Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.9K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.9K
Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

6.8K
Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
6.8K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Calculating Standard Deviation01:08

Calculating Standard Deviation

7.8K
The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high...
7.8K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

712
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
712
Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

9.3K
The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
9.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

In vitro optical characterization of the RayOne Galaxy spiral extended depth-of-focus intraocular lens using high-resolution Mach-Zehnder interferometry.

Biomedical optics express·2026
Same author

First Report of Circular Rainbow Glare After Femtosecond Laser-Assisted LASIK.

Journal of refractive surgery (Thorofare, N.J. : 1995)·2026
Same author

Simplification of a Three-Constant Intraocular Lens Calculation Formula to a Single-Constant Approach: The Haigis Formula.

Diagnostics (Basel, Switzerland)·2026
Same author

Intraocular Lens Calculation Concept Based on Aphakic Refraction-Considerations on a Cornea Model With Two Refracting Surfaces.

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)·2026
Same author

IOL power calculation in cataract surgery: closed-form sensitivities to biometric uncertainties.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same author

Response to the Letter to the Editor: Dual-Zone Keratometry for Identifying Central Radius and Corneal Asphericity.

Current eye research·2026

Related Experiment Video

Updated: Sep 12, 2025

Scanning Light Scattering Profiler SLPS Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses
06:55

Scanning Light Scattering Profiler SLPS Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses

Published on: June 6, 2017

7.7K

A two-step formula constant optimization strategy for minimal standard deviation and zero mean prediction error in

Achim Langenbucher1, Nóra Szentmáry2,3, Jascha Wendelstein1,4

  • 1Department of Experimental Ophthalmology, Saarland University, Saarbrücken, Germany.

Acta Ophthalmologica
|August 5, 2025
PubMed
Summary

A new two-step method optimizes intraocular lens (IOL) formula constants and refractive offset, achieving excellent precision and accuracy. This approach effectively reduces prediction scatter and eliminates refractive errors for improved surgical outcomes.

Keywords:
IOL power formulalens constant optimizationnonlinear optimizationrefractive outcometwo‐step strategy

More Related Videos

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

2.8K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K

Related Experiment Videos

Last Updated: Sep 12, 2025

Scanning Light Scattering Profiler SLPS Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses
06:55

Scanning Light Scattering Profiler SLPS Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses

Published on: June 6, 2017

7.7K
Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

2.8K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K

Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Optics

Background:

  • Accurate intraocular lens (IOL) power calculation is crucial for achieving desired refractive outcomes after cataract surgery.
  • Traditional IOL formulas often require optimization of constants (FC) and may exhibit systematic refractive errors.
  • Refractive offset (RO) correction is a potential method to improve prediction accuracy.

Purpose of the Study:

  • To evaluate the precision and accuracy of a novel two-step optimization approach for IOL formula constants (FC).
  • To assess the performance of refractive offset (RO) correction as a secondary tuning parameter in IOL power calculations.
  • To compare the proposed method with traditional optimization strategies for established IOL formulas.

Main Methods:

  • Utilized IOLMaster 700 biometric data from four diverse datasets, including different IOL models.
  • Optimized FC and RO for SRKT, Hoffer Q, Holladay 1, and Haigis formulas.
  • Employed iterative nonlinear optimization for root mean square prediction error (RMSPE) and a sequential Gatinel method optimizing FC for standard deviation prediction error (SDPE) first, then RO for mean prediction error.

Main Results:

  • The two-step optimization approach demonstrated comparable precision and accuracy across all tested formulas and datasets.
  • Differences in formula prediction error between optimization strategies were minimal (third decimal place) and clinically insignificant.
  • Direct FC optimization for SDPE resulted in substantial constant offsets and systematic refractive errors, particularly for Hoffer Q and Haigis formulas.

Conclusions:

  • The simple two-step method for optimizing FC and RO offers excellent performance, reducing scatter and zeroing refractive offset.
  • This approach is highly effective for the tested IOL formulas and datasets.
  • Further multicenter studies with diverse populations and biometers are recommended to confirm clinical applicability.