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Improving clinical refractive results of cataract surgery by machine learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/jcc.24842openalex-snapshot
Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins
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Some extensions of radial basis functions and their applications in artificial intelligence
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Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks
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iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
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Multivariate hyperbolic tangent neural network approximation
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Neural network ensemble operators for time series forecasting
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A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research
inferredcites →https://doi.org/10.1016/j.jcrs.2005.12.139openalex-snapshot
Prediction of the effective postoperative (intraocular lens) anterior chamber depth
inferredcites →https://doi.org/10.1016/j.jcrs.2007.10.031openalex-snapshot
Sources of error in intraocular lens power calculation
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Evaluation of 2 new optical biometry devices and comparison with the current gold standard biometer
inferredcites →https://doi.org/10.1016/j.jcrs.2014.06.033openalex-snapshot
Intraocular lens power calculation for eyes with an axial length greater than 26.0 mm: Comparison of formulas and methods
inferredcites →https://doi.org/10.1016/j.jcrs.2014.10.044openalex-snapshot
Comparison of visual recovery and refractive stability between femtosecond laser–assisted cataract surgery and standard phacoemulsification: Six-month follow-up
inferredcites →https://doi.org/10.1016/j.jcrs.2015.10.036openalex-snapshot
Measuring the cataractous lens
inferredcites →https://doi.org/10.1016/j.jcrs.2016.06.029openalex-snapshot
Comparison of 9 intraocular lens power calculation formulas
inferredcites →https://doi.org/10.1016/j.jcrs.2016.07.021openalex-snapshot
Intraocular lens power formula accuracy: Comparison of 7 formulas
inferredcites →https://doi.org/10.1016/j.jcrs.2016.12.021openalex-snapshot
Accuracy of 3 new methods for intraocular lens power selection
inferredcites →https://doi.org/10.1016/j.jcrs.2017.06.006openalex-snapshot
Pursuing perfection in intraocular lens calculations: I. Logical approach for classifying IOL calculation formulas
inferredcites →https://doi.org/10.1016/j.jcrs.2017.08.003openalex-snapshot
Pursuing perfection in intraocular lens calculations: III. Criteria for analyzing outcomes
inferredcites →https://doi.org/10.1016/j.jcrs.2018.01.015openalex-snapshot
Accuracy of 8 intraocular lens calculation formulas in relation to anterior chamber depth in patients with normal axial lengths
inferredcites →https://doi.org/10.1016/j.jcrs.2018.05.021openalex-snapshot
Comparison of 9 modern intraocular lens power calculation formulas for a quadrifocal intraocular lens
inferredcites →https://doi.org/10.1016/j.jcrs.2018.07.023openalex-snapshot
Precision of intraocular lens power prediction in eyes shorter than 22 mm: An analysis of 6 formulas
inferredcites →https://doi.org/10.1016/j.jesp.2013.03.013openalex-snapshot
Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median
inferredcites →https://doi.org/10.1016/j.jhydrol.2006.01.021openalex-snapshot
Support vector regression for real-time flood stage forecasting
inferredcites →https://doi.org/10.1016/j.ophtha.2013.07.056openalex-snapshot
Cost-Effectiveness of Femtosecond Laser-Assisted Cataract Surgery versus Phacoemulsification Cataract Surgery
inferredcites →https://doi.org/10.1016/j.ophtha.2017.08.027openalex-snapshot
Accuracy of Intraocular Lens Calculation Formulas
inferredcites →https://doi.org/10.1016/j.sjopt.2011.11.007openalex-snapshot
Challenges and approaches in modern biometry and IOL calculation
inferredcites →https://doi.org/10.1016/s0002-9394(14)74917-7openalex-snapshot
Biometry of 7,500 Cataractous Eyes
inferredcites →https://doi.org/10.1016/s0039-6257(99)00085-5openalex-snapshot
Phacoemulsification and Modern Cataract Surgery
inferredcites →https://doi.org/10.1016/s0886-3350(13)80705-5openalex-snapshot
Development of the SRK/T intraocular lens implant power calculation formula
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Comparison of intraocular lens computations using a neural network versus the Holladay formula
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A tutorial on support vector regression
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Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology.
