Evidence map›Paper›PMID 37259154›Full record

ArticleEye and vision (London, England)2023

The Zhu-Lu formula: a machine learning-based intraocular lens power calculation formula for highly myopic eyes.

Dongling Guo, Wenwen He, Ling Wei, Yunxiao Song, Jiao Qi, Yunqian Yao, Xu Chen, Jinhai Huang, Yi Lu, Xiangjia Zhu

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PubMed Full text at PMC Publisher
In one paragraph

Article in Eye and vision (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 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

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

10 authors.

Dongling Guo *Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China.
Wenwen He *Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China.
Ling Wei *Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China.
Yunxiao Song *University of Illinois at Urbana-Champaign, Illinois, USA.
Jiao QiEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China.
Yunqian YaoEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China.
Xu ChenShanghai Aier Eye Hospital, Shanghai, China.
Jinhai HuangEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China.
Yi LuEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China. luyieent@163.com.
Xiangjia ZhuEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China. zhuxiangjia1982@126.com.ORCID http://orcid.org/0000-0001-5588-1667

Funding

Clinical Science and Technology Innovation Project of Shanghai Shenkan Hospital Development Center SHDC12019X08Clinical Science and Technology Innovation Project of Shanghai Shenkan Hospital Development Center SHDC12020111Clinical Science and Technology Innovation Project of Shanghai Shenkan Hospital Development Center SHDC2020CR4078Double-E Plan of Eye & ENT Hospital SYA202006National Natural Science Foundation of China 81470613National Natural Science Foundation of China 81670835National Natural Science Foundation of China 81870642National Natural Science Foundation of China 81970780National Natural Science Foundation of China 82122017National Natural Science Foundation of China 82271069Science and Technology Innvation Action Plan of Shanghai Science and Technology Commission 19441900700Science and Technology Innvation Action Plan of Shanghai Science and Technology Commission 21S31904900Shanghai Municipal Key Clinical Specialty Program shslczdzk01901
6 · The paper itself

Abstract

backgroundTo develop a novel machine learning-based intraocular lens (IOL) power calculation formula for highly myopic eyes.

methodsA total of 1828 eyes (from 1828 highly myopic patients) undergoing cataract surgery in our hospital were used as the internal dataset, and 151 eyes from 151 highly myopic patients from two other hospitals were used as external test dataset. The Zhu-Lu formula was developed based on the eXtreme Gradient Boosting and the support vector regression algorithms. Its accuracy was compared in the internal and external test datasets with the Barrett Universal II (BUII), Emmetropia Verifying Optical (EVO) 2.0, Kane, Pearl-DGS and Radial Basis Function (RBF) 3.0 formulas.

resultsIn the internal test dataset, the Zhu-Lu, RBF 3.0 and BUII ranked top three from low to high taking into account standard deviations (SDs) of prediction errors (PEs). The Zhu-Lu and RBF 3.0 showed significantly lower median absolute errors (MedAEs) than the other formulas (all P < 0.05). In the external test dataset, the Zhu-Lu, Kane and EVO 2.0 ranked top three from low to high considering SDs of PEs. The Zhu-Lu formula showed a comparable MedAE with BUII and EVO 2.0 but significantly lower than Kane, Pearl-DGS and RBF 3.0 (all P < 0.05). The Zhu-Lu formula ranked first regarding the percentages of eyes within ± 0.50 D of the PE in both test datasets (internal: 80.61%; external: 72.85%). In the axial length subgroup analysis, the PE of the Zhu-Lu stayed stably close to zero in all subgroups.

conclusionsThe novel IOL power calculation formula for highly myopic eyes demonstrated improved and stable predictive accuracy compared with other artificial intelligence-based formulas.

Indexed as

High myopiaIOL power calculationMachine learningPrediction error

Identifiers

PMID37259154
PMCPMC10233923

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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.