Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends

Daniel Sonntag; Devansh Srivastav; Duy M. H. Nguyen; Hans-Juergen Profitlich; Hasan Md Tusfiqur Alam; Ngan Le; Tai Nguyen

REVIEW 4 major objections 5 minor 1 cited by

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Deep learning in ophthalmology is now mapped across four diseases, four method families, and the datasets behind them—with trust and validation as the remaining barriers.

desk verdict Broad but sloppy survey: useful as an orientation map, not as a reference, until the dataset tables are audited. read the letter →

arxiv 2501.04073 v1 pith:SUKFKZ5J submitted 2025-01-07 eess.IV cs.CV

Duy M. H. Nguyen , Hasan Md Tusfiqur Alam , Tai Nguyen , Devansh Srivastav , Hans-Juergen Profitlich , Ngan Le , Daniel Sonntag This is my paper · ORCID

classification eess.IVcs.CV

keywords DiabeticretinopathyGlaucomaAge-relatedmaculardegenerationRetinalvesselsegmentationDeeplearningTransformerarchitecturesExplainableAIMultimodal

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review aims to give a comprehensive, current map of deep learning for ophthalmology, organized around four application areas: diabetic retinopathy, glaucoma, age-related macular degeneration, and retinal vessel segmentation. It covers the machine-learning and deep-learning methods applied to each area—CNNs, RNNs, attention mechanisms, and transformers—alongside the public datasets and reported performance metrics. The paper also identifies future directions it argues the field should take, including multimodal data integration, user-centered explainable AI, longitudinal monitoring, generative-AI explanations, fairness, and real-world clinical validation. If the map is accurate, it gives researchers and clinicians a single reference for choosing methods and datasets and for seeing where the field is heading.

What carries the argument

The organizing device is a four-problem taxonomy—diabetic retinopathy, glaucoma, age-related macular degeneration, and retinal vessel segmentation—crossed with architecture families (machine-learning algorithms, CNNs, RNNs, transformers, attention-based and encoder-decoder networks). Each combination is anchored by tables of representative studies with reported metrics and by dataset tables that list image counts, formats, and resolutions. The taxonomy does the work of turning a large literature into a structured map that lets a reader locate methods, benchmarks, and open problems by disease and by model type.

What would settle it

Check a sample of the survey's dataset counts against the original sources; the APTOS case is already decisive. The official APTOS 2019 Blindness Detection competition reports 3,662 training fundus photographs, so Table II's '13,000' is wrong, and the paper's tables cannot be treated as reliable without an audit of the remaining entries.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that deep learning has become the dominant technical approach in ophthalmic imaging for posterior-segment diseases, and that the state of the art can be usefully organized as a taxonomy: four clinical problems cross-cut by method families and supported by cataloged datasets. The survey reports that CNN-based systems already reach high accuracy across the four applications, that transformer and attention-based models are increasingly competitive and more interpretable in some glaucoma and AMD tasks, and that recurrent and hybrid models add value for disease progression. It further argues that the decisive obstacles to clinical adoption are no longer raw accuracy but explainability, data diversity, multimodal fusion, and rigorous prospective validation.

Load-bearing premise

The review's value as a reference depends on the dataset statistics and performance numbers in its tables being faithful to the original papers, and that assumption already shows visible cracks: the diabetic retinopathy dataset table lists APTOS as 13,000 images while the text says the APTOS collection has 3,662 fundus photographs.

Editorial extensions

If this is right

If the survey's map is reliable, a researcher entering any of the four areas can use its dataset tables to pick a benchmark and its method tables to see which architectures have already been tried.
For diabetic retinopathy, the reported AI-human hybrid screening workflow suggests that pairing automated first-pass screening with expert overreading can keep sensitivity high while improving specificity outside specialist clinics.
For glaucoma, the review's evidence points to transformers and geometric deep learning as emerging routes to better generalization across fundus datasets and 3D optic-nerve-head analysis.
For AMD, integrating genetic data and multimodal OCT/OCTA features appears to improve progression prediction over image-only models.
Across all four areas, the review argues that explainability, multimodal integration, and clinical validation are the shared bottlenecks that determine whether DL systems move into practice.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the performance numbers in the tables come from different datasets, splits, and evaluation protocols, an outside reader should treat cross-paper comparisons as indicative rather than head-to-head.
A natural next step the authors do not develop is a living, versioned database of datasets and benchmark results, which would address the transcription-accuracy risk in static survey tables.
If transformer and multimodal methods continue their reported trajectory, screening may shift from single-image classifiers to longitudinal risk models that combine imaging with genetics and clinical history.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Frankly, this survey is a decent orientation map to deep learning in ophthalmology, but you can't trust the tables without checking the original sources. The stress-test note got the key issue right: Table II lists APTOS as 13,000 images while Section III-A.3.j correctly says 3,662 fundus photographs. That's exactly the kind of error that kills a survey's reference value. I also see KORA at 2,546 in Table VI vs 2,840 in the text, 'ACU' instead of 'AUC' in Table III, and placeholder author names like 'Decencire2013' in the e-ophtha rows. These are mechanical fixes, but they are load-bearing: if a researcher can't trust the tables, the paper's main contribution evaporates.

