Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Vector Algebra: Method of Components

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

Response Surface Methodology

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

Statistical Analysis System (SAS)

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...

Introduction to Vectors

Introduction to Vectors

To define some physical quantities, there is a need to specify both magnitude as well as direction. For example, when the U.S. Coast Guard dispatches a ship or a helicopter for a rescue mission, the rescue team needs to know not only the distance to the distress signal, but also the direction from which the signal is coming, so that they can get to it as quickly as possible. Physical quantities specified completely with a number of units (magnitude) and a direction are called vector quantities.

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Vector Algebra: Graphical Method

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...

You might also read

Related Articles

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

Sort by

Same author

Short-term Patient-Reported Outcomes After Primary Anterior Cruciate Ligament Reconstruction With and Without Lateral Extra-articular Tenodesis: A Matched-Cohort Analysis From the Swedish Knee Ligament Registry.

The American journal of sports medicine·2026

Same author

Deep learning methods for automated orthopaedic measurements in computed tomography scans.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2026

Same author

Magnetic Resonance Imaging Assessment of Bridge-Enhanced ACL Restoration: An Imaging Atlas of Morphological Features and Healing Trajectories.

Orthopaedic journal of sports medicine·2026

Same author

Deep learning methods for automated orthopaedic measurements in radiographs.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2026

Same author

From patient notes to prognostication: The revolutionary potential of event-based foundation models in orthopaedics.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2026

Same author

From code to care in hours: Why "go fast and fix things" is the new medical imperative.

Journal of experimental orthopaedics·2026

Same journal

Bionic Reconstruction of the Upper Extremity.

Hand clinics·2026

Same journal

Erratum.

Hand clinics·2026

Same journal

Occupational Therapist Perspectives in Upper Limb Prosthetic Rehabilitation.

Hand clinics·2026

Same journal

Mental Health: The Invisible Challenge and the Opportunity.

Hand clinics·2026

Same journal

The Role of Amputation and Myoelectric Prosthetic Fitting in Traumatic Adult Brachial Plexus Injury.

Hand clinics·2026

Same journal

Osseointegration of the Upper Extremity.

Hand clinics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Support Vector Machines: Techniques and Applications.

James A Pruneski¹, Ayoosh Pareek²

¹Department of Orthopaedic Surgery, Tripler Army Medical Center, 1 Jarrett White Road, Honolulu, HI 96859, USA. Electronic address: https://twitter.com/PruneskiJ.

|November 8, 2025

Summary

This summary is machine-generated.

Support vector machines (SVMs) are powerful tools in health care for classification and prediction. Research shows SVMs excel in high-dimensional data for orthopedic and plastic surgery, though limitations exist.

Keywords:

Artificial intelligence Machine learning Orthopedic Plastic Support vector machine Surgery

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

Related Experiment Videos

Last Updated: Jan 11, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

Area of Science:

Biomedical data analysis
Machine learning in healthcare

Background:

Support vector machines (SVMs) are extensively used in health care research.
They are applied for classification, regression, and outlier detection.
SVMs create hyperplanes to maximize class separation in feature spaces for accurate predictions.

Purpose of the Study:

To highlight the utility and variations of SVMs in health care.
To discuss their application in orthopedic and plastic surgery for diagnosis and outcome prediction.
To acknowledge their strengths in high-dimensional data and identify areas for future improvement.

Main Methods:

Utilizing linear, nonlinear (kernel-based), and multiclass SVM variations.
Applying SVMs to analyze high-dimensional datasets in medical research.
Reviewing successful implementations in orthopedic and plastic surgery studies.

Main Results:

SVMs demonstrate effectiveness in classification, regression, and outlier detection tasks.
Successful application of SVMs for diagnosis and outcome prediction in orthopedic and plastic surgery.
SVMs are robust for handling high-dimensional datasets.

Conclusions:

Support vector machines are a robust and popular technique in health care research.
Their effectiveness is noted in specific surgical fields like orthopedics and plastic surgery.
Further research is beneficial to enhance the capabilities of SVMs.