Rigor Reviewed

Rigor-Reviewed Manuscript

Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial.

Name: Αlpha¹ Rigor Record — Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial.
Item: Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial.
Rating: 96
Author: Αlpha¹

Illingworth PJ, Venetis C, Gardner DK, Nelson SM, Berntsen J, Larman MG, Agresta F, Ahitan S, Ahlström A, Cattrall F, Cooke S, Demmers K, Gabrielsen A, Hindkjær J, Kelley RL, Knight C, Lee L, Lahoud R, Mangat M, Park H, Price A, Trew G, Troest B, Vincent A, Wennerström S, Zujovic L, Hardarson T

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Αlpha¹ Rigor Review — 2.25★ (Weak), 0 critical, 3 high issues for “Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial.”

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Αlpha¹ Editorial

This rigor review was examined and confirmed by Αlpha¹ Editorial · August 16, 2026

Αlpha¹ Rigor Review

Rigor Reviewed

2.25 / 5

Weak — serious problems found

Interventional Study

Critical

High-priority

How this rating was calculated▸

Started at 5★ — deductions

IntegrityIntegrity concern ×3−1.5★
ClaimsEfficacy rests on an unvalidated surrogate endpoint−0.5★
ClaimsTreatment effect not shown to be clinically meaningful−0.5★
ReportingData & code availability partially met−0.25★

Model-reported confidence: highReviewer agreement: highEngine: 7.39.0Source: full text

Not verified in this report

The numeric-impossibility checks (GRIM/GRIMMER/DEBIT/SPRITE) did not run: 17 reported means were read, and their group size is not stated where the values are printed (this source has no machine-readable table structure). These checks need the count the mean was averaged over, so none was performed.

Problems surfaced7 types

Numerical inconsistencies1

Values that contradict each other or are impossible for the stated sample: recomputed p-values and test statistics, GRIM/GRIMMER checks on summary numbers, percentages against their own counts, totals against their parts, and estimates against their own confidence intervals.

Internal contradictions in the reported numbersAssessed

Overstated conclusions3

Conclusions that reach past what the paper's own results support — including a significance claim that no longer holds when the statistic is recomputed, and efficacy resting on an unvalidated surrogate endpoint.

Efficacy rests on an unvalidated surrogate endpointAssessed
Treatment effect not shown to be clinically meaningfulAssessed
Conclusions only partially backed by the presented evidenceAssessed

Data authenticity concerns2

An adversarial read for patterns associated with data that may not be genuine: results that look too clean, implausibly large effects, duplicated data or images, and methods that do not match the results reported.

Methods and results do not matchAssessed
Other integrity concernAssessed

Reporting gaps1

Required detail the manuscript never states — study design, biological variables, ethics approval and consent, key resources, statistical reporting, data and code availability, and overall transparency.

Data/code availability incompleteAssessed

This Αlpha¹ Rigor Review uses AI Rigor Reviewers trained on a curated corpus of high-fidelity and retracted papers, with expert supervision and curation. It can still make mistakes; verify each finding against the source before relying on it.

This is a well-conducted, rigorously reported multicenter RCT comparing deep learning versus standard morphology for embryo selection in IVF. The main methodological strengths are the randomized double-blind design, pre-specified analysis populations, and transparent reporting. The primary weakness is the vague data availability statement and proprietary code, which limit reproducibility.

Both reviewers classified the study as interventional, and this was adopted. The evaluation covered all eight dimensions; several sub-criteria were marked not applicable (e.g., animal-related items, cell line authentication) due to the human clinical trial context. The statistics verification recomputed 7 tests, all consistent; the citation check found no retracted or missing references.

5 major claims checked against the paper's own evidence: 2 only partially supported (evidence backs part of the claim; gaps or caveats remain); the rest adequately supported.

Premise concern: surrogate not validated for clinical benefit; effect size not shown to be clinically meaningful.

Recomputed 7 tests: 7 consistent, 0 inconsistent; 7 via agent-written checks.

3 integrity concerns flagged (0 high).

Reporting

Eight Dimensions7 pass · 1 warn

The introduction cites prior studies on AI in embryo selection, acknowledges the lack of RCTs for deep learning, and justifies the need for a noninferiority trial. The rationale is well-linked to the study objectives, and limitations of prior research (retrospective designs, small samples) are addressed by the prospective RCT design.

Sub-criteria

prior work citedADEQUATE

premise rationaleADEQUATE

limitations addressedADEQUATE

Per reviewer (2) — the votes behind the verdict

PASSReviewer 1· 90% conf

The paper establishes a clear scientific premise, citing prior work and providing a logical rationale for the trial.

Evidence

direct quote[Introduction, paragraph 3]

“A number of studies of AI have suggested significant improvements in consistency of embryo scoring , , but these evaluations have been retrospective. Two previous studies , have prospectively examined the efficacy of traditional machine learning algorithms based on previous data and human input for embryo selection.”