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REVIEW 5 major objections 7 minor 122 references

The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective

T0 review · 5 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This review argues that AI-enhanced retinal imaging can be developed into a unified, non-invasive screening platform for life-threatening systemic diseases, and positions itself as the first cross-domain synthesis of retinal oculomics…

desk verdict A broad but carelessly assembled review whose central novelty claim is contradicted by its own references and whose citation errors undermine its evidentiary value. read the letter →

arxiv 2505.20810 v1 pith:GDQWBOQD submitted 2025-05-27 eess.IV cs.CV

Tariq M Khan , Toufique Ahmed Soomro , Imran Razzak This is my paper · ORCID
classification eess.IVcs.CV
keywords retinalimagingoculomicsdeeplearningcardiovascularriskpredictionneurodegenerativebiomarkersopticalcoherencetomographysystemicdiseasescreeningclinicaltranslation
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

The paper tries to establish that the retina is a practical window into systemic health, and that AI applied to retinal images can detect early signs of cardiovascular, neurodegenerative, metabolic, and hematological diseases. It claims to be the first review to unify AI-enhanced retinal oculomics across both neurological and cardiovascular domains into a single framework for clinical translation. If correct, routine eye scans could eventually supplement blood tests, brain imaging, and cardiovascular risk scores, enabling earlier and cheaper screening. The review synthesizes performance figures from many studies, including deep-learning sensitivity above 90 percent for diabetic retinopathy and an AUC of 0.89 for cardiovascular risk prediction from fundus photos.

What carries the argument

The central object is oculomics: the treatment of the retina as a surrogate tissue whose microvasculature and neural layers mirror systemic and cerebral pathology. The engine is AI/deep learning, which learns to map fundus photographs, OCT, and OCTA scans to disease labels and risk factors without relying only on predefined features. The imaging modalities—fundus photography, optical coherence tomography, OCT angiography, and adaptive optics—supply the high-resolution structural and vascular measurements that the AI models analyze.

What would settle it

Check whether reference [18], cited for hemoglobin and red-blood-cell prediction, is actually a study of central serous chorioretinopathy, and whether reference [81], cited for five-year atherosclerosis risk prediction, is a coronary artery disease genetics paper; if either mismatch is confirmed, the corresponding claims in Sections 6.1 and 5.3 lack the stated evidentiary support.

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Extended reading notes

Core claim

On its own terms, the paper's central discovery is that retinal biomarkers—arteriolar narrowing, venular dilation, retinal nerve fiber layer thinning, microvascular density loss, and even amyloid deposits—are consistently linked to systemic disease across multiple organ systems, and that modern imaging plus deep learning can extract these signals at scale. The authors assert that no prior review has systematically unified AI-enhanced retinal oculomics across neurological and cardiovascular disease domains, and they offer this review as the integrated framework that maps modality strengths, quantifies diagnostic performance, and outlines a roadmap for multicenter standardization and prospective validation. Their conclusion is that retinal imaging, powered by AI, is poised to become a cornerstone of precision medicine for early detection and risk stratification.

Load-bearing premise

The review's load-bearing premise is that each cited study actually supports the specific statement attached to it, so the performance figures, disease associations, and summary tables rest on accurate sourcing.

Editorial extensions

If this is right

  • Retinal fundus photography could be added to primary-care screening for cardiovascular risk, since deep learning models already predict risk factors like age, blood pressure, and major adverse cardiovascular events from such images.
  • OCT-based measures of retinal nerve fiber layer and macular thickness could become non-invasive biomarkers for early Alzheimer's disease and for tracking multiple sclerosis progression.
  • AI analysis of retinal images could support non-invasive screening for anemia, chronic kidney disease, and liver disease, though the review notes predictive accuracy for continuous lab values such as hemoglobin remains modest.
  • Standardized imaging protocols and external validation in diverse populations would be required before any of these retinal biomarkers enter routine clinical workflows.
  • A unified cross-domain framework could accelerate translation by aligning regulatory, interoperability, and data-sharing standards across cardiology, neurology, nephrology, and primary care.

