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NCT Number: NCT07451509

Implementation of Machine Learning Into Optical Pedography

This study aims to evaluate the validity and reliability of a proposed plantar pressure assessment intrument based on an implementation of machine learning into optical pedography.

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Key information

Conditions

Healthy Foot Pressure Area Pressure Distribution

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Faculty of Health Sciences, Palacký University Olomouc, Czech Republic

Olomouc, Czechia, 77900

Location contact

Jonatan Dvořáček

CONTACT

[email protected]

+420731061089

Jonatan Dvořáček

PRINCIPAL_INVESTIGATOR

Petr Konečný

SUB_INVESTIGATOR

About this study

This study aims to evaluate the validity and reliability of a proposed plantar pressure assessment intrument based on an implementation of machine learning into optical pedography. The whole process will be divided into distinct steps required for the targeted outcome, which includes:

  • collecting visual data (podoscope foot pictures) and training a segmentation machine learning-based algorithm designed for recognizing only feet area, a total of atleast 30 participants performing 9 different standing positions - over 270 usable pictures for training and functionality validation
  • collecting personal, visual and pressure data (participant weight, podoscope foot pictures, pedobarographic platform measurements) and training a machine/deep learning-based model designed for feet pressure distribution areas identification and quantification, a total of estimated 60 participants undergoing 5 alternating, as similar as possible, measurements on podoscope and pedobarographic platform
  • evaluating the validity and reliability of a new plantar pressure measuring instrument following the same imaging procedure as described in step 2, a total of estimated 60 participants

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • A healthy individual with no absence of lower extremity
  • Aged ≥18 years
  • Willingness to participate and ability to follow the assessors instructions.

Exclusion criteria

  • Presence of foot diseases alternating foot contact area
  • Cognitive or psychiatric disorders limiting cooperation
  • Lack of informed consent or non-compliance during imaging

Treatment and study plan

Optical pedography

Diagnostic Test

Comparison of 5 alternating measures taken on podoscope and pedobarographic platform

Primary outcomes

  1. Criterion validity of foot area measurement

    Time frame: Day 1

    The total contact area of the foot during a static stance will be measured by the machine learning based optical pedography Instrument and compared to the gold standard pedobarographic platform. The validity will be determined by the Pearson Correlation Coefficient (r) between the two devices. Unit of measure: Pearson Correlation Coefficient (r) ranging from -1 to 1.

  2. Criterion validity of peak plantar pressure distribution

    Time frame: Day 1

    The distribution of pressure across the plantar surface (specifically mean peak pressure) will be measured. Validity will be assessed by calculating the Intraclass Correlation Coefficient (ICC) between the machine learning based optical pedography instrument and the gold standard pedobarographic platform. Unit of measure: Intraclass Correlation Coefficient (ICC) ranging from 0 to 1.

  3. Intrasession reliability of foot area measurement

    Time frame: Day 1

    The consistency of the total contact area (cm 2) measured across 5 repeated trials within a single session. Reliability will be assessed using the Intraclass Correlation Coefficient (ICC 3,5) to determine the degree of agreement between the five captures of the same participant's feet. Units of measure: Intraclass Correlation Coefficient (ICC) ranging from 0 to 1

  4. Intrasession reliability of mean peak pressure

    Time frame: Day 1

    The consistency of mean peak pressure measurements across 5 repeated trials within a single session. Reliability will be assessed by calculating the Intraclass Correlation Coefficient (ICC 3,5) for the five measurements taken on the machine learning based optical pedography instrument. Unit of measure: Intraclass Correlation Coefficient (ICC) ranging from 0 to 1

Study contacts

Contact information is provided by the study sponsor or research team.

Jonatan Dvořáček

CONTACT

[email protected]

+420731061089

Petr Konečný

CONTACT

[email protected]

+420604573931

Sponsors and collaborators

Lead sponsor

Palacky University

Other

Registry information

Official study title

Implementing Machine Learning Into Optical Pedography: Development and Validation of a Novel Instrument: a Validation Study

Important dates

Study start
2026
Primary completion
2026
Study completion
2027
First posted
Mar 5, 2026
Registry last updated
Mar 5, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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