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Related Concept Videos

Geometric Mean01:15

Geometric Mean

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The mean is a measure of the central tendency of a data set. In some data sets, the data is inherently multiplicative, and the arithmetic mean is not useful. For example, the human population multiplies with time, and so does the credit amount of financial investment, as the interest compounds over successive time intervals.
In cases of multiplicative data, the geometric mean is used for statistical analysis. First, the product of all the elements is taken. Then, if there are n elements in the...
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Variation: Normal Distribution, Range, and Standard Deviation02:32

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Standard Deviation01:10

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The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
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Mean Absolute Deviation01:13

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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
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Standard Deviation of Calculated Results01:14

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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.
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Calculating Standard Deviation01:08

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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...
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Endoscopic Septoplasty with Limited Two-line Resection: Minimally Invasive Surgery for Septal Deviation
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Geometric morphometric contribution to septal deviation analysis.

Thomas Radulesco1,2, Djamel Hazbri3,4, Patrick Dessi3

  • 1Department of Oto-Rhino-Laryngology and Head and Neck Surgery, APHM, La Conception University Hospital, 147 Bvd Baille, 13005, Marseille, France. thomas.radulesco@ap-hm.fr.

Surgical and Radiologic Anatomy : SRA
|April 3, 2019
PubMed
Summary

Geometric morphometrics (GM) classified nasal septum deviations (SD) into S-shaped and C-shaped conformations. These distinct septal deviations showed significant differences in nasal resistance compared to controls.

Keywords:
ClusterGeometric morphometricsLandmarksNasal obstructionNasal septumSeptal deviation

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Area of Science:

  • Otolaryngology
  • Biomedical Engineering
  • Anatomy

Background:

  • The nasal septum exhibits significant individual variations in shape.
  • Understanding these variations is crucial for diagnosing and treating nasal obstruction.

Purpose of the Study:

  • To develop a validated protocol for nasal septum analysis using geometric morphometrics (GM).
  • To establish a classification system for septal deviations (SD) based on their conformations.

Main Methods:

  • Retrospective study of CT scans from patients with SD and a control group.
  • 3D segmentation and landmark analysis of the nasal septum using GM.
  • Cluster analysis to classify septal conformations and comparison of nasal resistances.

Main Results:

  • Two distinct septal deviation conformations were identified: S-shaped and C-shaped.
  • Measurement error was 7.9%, indicating a reliable protocol.
  • Both S-shaped and C-shaped deviations showed statistically significant differences in nasal resistance compared to controls.

Conclusions:

  • A reliable and reproducible classification of septal deviations based on GM was established.
  • This classification aids in understanding the relationship between nasal septum conformation and function.
  • Improved understanding can enhance the diagnosis and treatment of nasal obstruction.