Reading between the lines

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

  • The claim that this is the first unified review is a literature-positioning statement; its truth depends on the search scope and inclusion criteria, which the paper does not independently verify against a systematic protocol.
  • Because the review aggregates performance numbers from heterogeneous, mostly retrospective studies without meta-analysis, the pooled figures should be treated as indicative ranges rather than settled effect sizes.
  • If retinal oculomics matures, the same imaging infrastructure could be reused across multiple specialties, potentially changing the cost structure of population screening for cardiovascular and neurodegenerative disease.
  • A testable extension would be a prospective study asking whether adding an AI retinal risk score to conventional risk models improves reclassification of patients for statin therapy or dementia workup.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 7 minor

Summary. The manuscript is a narrative review claiming to be the first systematic unification of AI-enhanced retinal oculomics across neurological and cardiovascular disease domains, with additional coverage of metabolic, renal, hepatobiliary, and hematological applications. It surveys imaging modalities (fundus photography, OCT/OCTA, adaptive optics), AI/ML approaches, and clinical-translation challenges, and proposes a roadmap for standardization, external validation, and workflow integration. The paper includes several summary tables of reported performance figures and a cross-domain challenge table.

Significance. If the review's synthesis and performance figures were reliable, a cross-domain survey of retinal oculomics could be a useful resource for researchers and clinicians. The paper explicitly identifies important challenges such as external validation, dataset bias, and protocol standardization, and it correctly emphasizes the need for prospective multicenter studies. However, the evidentiary value of the review is heavily compromised by numerous mismatches between cited references and the claims they are supposed to support, and the stated novelty is contradicted by references within the paper's own bibliography. The tables therefore cannot be trusted as accurate summaries of the literature, and the central contribution is not established.

