Implementation of Machine Learning Into Optical Pedography

NCT07451509 | Not Yet Recruiting

• 1 other identifier: UPOL-340091/1030S-2025
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Healthy; Foot; Pressure Area; Pressure Distribution

Keywords

pedography; machine learning; AI; plantar pressure; validity; reliability

Outcome Measures

Primary Outcomes (4)

• Criterion validity of foot area measurement

  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.

  Day 1

• Criterion validity of peak plantar pressure distribution

  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.

  Day 1

• Intrasession reliability of foot area measurement

  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

  Day 1

• Intrasession reliability of mean peak pressure

  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

  Day 1

Interventions

Optical pedography DIAGNOSTIC_TEST

Comparison of 5 alternating measures taken on podoscope and pedobarographic platform

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Healthy individuals willing to participate in the study protocol

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Palacky University: lead

Study Sites (1)

Faculty of Health Sciences, Palacký University Olomouc, Czech Republic

Olomouc, Czechia, 77900, Czechia

Location

Central Study Contacts

Jonatan Dvořáček

CONTACT

+420731061089; jonatan.dvoracek01@upol.cz

Petr Konečný

CONTACT

+420604573931; petr.konecny@upol.cz

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PhD student

Study Record Dates

• First Submitted: February 11, 2026
• First Posted: March 5, 2026
• Study Start: March 1, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2027
• Last Updated: March 5, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

CZ (1)