The Rise of Intelligent Ortho-K
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By
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ALIA CAPPELLANI, OD
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May 1, 2026
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Clinical Scorecard: The Rise of Intelligent Ortho-K
At a Glance
| Category | Detail |
|---|
| Condition | Orthokeratology (ortho-k) |
| Key Mechanisms | Integration of cloud-based design and ordering platforms using machine learning and predictive modeling. |
| Target Population | Patients requiring myopia management. |
| Care Setting | Clinical practice in optometry. |
Key Highlights
- AI models improve accuracy in predicting ortho-k lens parameters.
- Cloud-based platforms enhance efficiency and reduce trial lens usage.
- Guided troubleshooting tools address common fitting issues.
- AI can detect subtle changes in tear film quality.
- Predictive models estimate risks of lens decentration.
Guideline-Based Recommendations
Diagnosis
- Evaluate ocular surface health and lid anatomy before lens selection.
Management
- Use structured workflows for troubleshooting and follow-up.
Monitoring & Follow-up
- Proactively identify complications such as dry eye and decentration.
Risks
- Avoid over-reliance on automated recommendations; maintain clinical judgment.
Patient & Prescribing Data
Patients undergoing ortho-k fitting.
AI-assisted systems reduce the number of trial lenses needed.
Clinical Best Practices
- Incorporate cloud-based platforms for data-driven decision support.
- Train staff to assist with data entry and patient education.
- Focus on high-level decision making and patient-facing care.
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