Evidence map›Paper›PMID 40988042›Full record

ArticleBMC ophthalmology2025

Comparative evaluation of traditional and AI-based intraocular lens power calculation formulas in highly myopic eyes.

Xiaopeng Jiang, Jiangjie Wang, Qingmin Jiang, Xiangyu Zhou, Fei Xia, Meng Gao

Abstract readComparative Study
In one paragraph

Article in BMC ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xiaopeng JiangDepartment of Ophthalmology, Affiliated Hospital of Shandong Second Medical University, 4948 Shengli East Street, Weifang, 261042, China.
Jiangjie WangDepartment of Ophthalmology, Affiliated Hospital of Shandong Second Medical University, 4948 Shengli East Street, Weifang, 261042, China.
Qingmin JiangDepartment of Ophthalmology, Affiliated Hospital of Shandong Second Medical University, 4948 Shengli East Street, Weifang, 261042, China.
Xiangyu ZhouDepartment of Ophthalmology, Affiliated Hospital of Shandong Second Medical University, 4948 Shengli East Street, Weifang, 261042, China.
Fei XiaDepartment of Ophthalmology, Affiliated Hospital of Shandong Second Medical University, 4948 Shengli East Street, Weifang, 261042, China. feixia925929@outlook.com.
Meng GaoDepartment of Ophthalmology, Affiliated Hospital of Shandong Second Medical University, 4948 Shengli East Street, Weifang, 261042, China. gaomeng@sdsmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo assess the accuracy of artificial intelligence (AI)-based intraocular lens (IOL) power calculation formulas compared with traditional methods in highly myopic eyes, and to evaluate their performance across varying axial lengths and corneal curvatures.

methodsThis retrospective case series included 115 highly myopic eyes that underwent phacoemulsification with IOL implantation. IOL power was calculated using four conventional formulas (SRK/T, Haigis, Holladay 2, Barrett Universal II) and seven AI-based formulas (Hill-RBF 3.0, Karmona, Hoffer QST, PEARL-DGS, Ladas Super Formula, Kane, HM-ZL). The outcomes were evaluated using standard deviation (SD), assessed with Heteroscedastic test; root-mean-square absolute error (RMSAE), assessed with bootstrap-t method; mean absolute error (MAE), assessed with Friedman test; and the percentage of eyes within ± 0.25 D to ± 1.00 D of prediction error, assessed with Cochran's Q test. Subgroup analyses were performed based on axial length (AL) and corneal curvature (Kmean).

resultsMost AI-based formulas-especially Hill-RBF 3.0, PEARL-DGS and Kane-demonstrated higher accuracy than traditional formulas.

resultsOverall, the MAEs of Hill-RBF 3.0, PEARL-DGS, and Kane were significantly lower than that of Holladay 2 (P < 0.05). The SD of PEARL-DGS also differed significantly from Holladay 2 (P < 0.05). In the long axial length group, Hill-RBF 3.0, PEARL-DGS, and Kane showed significantly lower MAEs than Holladay 2 (P < 0.05). In the moderate corneal curvature group, BUⅡ, Hill-RBF 3.0, Hoffer QST, PEARL-DGS, and Kane had significantly lower MAEs than Holladay 2, and the SDs of Hill-RBF 3.0 and PEARL-DGS differed significantly from both Holladay 2 and SRK/T (P < 0.05). Trend lines showed that AI-based formulas exhibited more consistent and stable performance across different AL and K

conclusionAI-based formulas provide superior refractive prediction in highly myopic eyes compared with traditional methods. Tailored formula selection based on biometric profiles may enhance refractive outcomes in cataract surgery.

Indexed as

Artificial IntelligenceLenses, IntraocularMyopia, DegenerativeOptics and PhotonicsRefraction, OcularAdultAgedAxial Length, EyeBiometryFemaleHumansLens Implantation, IntraocularMaleMiddle AgedPhacoemulsificationRetrospective StudiesArtificial intelligenceHigh myopiaIntraocular lensIOL power formulaPrediction accuracy

Identifiers

PMID40988042
PMCPMC12459027

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.