{"id":"bd13b5ef-4cf8-489e-97f5-cfa189de3b5e","arxiv_id":"2506.20303","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"An eight-attribute fundus quality rubric with a ResNet18 regressor, plus a weakly supported claim that quality-aware filtering improves DR grading.","lead":"This paper introduces FundaQ-8, an eight-part checklist for grading the quality of retinal photographs, and trains a standard ResNet18 neural network to predict the scores. It also claims that filtering low-quality images improves automated diabetic retinopathy grading, but the supporting results contain internal inconsistencies and lack a baseline comparison.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The DR robustness evidence for FundaQ-8 is internally inconsistent and lacks a no-FundaQ-8 baseline, leaving the score's clinical validity unsubstantiated.","rationale":"The reader's weakest-assumption analysis correctly identifies the FundaQ-8 rubric's clinical validity as the load-bearing premise. My stress-test converges on that same premise, but locates the most concrete failure in Section V.C: the DR experiment that would have supplied external evidence for clinical validity is internally inconsistent and lacks the necessary baseline. The regression metrics (MAE 0.0992, R2 0.7734) and EyeQ correlation (Spearman -0.752) are plausible evidence that the model reproduces the authors' own rubric, but they do not establish that the rubric measures diagnostic quality; that requires a valid downstream-task demonstration. The DR experiment as reported cannot provide it. I do not see grounds for rejection, because the issue is fixable: a clean DR experiment with a proper confusion matrix and baseline, or an independent clinical validation of the rubric, would address the concern. The present evidence justifies the reader's CONDITIONAL verdict, so I leave the verdict unchanged. I credit the paper for a transparent methodology and for attempting external validation, but the internal inconsistency in Table V is a concrete, checkable flaw that must be resolved before the clinical claims can be accepted.","tokens_in":9663,"tokens_out":5359,"duration_ms":60107,"concrete_test":"Re-run the DR grading experiment on a fixed, pre-specified test set (e.g., a held-out subset of EyeQ) with a single, frozen DR classifier. Report the full confusion matrix (TP, FP, TN, FN) for each FundaQ-8 quality group, derive all metrics directly from that matrix, and add a baseline where the same classifier is evaluated on all images with no quality filtering. If the corrected metrics differ from Table V, or if filtering by FundaQ-8 does not improve DR grading accuracy/F1 over the unfiltered baseline, then the clinical-validity claim fails.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim is that FundaQ-8 is an objective, clinically meaningful quality score. The only experiment intended to tie FundaQ-8 to clinical utility is the diabetic retinopathy (DR) grading analysis in Section V.C, but that evidence is defective in two ways. First, Table V reports identical values for accuracy, sensitivity, specificity, and F1 within each quality group (e.g., all four metrics equal 0.5464 for the 'Bad' group). For any real binary classifier, these four metrics cannot coincide except in degenerate cases, and the prose in the same section gives different numbers (e.g., 'Good' accuracy 0.7832 vs. table value 0.7357), so the table cannot be reconciled with the text. Second, the experiment compares DR grading performance across quality groups but does not include a baseline where the same DR model runs without FundaQ-8 filtering. Therefore, even if the metrics were corrected, the results would only show that better-quality images are easier to grade, not that 'incorporating FundaQ-8 into deep learning models for diabetic retinopathy grading improves diagnostic robustness' as claimed in the abstract. The load-bearing premise that FundaQ-8 scores predict diagnostic usability is thus supported only by the rubric's face validity, not by the one experiment designed to demonstrate it.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript proposes FundaQ-8, an eight-attribute scoring rubric for fundus image quality, and trains a ResNet18 regression model on 1,800 images labeled with this rubric to predict a continuous 0–1 quality score. The model is evaluated on a held-out test set (MAE 0.0992, R² 0.7734), compared against the EyeQ categorical quality labels (Spearman -0.752, OLS R² 0.653), and used to group images by predicted quality in a diabetic retinopathy grading experiment. The central claims are that FundaQ-8 provides an objective, interpretable, expert-validated quality score and that incorporating it into DR grading improves diagnostic robustness.","tokens_in":9967,"tokens_out":4557,"duration_ms":51878,"significance":"If the claims are established, a continuous, interpretable FIQA score trained from a structured clinical rubric would be a practically useful contribution to fundus image quality assessment, particularly for screening workflows. The paper has notable strengths: the regression evaluation is straightforward and the reported in-distribution errors are plausible; the external EyeQ validation on a large dataset (n=28,792) is a useful sanity check; and the eight-attribute rubric is transparent and clinically motivated. However, the load-bearing evidence for clinical utility is the DR experiment, and that experiment is internally inconsistent and missing a necessary