quantitative measurements of features such as hypertransmission defects, RPE attenuation/disruption, EZ disruption width, and ELM disruption width showed excellent interreader agreement with HR-OCT (> 0.90 for all features) but only moderate agreement with standard OCT (0.51-0.60). CONCLUSIONS The study results suggest that HR-OCT improves the accuracy and reliability of classifying and quantifying atrophic lesions associated with AMD compared with standard resolution OCT. The quantitative findings in particular may have implications for future research and clinical practice, especially with the availability of therapeutic agents for treating geographic atrophy and the development of commercially available HR-OCT devices. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Alireza Mahmoudi
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
| | - Giulia Corradetti
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Mehdi Emamverdi
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Sophiana Lindenberg
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Ye He
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Stein Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Deniz Oncel
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Ahmad Santina
- Stein Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Jiwon Baek
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Shin Kadomoto
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Muneeswar Gupta Nittala
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - SriniVas R Sadda
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
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Nanegrungsunk O, Corradetti G, Phinyo P, Choovuthayakorn J, Sadda SR. PREVALENCE AND PERSISTENCE OF HYPERTRANSMISSION DEFECTS OF VARIOUS SIZES IN EYES WITH INTERMEDIATE AGE-RELATED MACULAR DEGENERATION. Retina 2024; 44:20-27. [PMID: 37683194 DOI: 10.1097/iae.0000000000003929] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
PURPOSE To determine the prevalence and rate of persistence over 2 years of various-sized hypertransmission defects (hyperTDs) in eyes with intermediate age-related macular degeneration. METHODS Retrospective analysis of optical coherence tomography data from consecutive intermediate age-related macular degeneration patients. Choroidal en face optical coherence tomography images were evaluated for the presence and number of hyperTDs of three different sizes based on greatest linear dimension (small, 63-124 µ m; medium, 125-249 µ m; large, ≥250 µ m) at baseline and at the 2-year follow-up. Interreader agreement was determined by Gwet's agreement coefficient. Disagreements between graders were resolved by the senior investigator to yield a single consensus for all cases. RESULTS From 273 intermediate age-related macular degeneration eyes (247 patients), 72 and 76 hyperTD lesions were independently identified by two graders at baseline and overall agreement coefficient was 0.89 (95% CI, 0.86-0.93). After adjudication by the senior grader, the final consensus yielded 78 hyperTD lesions from 46 eyes (16.8%) of 42 patients (17.0%) in this study cohort. Among eyes with follow-up optical coherence tomography, 32 of 45 hyperTD lesions (71.1%) persisted. The rates of persistence were 100.0%, 72.7%, and 53.3% in large, medium, and small hyperTD sizes, respectively. CONCLUSION HyperTDs were present in a significant proportion of intermediate age-related macular degeneration eyes. Acceptable interreader agreement was demonstrated in identifying hyperTD. Larger hyperTD lesions were more likely to persist over 2 years.