major comments (5)
  1. [§1, p. 4 (end of Introduction)] The novelty claim that 'No prior review has systematically unified AI-enhanced retinal oculomics across both neurological and cardiovascular disease domains' is contradicted by the paper's own references. Reference [29] is Wagner et al., 'Insights into systemic disease through retinal imaging-based oculomics' (Translational Vision Science & Technology, 2020), a review that explicitly spans cardiovascular and neurological conditions. Reference [30] (Barriada & Masip) reviews deep-learning methods for cardiovascular risk assessment from retinal images, and reference [62] is a systematic scoping review of retinal fundus imaging for cardiovascular markers. The authors must either remove the 'no prior review' claim or restrict the novelty to a precise, reproducible scope that remains after acknowledging these works.
  2. [§6.1, Table 4 and surrounding text] The claim that 'Zhang et al. [18]' reported fundus-image-based models with low R2 (0.06–0.57) for hemoglobin and RBC counts is unsupported. Reference [18] is X. Zhang, C. Z. F. Lim, J. Chhablani, Y. M. Wong, 'Central serous chorioretinopathy: Updates in the pathogenesis, diagnosis and therapeutic strategies' (Eye and Vision, 2023), which contains no hemoglobin prediction results. The R2 values appear to be misattributed, and the same reference is later used in §7.2 for CKD risk classification, where it is also inappropriate. Without a correct citation, the hematological parameter discussion and Table 4 lose their evidentiary basis.
  3. [§5.3, paragraph on Lieb et al.] The statement that 'Lieb et al. [81]' analyzed retinal images from diabetic patients to classify atherosclerotic risk categories and predicted atherosclerosis up to five years in advance is contradicted by reference [81], which is W. Lieb and R. S. Vasan, 'Genetics of coronary artery disease' (Circulation, 2013). This is a genetics review, not a retinal imaging study. This misattribution undermines the credibility of the AI-for-cardiovascular-risk section and indicates that the reference list does not reliably correspond to the claims made.
  4. [§7.5, 'Importance of Vessels Segmentaion'] This subsection is largely a list of the authors' own segmentation networks (references [105]–[117]), with more than ten self-citations and no comparative assessment against other published methods or benchmarks. The text describes general benefits of vessel segmentation but does not critically evaluate these specific methods or justify their selection over alternative approaches. At minimum, the authors should replace this self-citation-heavy passage with a balanced discussion that includes non-self references and quantitative comparisons, or remove it from a review whose scope is systemic disease detection.
  5. [Overall, 'systematic' synthesis and methods] The abstract and introduction describe the paper as a systematic synthesis, but no search strategy, inclusion criteria, or PRISMA-style methodology is provided. Given that the paper claims to 'systematically synthesize' a broad literature and quantify diagnostic performance, the absence of a reproducible search method makes the selection of references and the reported performance figures unverifiable. This is a load-bearing methodological gap for a review that asserts systematic coverage.
minor comments (7)
  1. [§3.2.1] The sentence 'A theoretical model proposed by Sabanayagam [35] suggests that nearly all individuals with PD exhibit telangiectasia in the mega-reflex' is unclear and appears to misattribute a retinal imaging finding; the cited reference [35] is a chronic kidney disease deep learning paper, not a Parkinson's disease study.
  2. [§3.3] 'Retinal Omics' is used to mean multimodal imaging and AI analysis, which is a nonstandard use of the term 'omics'; this should be defined or replaced with clearer terminology.
  3. [§5.2.1] The text cites 'Wang et al. [29]' for a mechanistic connection between oxidative-stress-driven nephron dysfunction and CAD-related stenosis, but reference [29] is Wagner et al., an oculomics review; the citation does not match the stated claim.
  4. [§6.2] The text attributes both 'Almonte et al. [89]' observations to the same reference, but the second finding (microvascular density loss in generalized anxiety disorder) is not supported by the title/scope of the cited Almonte and Capellà review; please verify and correct the citation.
  5. [Table 2 (Cheung et al. row)] The reported AUROC of 0.93 with sensitivity 93.2% and specificity 82.0% is given without confidence intervals or external-validation context; consider adding these details or tempering the summary statement.
  6. [Throughout] Several reference numbers are used inconsistently: for example, [78] is cited for a meta-analysis by McGeechan et al., but the reference list at [78] is McGeechan et al., so numbering is correct, yet the same number is also used in §5.2.1 for 'Wang et al. (2018)'. Please recheck all citation numbering and ensure each claim is supported by the cited source.
  7. [§9 Conclusions] The concluding section introduces a sentence about pregnant women with pro-atherosclerotic changes that is not connected to any prior analysis in the review; either elaborate or remove this tangential statement.

Circularity Check

1 steps flagged · score 4.0 of 10

No circular derivation of the review's predictions; the main self-citation burden is localized in Section 7.5, while the 'no prior review' novelty claim is contradicted by the paper's own references rather than established circularly.

  1. self citation load bearing [Section 7.5, 'Importance of Vessels Segmentaion']
    "Accurate segmentation of the retinal vasculature enables precise quantification of vessel caliber, tortuosity, and branching patterns, providing early biomarkers for systemic hypertension and cardiovascular risk [105, 106, 107]. ... Hybrid deep architectures like TBConvL-Net integrate convolutional and transformer modules to enhance segmentation accuracy across heterogeneous imaging modalities [116, 117]. The maturation of segmentation algorithms is essential for integrating AI-enhanced oculomics into precision medicine, allowing personalized disease prediction and monitoring [107, 110]."

    This subsection asserts that retinal vessel segmentation is an essential enabling technology for AI-based oculomics, and nearly all supporting citations are the authors' own prior segmentation papers (refs 105-117). The claim is therefore supported by the authors' own body of work rather than by independent or comparative evidence, giving the section a self-promotional character. However, this is not the paper's central thesis: the review's synthesis of externally reported biomarker studies does not depend on these self-citations, so the circularity is partial and localized.