baseline. The objectivity claim is also not supported by any inter-rater reliability data. With corrections, the core regression modeling could be acceptable, but the clinical-validity and DR-robustness claims require substantial additional evidence.","major_comments":[{"comment":"Table V reports identical values for accuracy, sensitivity, specificity, and F1 within each quality group (0.5464 for Bad, 0.4846 for Medium, 0.7357 for Good). For any real binary or multiclass confusion matrix, these four metrics cannot coincide except in degenerate cases. The prose in the same section reports different values, e.g., Good accuracy 0.7832, sensitivity 0.6108, specificity 0.8136, and F1 0.6258, while Bad sensitivity and F1 are given as 0.4846 and 0.4152. The table and text cannot both be correct, and the DR robustness claim is therefore unverifiable as presented. Please report the full confusion matrices and recompute all metrics consistently.","section":"Section V.C, Table V"},{"comment":"The DR experiment compares grading performance across quality groups but does not include a baseline condition without FundaQ-8 filtering or an alternative quality-assessment filter. Consequently, the results, even if corrected, would only demonstrate that higher-quality images are easier to grade, not that 'incorporating FundaQ-8 into deep learning models for diabetic retinopathy grading improves diagnostic robustness' as claimed in the abstract. A proper comparison between DR grading on the full dataset and DR grading after FundaQ-8-based filtering is needed to support the stated conclusion.","section":"Section V.C and Abstract"},{"comment":"The claim that FundaQ-8 is 'objective' and 'expert-validated' currently rests on face validity alone. The scoring process 'included iterative reviews to ensure inter-rater reliability,' but no inter-rater reliability statistics (e.g., Cohen's kappa or intraclass correlation) are reported. Additionally, the eight attributes are combined by simple summation and normalization to a 0–1 score, effectively assigning equal weights, with no sensitivity analysis or justification. Because the model is trained and evaluated on this self-defined rubric, the test-set R² largely measures consistency with the rubric rather than independent clinical utility; the external EyeQ correlation provides only indirect support.","section":"Section III.B"},{"comment":"The DR grading task is simplified to three classes, but the manuscript does not describe how sensitivity, specificity, and F1 are computed for this multiclass setting. Without a defined multiclass averaging scheme, the numerical values cannot be interpreted, and the inconsistency in Table V suggests the computations may be incorrect. Please clarify the evaluation protocol and provide class-wise counts.","section":"Section V.C and Section III.E(c)"}],"minor_comments":[{"comment":"There are multiple typos and spacing errors, including 'fundus iamge' (Section III introduction), 'Opthalmologist' (author affiliation), and 'inval uabl e' (Acknowledgment). A careful proofread is needed.","section":"Throughout"},{"comment":"The quality-group boundaries are inconsistently defined: the text defines Good as 'score≥0.8', but Table V writes 'Score > 0.8', and Medium is '0.4<Score<0.8', leaving the boundary values 0.4 and 0.8 undefined. Please specify inclusive/exclusive ranges.","section":"Section III.E(c) and Table V"},{"comment":"The EyeQ dataset is cited as reference [9], but [9] is a survey on fundus image quality assessment; please cite the original EyeQ dataset source (e.g., Fu et al., 'Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces' or the appropriate dataset paper).","section":"Section V.B"},{"comment":"The 'clinically acceptable 15% threshold' for MAE is introduced without a citation or definition. Please provide a source or justification for this threshold.","section":"Section V.A and Section VI"},{"comment":"The claim of stratified partitioning into training, validation, and test sets should be supported by reporting the score distributions for each split, not only the overall histogram in Fig. 3.","section":"Section III.D"}],"recommendation":"major_revision","confidential_remarks":"The manuscript has a serious internal inconsistency in the DR experiment (Table V vs. prose) and lacks a no-filter baseline, which undermines the paper's strongest clinical claim. The novelty relative to existing continuous FIQA methods such as FundusQ-Net and FTHNet is not clearly delineated, and no code or data is provided for reproducibility. These issues are fixable within the scope of a revision, so I recommend major revision rather than rejection."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Worth a look for the rubric itself: eight concrete attributes scored 0–2 with explicit anchors, co-designed with ophthalmologists, normalized to 0–1. That is a legitimate extension of multi-attribute FIQA, and the regression model trained on 1,800 images gets MAE 0.099 and R² 0.77 on its own test set, with a sensible external check: Spearman −0.75 against EyeQ categories on 28,792 images. If the authors ship the weights and annotation protocol, the core model is reproducible.