Application of total keratometry in ten intraocular lens power calculation formulas in highly myopic eyes

Wei, Ling; Cheng, Kaiwen; He, Wenwen; Zhu, Xiangjia; Lu, Yi

doi:10.1186/s40662-022-00293-3

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…Furthermore, the XGBoost and SVR algorithms are both highly accurate in data prediction and have helped with numerous situations in ophthalmology, such as diabetic retinopathy diagnosis, implantable collamer lens size selection, and post-cataract-surgery focus depth prediction [ 23 – 25 ]. Using the XGBoost, our group has previously developed an enhanced calculator for the BUII formula, which demonstrated improved prediction accuracy in highly myopic eyes [ 26 , 27 ]. Yet this enhanced calculator might be influenced by the internal optimizations of the BUII formula itself, which is still unpublished to the public.…”

Section: Discussionmentioning

confidence: 99%

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

Guo

Wei

et al. 2023

Eye and Vis

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Background To develop a novel machine learning-based intraocular lens (IOL) power calculation formula for highly myopic eyes. Methods A 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. Results In 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. Conclusions The novel IOL power calculation formula for highly myopic eyes demonstrated improved and stable predictive accuracy compared with other artificial intelligence-based formulas.

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Section: Discussionmentioning

confidence: 99%

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

Guo

Wei

et al. 2023

Eye and Vis

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“…The present study is the first network meta-analysis to evaluate the accuracy of AI-based formulas for IOL power calculation in myopic eyes with axial lengths >26.0 mm. To clearly discuss the characteristics of each formula, we divided the 21 formulas into the following four types: (1) AI-based formulas; (2) newer The XGBoost formula was designed exclusively for myopic eyes (19,26). It was developed and validated using data from 1,450 patients.…”

Section: Discussionmentioning

confidence: 99%

The accuracy of intraocular lens power calculation formulas based on artificial intelligence in highly myopic eyes: a systematic review and network meta-analysis

Zhou,

Dai,

Sun

et al. 2023

Front. Public Health

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ObjectiveTo systematically compare and rank the accuracy of AI-based intraocular lens (IOL) power calculation formulas and traditional IOL formulas in highly myopic eyes.MethodsWe screened PubMed, Web of Science, Embase, and Cochrane Library databases for studies published from inception to April 2023. The following outcome data were collected: mean absolute error (MAE), percentage of eyes with a refractive prediction error (PE) within ±0.25, ±0.50, and ±1.00 diopters (D), and median absolute error (MedAE). The network meta-analysis was conducted by R 4.3.0 and STATA 17.0.ResultsTwelve studies involving 2,430 adult myopic eyes (with axial lengths >26.0 mm) that underwent uncomplicated cataract surgery with mono-focal IOL implantation were included. The network meta-analysis of 21 formulas showed that the top three AI-based formulas, as per the surface under the cumulative ranking curve (SUCRA) values, were XGBoost, Hill-RBF, and Kane. The three formulas had the lowest MedAE and were more accurate than traditional vergence formulas, such as SRK/T, Holladay 1, Holladay 2, Haigis, and Hoffer Q regarding MAE, percentage of eyes with PE within ±0.25, ±0.50, and ±1.00 D.ConclusionsThe top AI-based formulas for calculating IOL power in highly myopic eyes were XGBoost, Hill-RBF, and Kane. They were significantly more accurate than traditional vergence formulas and ranked better than formulas with Wang–Koch AL modifications or newer generations of formulas such as Barrett and Olsen.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022335969.

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“…XGB is also a GBDT-based algorithm, which performs excellently in many competitions and practical applications [21,22]. In the eld of PE risk prediction, XGB has been proven to effectively address imbalanced data problems and handle various types of features.…”

Section: Xgboostmentioning

confidence: 99%

Development and Validation of a Deep Learning-Enhanced Prediction Model for the Likelihood of Pulmonary Embolism

Tian

Wang

et al. 2023

Preprint

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Introduction Pulmonary embolism (PE) is a common and potentially fatal disease, and timely and accurate assessment of the risk of PE occurrence in patients with Deep Vein Thrombosis (DVT) is crucial. This study aims to develop a precise and efficient deep learning-based PE risk prediction model, PE-Mind. Materials and Methods We first preprocessed and reduced the high-dimensional clinical features collected from patients. The 37 most important clinical features were grouped, sorted, and connected to capture potential associations between them. The proposed model utilizes a convolutional approach, including three custom-designed residual modules. To validate the model's superiority, we also compared it with five mainstream models. Results The results show that PE-Mind demonstrated the highest accuracy and reliability, achieving an accuracy of 0.7826 and an area under the receiver operating characteristic curve of 0.8641 on the prospective test set, outperforming other models. Based on this, we have also developed a Web server, PulmoRiskAI, for real-time physician operation. Conclusions The proposed method has the potential to become a practical clinical tool, providing doctors with more accurate PE risk assessments and timely identification of high-risk patients.

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Application of total keratometry in ten intraocular lens power calculation formulas in highly myopic eyes

Cited by 11 publications

References 37 publications

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

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

The accuracy of intraocular lens power calculation formulas based on artificial intelligence in highly myopic eyes: a systematic review and network meta-analysis

Development and Validation of a Deep Learning-Enhanced Prediction Model for the Likelihood of Pulmonary Embolism

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