What the paper does well: it covers four application areas (DR, glaucoma, AMD, vessel segmentation) and organizes recent methods, datasets, and performance numbers into a readable structure. The inclusion of transformer-based methods and multimodal learning is timely. The method-focused sections are reasonably accurate at the narrative level. Some future directions (XAI, longitudinal monitoring, generative AI) are standard but not wrong. The self-citations are descriptive and don't carry the argument; no circularity problem.

The soft spots beyond the tables: Figure 2's publication trends aren't reproducible—the text mentions databases and keywords but gives no exact query or date. The paper says it focuses on 2019-2023 from 'top-tier venues' but never defines those venues or a systematic search strategy, so the coverage is a convenience sample. Also, the future perspectives section reads like a list of buzzwords; it doesn't prioritize or connect them to concrete open problems.

I'd send this to serious peer review but only with the expectation of heavy revision. The fix is mechanical but essential: audit every table row against primary sources, add a reproducible protocol for the bibliometric figure, and define inclusion criteria. Once that's done, the survey could serve as a useful entry point for students and clinicians. As it stands, I'd advise anyone to treat the text as a rough guide and the tables as leads, not facts.

Referee Report

4 major / 5 minor

Summary. This manuscript is a narrative literature review of deep-learning applications in ophthalmology, organized around four tasks: diabetic retinopathy grading and detection, glaucoma detection, age-related macular degeneration diagnosis, and retinal vessel segmentation. It provides background on machine-learning and deep-learning architectures, surveys representative methods in each application area (including CNNs, RNNs, transformer-based models, and attention mechanisms), tabulates typical studies and supporting datasets, and concludes with future directions such as multimodal data integration, explainable AI, longitudinal prognostic modeling, generative AI, fairness, and clinical validation. The paper's stated central claim is that it offers a comprehensive and reliable state-of-the-art map for these four application areas and their datasets.

Significance. If accurate, the survey would be a useful entry point to a large and fast-moving literature: it consolidates methods across four subfields, covers architectures from CNNs to transformers, includes performance tables and dataset inventories, and proposes a sensible taxonomy of future research directions. The paper offers no new algorithms or experiments; its contribution is organizational and aggregative. However, the value of an aggregative survey depends on the reliability of its transcribed statistics and metrics, and the manuscript currently contains internal contradictions that prevent it from being used as a dependable reference without further verification. The breadth of coverage is a genuine strength, but it is not yet matched by the required level of factual precision.

major comments (4)

[Section III-A.3.j and Table II] Table II lists APTOS [84] as having 13,000 images, while Section III-A.3.j states that the APTOS 2019 Blindness Detection dataset comprises 3,662 fundus photographs. These numbers cannot both be correct. Because the dataset tables are one of the paper's primary reference deliverables, this internal contradiction directly undermines the claim that the survey is a reliable state-of-the-art map. The authors should correct the APTOS entry and auditevery row of Tables II, IV, VI, and VIII against primary sources; a spot-check is no longer sufficient once an internal inconsistency has been confirmed.
[Table III and Table V] Several reported metrics are transcribed with errors that make cross-method comparison impossible. In Table III, the row for reference [118] reports "ACU = 0.99 (private test set)"; ACU is not a standard metric and the context indicates it should be AUC. In Table V, the first row ([141]) reports "Sen=0.8, Spec=0.55; Sen=0.34, Sen=0.90", where the final term repeats "Sen" and presumably should be "Spec". These are not stylistic differences but correctness errors in the performance tables, and they need to be corrected as part of the audit.
[Section III-C.4.c and Table VI] The KORA dataset is described in Section III-C.4.c as comprising fundus images from 2,840 participants aged 25 to 74 years, whereas Table VI lists 2,546 images for KORA. This is another instance of conflicting dataset statistics within the same paper. The authors should determine the correct figure and ensure that the text and the table agree, and they should check the other AMD dataset entries for the same class of error.
[Table II dataset labels] Table II uses malformed dataset labels that obscure the source references: the rows appear as "e-ophtha EXDecencire2013", "e-ophtha MADecencire2013", and "Messidor-2Decencire2014", and the text in Section III-A.3.d refers to the database as "Decencire2013". The intended source is the e-ophtha dataset by Decencière et al. (2013), not an entity named "Decencire2013". The concatenation of dataset name and citation placeholder makes the table difficult to use and must be fixed in the audit.

minor comments (5)