\n\nBut the clinical claim rests on the DR experiment in Section V.C, and that is broken. Table V lists identical values for accuracy, sensitivity, specificity, and F1 within each quality group (0.5464 for Bad, 0.4846 for Medium, 0.7357 for Good). Four metrics cannot coincide like that except in pathological edge cases, and the prose gives different values (Good accuracy 0.7832 vs. table 0.7357). The table and the text cannot both be true. On top of that, the experiment has no baseline: it compares DR grading across quality bins but never runs the same DR model without FundaQ-8 filtering. All it can show, even with corrected numbers, is that better-quality images are easier to grade. The abstract claims incorporating FundaQ-8 improves diagnostic robustness, but nothing in the paper demonstrates that. The score's clinical validity is supported only by the rubric's face validity and the EyeQ correlation, which is correlation with another subjective labeling scheme.\n\nThe circularity worry is real but not fatal: training and testing on your own rubric measures internal consistency, not clinical utility. The EyeQ correlation is the independent evidence, and it is decent.\n\nCitation pattern is fine; they cite the multi-attribute antecedent [12] and the continuous-scale work. No invented entities.\n\nWho this is for: anyone building FIQA tools who wants a structured attribute rubric and a reproducible regression baseline. It deserves a serious referee, but only with major revision: fix or remove the DR section, release code and weights, and be honest that the rubric is a design choice, not a validated clinical gold standard.","headline":"The eight-attribute rubric and external EyeQ correlation are real, but the DR experiment is internally inconsistent and the clinical claim does not survive it.","tokens_in":10467,"tokens_out":2152,"would_cite":false,"duration_ms":20229,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"FundaQ-8 is an eight-attribute scoring rubric that lets a small regression network rate retinal image quality on a continuous 0 to 1 scale, matching expert scores on its test set and stratifying diabetic retinopathy detection.","keywords":["fundus image quality assessment","FundaQ-8","ResNet18 regression","transfer learning","diabetic retinopathy grading","EyeQ validation","continuous quality score","clinical rubric"],"falsifier":"Have an independent panel of ophthalmologists rate the same images with their own quality criteria and compare against FundaQ-8; near-zero agreement would show the rubric does not reproduce expert judgment. A more direct test would measure whether clinically significant lesions are missed in images the model scores above 0.8 as often as in images scored below 0.4; if low-scoring images are no more likely to hide pathology, the score is not a diagnostic-quality measure.","tokens_in":9529,"feed_emoji":"👁️","tokens_out":8059,"duration_ms":82914,"temperature":0.7,"pith_summary":"FundaQ-8 is a rubric developed with ophthalmologists that grades a retinal (fundus) photograph on eight clinical attributes — resolution, field of view, color fidelity, artifacts, vessel visibility, macula, optic disc, and optic cup — each scored 0 to 2 and normalized to a continuous 0 to 1 quality score. The paper trains a ResNet18 regression model on 1,800 images labeled with this rubric and reports strong agreement with expert scores on the test set (MAE 0.0992, $R^2$ 0.7734). It then shows that predicted scores correlate with EyeQ's independent quality categories and that diabetic retinopathy detection performs better on images the model scores as good. The significance, if the claims hold, is that an explicit clinical checklist can replace subjective categorical labels as a training target for automated image quality assessment, making the scores interpretable and potentially useful for screening workflows.","feed_headline":"A small network matches expert grading of retinal image quality","feed_subtitle":"Continuous scores from an eight-attribute rubric align with expert ratings and improve diabetic retinopathy screening.","key_machinery":"The central object is the FundaQ-8 scoring rubric: eight named attributes, each rated on a 0 to 2 Likert scale with descriptions tied to diagnostic usability, summed and normalized to a continuous 0 to 1 score. This rubric carries the argument by converting subjective expert judgment into structured labels; a ResNet18 with a single-neuron regression head is then trained to predict those labels, and the rubric's attribute-level detail is what gives the score its interpretability.","core_discovery":"The central claim is that a continuous, interpretable quality score for retinal images can be derived from eight explicit clinical attributes, and that a small transfer-learned network can learn to predict that score well enough to align with expert judgment. The paper reports test-set MAE of 0.0992 and $R^2$ of 0.7734, a strong negative correlation with EyeQ quality categories (Spearman -0.752), and DR detection accuracy that is substantially higher for images scored good than for images scored bad. In the authors' framing, this shows that a structured expert rubric can serve as a reproducible training target, giving automated quality assessment a granular and clinically meaningful output rather than a coarse category.","pith_inferences":["The rubric weights all eight attributes equally by construction; a natural extension the paper does not test is learning attribute weights from downstream diagnostic outcomes, which could change scores without changing the rubric's structure.","The strong correlation with EyeQ categories suggests the learned score could serve as a soft label for semi-supervised quality assessment on unlabeled clinical archives, but the paper does not demonstrate this.","A