inferredcites →https://doi.org/10.1038/sj.eye.6702954openalex-snapshot
Benchmark standards for refractive outcomes after NHS cataract surgery
inferredcites →https://doi.org/10.1049/cp:19991091openalex-snapshot
Support vector learning for ordinal regression
inferredcites →https://doi.org/10.1080/01621459.1946.10501898openalex-snapshot
The Statistical Sign Test
inferredcites →https://doi.org/10.1097/icu.0000000000000225openalex-snapshot
Intraocular lens alignment methods
inferredcites →https://doi.org/10.1097/icu.0b013e3283414f36openalex-snapshot
Functional visual acuity measurement in cataract and intraocular lens implantation
inferredcites →https://doi.org/10.1103/physreve.91.042914openalex-snapshot
Numerical study of extreme events in a laser diode with phase-conjugate optical feedback
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Efficient training of RBF neural networks for pattern recognition
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Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
inferredcites →https://doi.org/10.1109/ijcnn.2000.859420openalex-snapshot
Support vector machine for regression and applications to financial forecasting
inferredcites →https://doi.org/10.1109/iske.2008.4731075openalex-snapshot
Fast training Support Vector Machines using parallel sequential minimal optimization
inferredcites →https://doi.org/10.1109/itsc.2003.1252721openalex-snapshot
Travel time prediction with support vector regression
inferredcites →https://doi.org/10.1109/sp.2018.00038openalex-snapshot
Stealing Hyperparameters in Machine Learning
inferredcites →https://doi.org/10.1109/tnn.2004.836233openalex-snapshot
Smooth Function Approximation Using Neural Networks
inferredcites →https://doi.org/10.1111/ceo.13034openalex-snapshot
Comparison of Hill‐radial basis function, Barrett Universal and current third generation formulas for the calculation of intraocular lens power during cataract surgery
inferredcites →https://doi.org/10.1111/j.1541-0420.2010.01408.xopenalex-snapshot
Multiple McNemar Tests
inferredcites →https://doi.org/10.1111/j.1600-0420.2007.00879.xopenalex-snapshot
Calculation of intraocular lens power: a review
inferredcites →https://doi.org/10.1111/opo.12009openalex-snapshot
Statistical guidelines for the analysis of data obtained from one or both eyes
inferredcites →https://doi.org/10.1136/bjophthalmol-2011-300539openalex-snapshot
Global estimates of visual impairment: 2010
inferredcites →https://doi.org/10.1137/11s010840openalex-snapshot
Choosing Basis Functions and Shape Parameters for Radial Basis Function Methods
inferredcites →https://doi.org/10.1162/neco.1991.3.2.246openalex-snapshot
Universal Approximation Using Radial-Basis-Function Networks
inferredcites →https://doi.org/10.1167/iovs.16-19894openalex-snapshot
Cataract Surgical Rate and Socioeconomics: A Global Study
inferredcites →https://doi.org/10.1186/s12859-016-1163-xopenalex-snapshot
Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs
inferredcites →https://doi.org/10.1186/s12859-016-1369-yopenalex-snapshot
Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins
inferredcites →https://doi.org/10.1533/9780857099440openalex-snapshot
Machine learning and data mining
inferredcites →https://doi.org/10.17925/eor.2011.05.02.134openalex-snapshot
IOLMaster® 500 and Integration of the Holladay 2 Formula for Intraocular Lens Calculations
inferredcites →https://doi.org/10.20470/jsi.v5i1.178openalex-snapshot
Dealing with Missing Values in Data
inferredcites →https://doi.org/10.3310/hta18680openalex-snapshot
The clinical effectiveness and cost-effectiveness of second-eye cataract surgery: a systematic review and economic evaluation
inferredcites →https://doi.org/10.3390/s90806312openalex-snapshot
A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification
inferredcites →https://doi.org/10.4068/cmj.2015.51.2.91openalex-snapshot
Factors Affecting the Accuracy of Intraocular Lens Power Calculation with Lenstar
inferredcites →https://doi.org/10.5402/2012/324194openalex-snapshot
Using Radial Basis Function Networks for Function Approximation and Classification
inferredcites →https://doi.org/10.7717/peerj-cs.177openalex-snapshot
SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/aisy.202500055openalex-snapshot
Mainstream Artificial Intelligence Technologies in Contemporary Ophthalmology
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Overview of Artificial Intelligence Systems in Ophthalmology
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Overview of Intraocular Lens Power Calculation Formulas Based on Artificial Intelligence
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Overview of Artificial Intelligence Systems in Ophthalmology
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Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis
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Systematic evaluation of machine learning-enhanced trifocal IOL power selection for axial myopia cataract patients
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Additional file 1 of The Zhu-Lu formula: a machine learning-based intraocular lens power calculation formula for highly myopic eyes
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