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Affiliation(s)
- Onnisa Nanegrungsunk
- Doheny Imaging Reading Center and Doheny Eye Institute, Pasadena, California
- Department of Ophthalmology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California
- Retina Division, Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; and
| | - Giulia Corradetti
- Doheny Imaging Reading Center and Doheny Eye Institute, Pasadena, California
- Department of Ophthalmology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California
| | - Phichayut Phinyo
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Janejit Choovuthayakorn
- Retina Division, Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; and
| | - Srinivas R Sadda
- Doheny Imaging Reading Center and Doheny Eye Institute, Pasadena, California
- Department of Ophthalmology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California
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Lad EM, Finger RP, Guymer R. Biomarkers for the Progression of Intermediate Age-Related Macular Degeneration. Ophthalmol Ther 2023; 12:2917-2941. [PMID: 37773477 PMCID: PMC10640447 DOI: 10.1007/s40123-023-00807-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 08/30/2023] [Indexed: 10/01/2023] Open
Abstract
Age-related macular degeneration (AMD) is a leading cause of severe vision loss worldwide, with a global prevalence that is predicted to substantially increase. Identifying early biomarkers indicative of progression risk will improve our ability to assess which patients are at greatest risk of progressing from intermediate AMD (iAMD) to vision-threatening late-stage AMD. This is key to ensuring individualized management and timely intervention before substantial structural damage. Some structural biomarkers suggestive of AMD progression risk are well established, such as changes seen on color fundus photography and more recently optical coherence tomography (drusen volume, pigmentary abnormalities). Emerging biomarkers identified through multimodal imaging, including reticular pseudodrusen, hyperreflective foci, and drusen sub-phenotypes, are being intensively explored as risk factors for progression towards late-stage disease. Other structural biomarkers merit further research, such as ellipsoid zone reflectivity and choriocapillaris flow features. The measures of visual function that best detect change in iAMD and correlate with risk of progression remain under intense investigation, with tests such as dark adaptometry and cone-specific contrast tests being explored. Evidence on blood and plasma markers is preliminary, but there are indications that changes in levels of C-reactive protein and high-density lipoprotein cholesterol may be used to stratify patients and predict risk. With further research, some of these biomarkers may be used to monitor progression. Emerging artificial intelligence methods may help evaluate and validate these biomarkers; however, until we have large and well-curated longitudinal data sets, using artificial intelligence effectively to inform clinical trial design and detect outcomes will remain challenging. This is an exciting area of intense research, and further work is needed to establish the most promising biomarkers for disease progression and their use in clinical care and future trials. Ultimately, a multimodal approach may yield the most accurate means of monitoring and predicting future progression towards vision-threatening, late-stage AMD.
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Affiliation(s)
- Eleonora M Lad
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA.
| | - Robert P Finger
- Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Robyn Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Australia
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Schmitz-Valckenberg S, Saßmannshausen M, Braun M, Steffen V, Gao SS, Honigberg L, Ferrara D, Pfau M, Holz FG. Interreader Agreement and Longitudinal Progression of Incomplete/Complete Retinal Pigment Epithelium and Outer Retinal Atrophy in Age-Related Macular Degeneration. Ophthalmol Retina 2023; 7:1059-1068. [PMID: 37517799 DOI: 10.1016/j.oret.2023.07.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVE To analyze the ability to evaluate changes over time of individual lesions of incomplete or complete retinal pigment epithelium (RPE) and outer retinal atrophy (iRORA and cRORA, respectively) in patients with intermediate age-related macular degeneration (iAMD). DESIGN OCT images from patients enrolled in Proxima B clinical trial (NCT02399072) were utilized. PARTICIPANTS Patients enrolled in the Proxima B clinical trial, from the cohort with geographic atrophy (GA) in 1 eye and iAMD in the other eye at baseline, were included. METHODS Junior and senior readers analyzed OCT images for the qualitative presence of 9 distinct early atrophic features (presence of zone of choroidal hypertransmission, attenuation and/or disruption of RPE, disruption of ellipsoid zone [EZ] and external limiting membrane [ELM], outer nuclear layer [ONL] thinning, outer plexiform layer [OPL]/inner nuclear layer [INL] subsidence, and hyporeflective wedge-shaped band). If deemed "present," 7 features were quantified with a predefined tolerance level of 50 μm (diameter for the zone of choroidal hypertransmission, zone of attenuation and/or disruption of the RPE, outer retinal thickness left/right vertical diameter, outer retinal thickness thinnest vertical diameter, annotation of EZ, and ELM disruption). MAIN OUTCOME MEASURES Interreader agreements for qualitative assessments (κ-type statistics) and quantitative measurements (Bland-Altman statistics) were assessed. Progression of the lesion features over time was described. RESULTS Moderate agreement was found for presence of choroidal hypertransmission (κ = 0.54), followed by ELM disruption (κ = 0.58), OPL/INL subsidence (κ = 0.46), and a hyporeflective wedge-shaped band (κ = 0.47). Quantification measurements showed that choroidal hypertransmission had the highest agreement, whereas RPE attenuation/disruption had the lowest agreement. Longitudinal adjudicated changes for quantitative measurements of lesion progression showed that choroidal hypertransmission and ELM disruption showed significant progression, whereas EZ disruption and RPE attenuation/disruption did not. CONCLUSIONS The ability to evaluate changes over time for specific features of iRORA and cRORA was explored. The most robust biomarker was found to be choroidal hypertransmission, followed by ELM disruption and the qualitative markers of OPL/INL subsidence, as well as a wedge-shaped band. Disease progression over time could be assessed by some, but not all, spectral-domain OCT features that were explored. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Steffen Schmitz-Valckenberg
- John A. Moran Eye Center, University of Utah, Salt Lake City, Utah; GRADE Reading Center and Department of Ophthalmology, University of Bonn, Germany.