testable prediction follows from the DR-grading results: filtering screening images by a FundaQ-8 quality threshold should reduce false negatives in a real screening population, not just in the EyeQ subset; that remains to be shown."],"forward_implications":["Screening programs could use the continuous score to reject low-quality images before they reach a human reader, potentially reducing the 12-25% retake burden cited in the paper.","The model's alignment with EyeQ categories (Spearman -0.752) suggests the learned score transfers to an independent categorical labeling scheme, not just the training labels.","Because DR grading accuracy is higher for images the model scores as good, quality filtering could make automated diabetic retinopathy screening more reliable in clinical deployment.","The eight-attribute design gives clinicians a checklist to audit why an image scored as it did, rather than relying on a black-box quality number.","Continuous 0 to 1 scores allow flexible quality thresholds that can be tuned to the cost of false negatives in a given screening context."],"supporting_citations":[{"why":"Supplies the cited 12-25% retake rate that motivates the need for automated quality assessment.","marker":"[5]"},{"why":"Describes a regression-based fundus quality assessment network that the present architecture adapts.","marker":"[8]"},{"why":"Cited as the source of the EyeQ dataset and its three quality categories used for external validation.","marker":"[9]"},{"why":"Provides a deep-learning FIQA baseline on the DeepDRiD dataset that motivates the CNN regression approach.","marker":"[10]"},{"why":"Introduces human-visual-system-based perceptual parameters that the eight-attribute rubric parallels.","marker":"[11]"},{"why":"Demonstrates automatic fundus quality assessment on a continuous scale, which the model's 0 to 1 output extends.","marker":"[12]"},{"why":"Surveys deep-learning DR grading systems, motivating the integration of FIQA into DR screening.","marker":"[14]"},{"why":"Catalog of public glaucoma datasets, cited as the source of the standardized training images used alongside local clinical data.","marker":"[15]"}],"fun_headline_variants":["Eight clinical parameters let small AI rate retinal image quality","FundaQ-8: expert-structured scoring teaches network to rate retinas","AI predicts retinal quality score from eight clinical attributes","Continuous quality score from eight attributes aids DR screening"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the eight attributes chosen by the authors' ophthalmologists, weighted equally, capture what actually makes a retinal image diagnostically usable; if they do not, the model's scores inherit that blind spot.","fun_headline_variants_meta":{"raw":{"variants":["Eight clinical parameters let small AI rate retinal image quality","FundaQ-8: expert-structured scoring teaches network to rate retinas","AI predicts retinal quality score from eight clinical attributes","Continuous quality score from eight attributes aids DR screening"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000203,"raw_usage":{"total_tokens":1336,"prompt_tokens":843,"completion_tokens":493,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":459,"completion_tokens_details":{"reasoning_tokens":427}},"tokens_in":459,"tokens_out":493,"duration_ms":5812,"temperature":1.0,"reasoning_tokens":427,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T22:50:49.927818+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Have an independent panel of ophthalmologists rate the same images with their own quality criteria and compare against FundaQ-8; near-zero agreement would show the rubric does not reproduce expert judgment. A more direct test would measure whether clinically significant lesions are missed in images the model scores above 0.8 as often as in images scored below 0.4; if low-scoring images are no more likely to hide pathology, the score is not a diagnostic-quality measure.","supporting_citations":[{"cited_title":"Intermediate Assouad-like dimensions for measures","cited_arxiv_id":"2004.05133","evidence_quote":"Supplies the cited 12-25% retake rate that motivates the need for automated quality assessment."},{"cited_title":"Fundus image quality assessment: survey, challenges, and future scope,","cited_arxiv_id":null,"evidence_quote":"Cited as the source of the EyeQ dataset and its three quality categories used for external validation."},{"cited_title":"EfficientNetV2 Based Ensemble Model for Quality Estimation of Diabetic Retinopathy Images from DeepDRiD,","cited_arxiv_id":null,"evidence_quote":"Provides a deep-learning FIQA baseline on the DeepDRiD dataset that motivates the CNN regression approach."},{"cited_title":"Human Visual System-Based Fundus Image Quality Assessment of Portable Fundus Camera Photographs,","cited_arxiv_id":null,"evidence_quote":"Introduces human-visual-system-based perceptual parameters that the eight-attribute rubric parallels."},{"cited_title":"Automatic fundus image quality assessment on a continuous scale,","cited_arxiv_id":null,"evidence_quote":"Demonstrates automatic fundus quality assessment on a continuous scale, which the model's 0 to 1 output extends."},{"cited_title":"Deep Learning in Automatic Diabetic Retinopathy Detection and Grading Systems: A Comprehensive Survey and Comparison of Methods,","cited_arxiv_id":null,"evidence_quote":"Surveys deep-learning DR grading systems, motivating the integration of FIQA into DR screening."}],"review_version":1}