[Section II (Methodology)] The selection methodology is described only qualitatively (keywords such as "retinopathy", "glaucoma", and "retinal disease", with a focus on 2019-2023), but the precise search strings, inclusion and exclusion criteria, and the number of screened and included papers are not reported. A reproducible search protocol would strengthen the claim of comprehensiveness.
[Figure 2] The source and query details behind the publication-count trend in Figure 2 are not given, so the reader cannot verify the counts or reproduce the figure.
[Table III duplicate entries] Table III lists "Ming et al. [102]" twice, once under 2023 with accuracy/sensitivity/specificity/AUC values and again near the end of the table as "Ming et al. [102] ResNeST, Domain Adaptation" with REFUGE/LAG/ORIGA/RIM-ONE results. This duplication is confusing and should be resolved.
[Table III author names] Several author names in Table III do not match the reference list: "You et al. [118]" should be "Zhou et al." according to reference [118], and the rows labeled "Sarwar et al. [95]" and "Nahida et al. [98]" correspond to references by Kamal et al. and Akter et al. in the text. These name inconsistencies should be corrected.
[Section III-C.4] The sentence "We summarize below the most commonly used datasets in ADM research" should read "AMD research", and the dataset name "Ch´aks.u" in Table IV and Section III-B.5.m should be spelled consistently with its source spelling (Cháksu).

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is a literature survey that compiles external studies; self-citations are descriptive and not load-bearing.

full rationale

The paper is a review, not a derivation. It does not fit parameters, derive equations, or generate predictions from its own inputs; instead it summarizes external methods and datasets across four ophthalmic applications. The few self-citations (e.g., DRG-Net [72], TATL [157], LVM-Med [202], LoGra-Med [227], and the authors' multimodal and robustness papers [193], [213]) appear only as descriptive mentions in related-work or future-perspective passages, not as evidence used to establish a new claim or to forbid alternatives. No uniqueness theorem is imported from the authors' prior work, and no ansatz is smuggled in via citation. The dataset-table inconsistency for APTOS (Table II lists 13,000 while Section III-A.3.j states 3,662) is a transcription/quality-control defect that affects the survey's reference reliability, but it is not circular reasoning. Because the survey's central claim is comprehensive coverage and synthesis of existing literature, and that claim rests on external sources rather than on any chain of reasoning that reduces to its own outputs, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

This review introduces no free parameters, no fitted constants, and no invented entities. It rests on the correctness of the cited literature and on the authors' own transcription and selection decisions, which are partially unreliable (APTOS count) and partly underspecified (Figure 2 query).

assumptions (4)

domain assumption Cited performance metrics accurately reflect the original papers
The survey's tables report AUC, accuracy, sensitivity from individual studies without independent verification.
domain assumption Dataset statistics in Tables II, IV, VI, VIII are correct transcriptions
The APTOS count discrepancy shows this assumption is violated in at least one instance.
domain assumption Publication counts in Figure 2 come from a consistent bibliometric query
No query is specified, so the trend may not be reproducible.
domain assumption The 2019-2023 window and venue filter capture the state of the art
The survey excludes earlier foundational work from detailed analysis and does not justify the cutoff.

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Cite this review

Pith. "Pith review of Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends." pith.science (2026). https://pith.science/paper/SUKFKZ5J

@misc{pith2026250104073,
  author       = {Pith},
  title        = {Pith review of: Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SUKFKZ5J}},
  note         = {Machine review of arXiv:2501.04073}
}

read the original abstract

The emergence of artificial intelligence (AI), particularly deep learning (DL), has marked a new era in the realm of ophthalmology, offering transformative potential for the diagnosis and treatment of posterior segment eye diseases. This review explores the cutting-edge applications of DL across a range of ocular conditions, including diabetic retinopathy, glaucoma, age-related macular degeneration, and retinal vessel segmentation. We provide a comprehensive overview of foundational ML techniques and advanced DL architectures, such as CNNs, attention mechanisms, and transformer-based models, highlighting the evolving role of AI in enhancing diagnostic accuracy, optimizing treatment strategies, and improving overall patient care. Additionally, we present key challenges in integrating AI solutions into clinical practice, including ensuring data diversity, improving algorithm transparency, and effectively leveraging multimodal data. This review emphasizes AI's potential to improve disease diagnosis and enhance patient care while stressing the importance of collaborative efforts to overcome these barriers and fully harness AI's impact in advancing eye care.

Figures

Figures reproduced from arXiv: 2501.04073 by the authors.

**Figure 2.** Rapid growth in publications leveraging machine [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 4.** Proposed AI-human hybrid workflow: AI-screened [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

Figures from the paper (5 more)

**Figure 5.** Figure 5: Overall architecture of deep learning-based Computer [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: CAM: Class Activation Mapping for ensuring explain [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 8.** Figure 8: The main architecture of a unidirectional fusion net [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

**Figure 9.** Figure 9: CAS-UNet architecture, it features Cross-Fusion Chan [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

**Figure 10.** Figure 10: Future perspectives in AI for Ophthalmology encompass several key areas, including addressing disparities, integrating [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classification
cs.CV 2025-07 conditional novelty 3.0 of 10

HOG-CNN fuses a 26,244-dimensional HOG descriptor with a frozen CNN embedding and reports accuracy up to 98.5% on APTOS, 92.8% on IC-AMD, and 83.9% on ORIGA.

Reference graph

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