Diagnostic Evaluation of Keratoconus Using Corneal Topography and Machine Learning.

Diagnostic Evaluation of Keratoconus Using Corneal Topography and Machine Learning. - Diagnostic Evaluation of Keratoconus Using Machine Learning.

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000040128

Enrollment

250

Registered

2020-04-10

Start date

2019-01-01

Completion date

Unknown

Last updated

2026-06-29

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

Conditions

Keratoconus

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: evident findings characteristic of keratoconus (e.g., corneal topography with asymmetric bow-tie pattern with or without skewed axes), and at least one keratoconus sign (e.g., stromal thinning, conical protrusion of the cornea at the apex, Fleischer ring, Vogt striae, or anterior stromal scar) on slit-lamp examination by corneal specialists.

Exclusion criteria

Exclusion criteria: Other corneal diseases or history of corneal surgery.

Design outcomes

Primary

Measure	Time frame
Accuracy	—

Countries

Japan

Contacts

Public ContactKazutaka Kamiya

Kitasato University School of Allied Health Sciences

kamiyak-tky@umin.ac.jp0427788464

Outcome results