Machine learning based causality analysis of fingernail patterns with mineral content and biographical data

Machine learning based causality analysis of fingernail patterns with mineral content and biographical data - NutriNAIL

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00034566

Enrollment

300

Registered

2024-08-13

Start date

2024-06-25

Completion date

Unknown

Last updated

2025-10-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Explorative design, therefore open-ended with regard to the correlation between the mineral composition of fingernails and possible diseases

Interventions

Group 1: Test persons fill out a lifestyle survey, provide fingernail samples and fingernail surfaces are photographed.

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: none

Exclusion criteria

Exclusion criteria: Polished nails, artificial nails

Design outcomes

Primary

Measure	Time frame
No endpoints. The study combines AI technology and nutritional science to develop a comprehensive system for researching the relationship between nutrition, health and the characteristics of fingernails. On the one hand, a method will be developed to collect high-quality images of fingernails that will serve as the basis for an AI-based recognition system. On the other hand, a questionnaire will be designed to anonymously collect detailed data on participants' dietary habits, health conditions and demographic information. This information will be used to identify possible correlations between fingernail patterns and various nutritional and health parameters. Despite the methodological groundwork and the planned structured approach, the outcome of the research remains open-ended, particularly with regard to the correlations between the fingernail image data and health parameters.	—

Measure

Time frame

No endpoints. The study combines AI technology and nutritional science to develop a comprehensive system for researching the relationship between nutrition, health and the characteristics of fingernails. On the one hand, a method will be developed to collect high-quality images of fingernails that will serve as the basis for an AI-based recognition system. On the other hand, a questionnaire will be designed to anonymously collect detailed data on participants' dietary habits, health conditions and demographic information. This information will be used to identify possible correlations between fingernail patterns and various nutritional and health parameters. Despite the methodological groundwork and the planned structured approach, the outcome of the research remains open-ended, particularly with regard to the correlations between the fingernail image data and health parameters.

—

Countries

Germany

Contacts

Public ContactMarc Birringer

Hochschule Fulda

marc.birringer@oe.hs-fulda.de+49-0661 9640-385

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026