| | | | - Martina Braun
- GRADE Reading Center and Department of Ophthalmology, University of Bonn, Germany
| | | | - Simon S Gao
- Genentech, Inc., South San Francisco, California
| | | | | | - Maximilian Pfau
- GRADE Reading Center and Department of Ophthalmology, University of Bonn, Germany; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Frank G Holz
- GRADE Reading Center and Department of Ophthalmology, University of Bonn, Germany
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Corvi F, Corradetti G, Laiginhas R, Liu J, Gregori G, Rosenfeld PJ, Sadda SR. Comparison between B-Scan and En Face Images for Incomplete and Complete Retinal Pigment Epithelium and Outer Retinal Atrophy. Ophthalmol Retina 2023; 7:999-1009. [PMID: 37437713 DOI: 10.1016/j.oret.2023.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 07/14/2023]
Abstract
PURPOSE To evaluate and compare the detection of incomplete retinal pigment epithelium and outer retinal atrophy (iRORA) and complete retinal pigment epithelium and outer retinal atrophy (cRORA) assessed on OCT B-scans versus persistent choroidal hypertransmission defects (hyperTDs) assessed by en face choroidal OCT images. DESIGN Retrospective, cross-sectional study. PARTICIPANTS Patients with late atrophic age-related macular degeneration imaged on the same day using both Spectralis OCT and Cirrus OCT. MAIN OUTCOME MEASURE Agreement between the B-scan and en face OCT for the detection of hyperTDs, cRORA, and iRORA. METHODS Two independent graders examined en face OCT and structural OCT to determine the presence and location of hyperTDs, iRORA, and cRORA. RESULTS A total of 239 iRORA and cRORA lesions were detected on the B-scans, and 249 hyperTD lesions were identified on the en face OCT images. There was no significant difference (P = 0.88) in the number of lesions. There was no significant difference in the 134 cRORA lesions identified on B-scans and the 131 hyperTDs detected on en face OCT images (P = 0.13). A total of 105 iRORA lesions were identified by B-scan assessment; however, 50 of these iRORA lesions met the criteria for persistent hyperTDs on en face OCT images (P < 0.001). When considering the topographic correspondence between B-scan and en face OCT detected lesions, the mean percentage of agreement between B-scan detection of cRORA lesions with en face OCT detection was 97.6 % (P = 0.13). CONCLUSIONS We observed high overall agreement between cRORA lesions identified on B-scans and persistent hyperTDs identified on en face OCT. However, en face imaging was able to detect iRORA lesions that had a greatest linear dimension ≥ 250 μm in a nonhorizontal en face dimension. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Federico Corvi
- Doheny Eye Institute, University of California at Los Angeles, Los Angeles, California; Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California; Eye Clinic, Department of Biomedical and Clinical Science "Luigi Sacco", Sacco Hospital, University of Milan, Milan, Italy
| | - Giulia Corradetti
- Doheny Eye Institute, University of California at Los Angeles, Los Angeles, California; Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California
| | - Rita Laiginhas
- Department of Surgery and Physiology, Faculty of Medicine, University of Porto, Porto, Portugal. Centro Hospitalar e Universitário São João, Porto, Portugal; Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida
| | - Jeremy Liu
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida
| | - Giovanni Gregori
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida
| | - Philip J Rosenfeld
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida
| | - Srinivas R Sadda
- Doheny Eye Institute, University of California at Los Angeles, Los Angeles, California; Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California.