full rationale

The paper is a narrative review with no fitted parameters, no derivation equations, and no prediction that reduces to its inputs by construction; the performance figures in Tables 1-7 are attributed to externally authored studies, albeit sometimes inaccurately. The only circularity-adjacent element is Section 7.5, where the importance of vessel segmentation is supported almost entirely by the authors' own prior segmentation networks, which raises the self-citation burden without invalidating the main external-literature synthesis. The paper's strongest novelty assertion, 'No prior review has systematically unified AI-enhanced retinal oculomics across both neurological and cardiovascular disease domains', is contradicted by its own bibliography (e.g., refs [29], [30], [62]), but that is a factual novelty failure rather than a circular derivation. Score 4 reflects the substantial self-citation usage, while acknowledging that the central review content retains independent evidentiary content.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The review introduces no new entities. Its reliance is on prior literature, and the key risk is that those sources are not being read correctly.

assumptions (3)
  • domain assumption The cited studies accurately report their results
    The review's summaries of performance metrics and disease associations assume that each cited paper actually supports the specific numbers or findings presented. This assumption is shown to be false in multiple locations.
  • domain assumption Retinal microvascular features are valid surrogates for systemic vascular health
    The entire framing of the review, introduced in Section 1, depends on the premise that retinal changes mirror systemic disease. The review does not critically examine the strength or limits of this premise.
  • domain assumption AI models can predict systemic parameters from retinal images beyond predefined clinical features
    Adopted from Poplin et al. [15] and similar work, this premise underlies the review's emphasis on AI as a tool for unknown biomarker discovery, but it is not independently derived here.

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Cite this review

Pith. "Pith review of The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective." pith.science (2026). https://pith.science/paper/GDQWBOQD

@misc{pith2026250520810,
  author       = {Pith},
  title        = {Pith review of: The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GDQWBOQD}},
  note         = {Machine review of arXiv:2505.20810}
}
read the original abstract

Retinal imaging has emerged as a powerful, non-invasive modality for detecting and quantifying biomarkers of systemic diseases-ranging from diabetes and hypertension to Alzheimer's disease and cardiovascular disorders but current insights remain dispersed across platforms and specialties. Recent technological advances in optical coherence tomography (OCT/OCTA) and adaptive optics (AO) now deliver ultra-high-resolution scans (down to 5 {\mu}m ) with superior contrast and spatial integration, allowing early identification of microvascular abnormalities and neurodegenerative changes. At the same time, AI-driven and machine learning (ML) algorithms have revolutionized the analysis of large-scale retinal datasets, increasing sensitivity and specificity; for example, deep learning models achieve > 90 \% sensitivity for diabetic retinopathy and AUC = 0.89 for the prediction of cardiovascular risk from fundus photographs. The proliferation of mobile health technologies and telemedicine platforms further extends access, reduces costs, and facilitates community-based screening and longitudinal monitoring. Despite these breakthroughs, translation into routine practice is hindered by heterogeneous imaging protocols, limited external validation of AI models, and integration challenges within clinical workflows. In this review, we systematically synthesize the latest OCT/OCT and AO developments, AI/ML approaches, and mHealth/Tele-ophthalmology initiatives and quantify their diagnostic performance across disease domains. Finally, we propose a roadmap for multicenter protocol standardization, prospective validation trials, and seamless incorporation of retinal screening into primary and specialty care pathways-paving the way for precision prevention, early intervention, and ongoing treatment of life-threatening systemic diseases.

Figures

Figures reproduced from arXiv: 2505.20810 by the authors.

Figure 1
Figure 1. Bird’s-eye view of diseases and parameter prediction from retinal imaging. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Heatmap of retinal imaging modalities (fundus, OCT, OCTA, AO) versus systemic disease cate [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Representative retinal changes observed in hypertensive retinopathy. Key features include reti [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Representative retinal images showing Branch Retinal Vein Occlusion (BRVO). Key pathological [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Comparison of a normal eye and an eye affected by glaucoma, highlighting structural differences [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Anatomical pathway of the ocular blood supply and optic nerve. The image depicts the key [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Progression stages of diabetic retinopathy. (A) A healthy retina with no apparent diabetic retinopa [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]
Figure 8
Figure 8. Figure 8: Key retinal features associated with neurodegenerative diseases. The illustration highlights distinc [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Key Retinal Features associated with heart diseases. [PITH_FULL_IMAGE:figures/full_fig_p033_9.png]

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Reference graph

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.