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Dow ER, Jeong HK, Katz EA, Toth CA, Wang D, Lee T, Kuo D, Allingham MJ, Hadziahmetovic M, Mettu PS, Schuman S, Carin L, Keane PA, Henao R, Lad EM. A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography. JAMA Ophthalmol 2023; 141:1052-1061. [PMID: 37856139 PMCID: PMC10587827 DOI: 10.1001/jamaophthalmol.2023.4659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/27/2023] [Indexed: 10/20/2023]
Abstract
Importance The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans. Objective To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan. Design, Setting, and Participants This retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023. Exposure A position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3). Main Outcomes and Measures Prediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Results The study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3). Conclusions and Relevance The fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation.
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Affiliation(s)
- Eliot R. Dow
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Hyeon Ki Jeong
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
| | - Ella Arnon Katz
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Cynthia A. Toth
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Dong Wang
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - Terry Lee
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - David Kuo
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Michael J. Allingham
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Majda Hadziahmetovic
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Priyatham S. Mettu
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Stefanie Schuman
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Lawrence Carin
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Pearse A. Keane
- University College London Institute of Ophthalmology, National Institute for Health and Care Research, Biomedical Research Centre, Moorfields Eye Hospital National Health Services Foundation Trust, London, United Kingdom
| | - Ricardo Henao
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Eleonora M. Lad
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
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Wei W, Anantharanjit R, Patel RP, Cordeiro MF. Detection of macular atrophy in age-related macular degeneration aided by artificial intelligence. Expert Rev Mol Diagn 2023:1-10. [PMID: 37144908 DOI: 10.1080/14737159.2023.2208751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
INTRODUCTION Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular atrophy (MA) which is characterized by the permanent loss of the RPE and overlying photoreceptors either in dry AMD or in wet AMD. A recognized unmet need in AMD is the early detection of MA development. AREAS COVERED Artificial Intelligence (AI) has demonstrated great impact in detection of retinal diseases, especially with its robust ability to analyze big data afforded by ophthalmic imaging modalities, such as color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). Among these, OCT has been shown to have great promise in identifying early MA using the new criteria in 2018. EXPERT OPINION There are few studies in which AI-OCT methods have been used to identify MA; however, results are very promising when compared to other imaging modalities. In this paper, we review the development and advances of ophthalmic imaging modalities and their combination with AI technology to detect MA in AMD. In addition, we emphasize the application of AI-OCT as an objective, cost-effective tool for the early detection and monitoring of the progression of MA in AMD.
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Affiliation(s)
- Wei Wei
- Department of Ophthalmology, Ningbo Medical Center Lihuili Hospital, Ningbo, China
- Department of Surgery & Cancer, Imperial College London, London, UK
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
| | - Rajeevan Anantharanjit
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
- Western Eye Hospital, Imperial College Healthcare NHS trust, London, UK
| | - Radhika Pooja Patel
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
- Western Eye Hospital, Imperial College Healthcare NHS trust, London, UK
| | - Maria Francesca Cordeiro
- Department of Surgery & Cancer, Imperial College London, London, UK
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
- Western Eye Hospital, Imperial College Healthcare NHS trust, London, UK
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Clevenger L, Rachitskaya A. Identifying geographic atrophy. Curr Opin Ophthalmol 2023; 34:195-202. [PMID: 36943458 DOI: 10.1097/icu.0000000000000952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
PURPOSE OF REVIEW Age-related macular degeneration (AMD) is one of the leading causes of blindness and can progress to geographic atrophy (GA) in late stages of disease. This review article highlights recent literature which assists in the accurate and timely identification of GA, and monitoring of GA progression. RECENT FINDINGS Technology for diagnosing and monitoring GA has made significant advances in recent years, particularly regarding the use of optical coherence tomography (OCT). Identification of imaging features which may herald the development of GA or its progression is critical. Deep learning applications for OCT in AMD have shown promising growth over the past several years, but more prospective studies are needed to demonstrate generalizability and clinical utility. SUMMARY Identification of GA and of risk factors for GA development or progression is essential when counseling AMD patients and discussing prognosis. With new therapies on the horizon for the treatment of GA, identification of risk factors for the development and progression of GA will become critical in determining the patients who would be appropriate candidates for new targeted therapies.
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