Orthokeratology (ortho-k) is considered both a science and an art. While corneal topography and established fitting guides provide a strong foundation, successful ortho-k fits have traditionally relied on practitioner experience, consultant guidance and iterative lens adjustments. As a result, ongoing modifications and repeated visits can impact both clinician efficiency and patient experience. Today, however, the integration of cloud-based design and ordering platforms are shifting the paradigm, bringing a more predictive and personalized approach to modern ortho-k practice.
At the core of these platforms is the ability to deliver data-driven decision support throughout the entire ortho-k fitting process. Using machine learning and predictive modeling, these systems analyze large datasets to identify patterns in corneal shape, refractive error, and treatment response that extend beyond traditional fitting nomograms.
Evidence of Accuracy, Efficiency, and Safety
Recent evidence supports this shift to algorithm-driven fitting. Artificial intelligence (AI) models trained on expert fitting data have demonstrated high accuracy in predicting ortho-k lens parameters, outperforming conventional calculations and reducing reliance on trial and error.1 Similarly, randomized controlled data comparing computer-aided fitting to traditional methods has shown improved efficiency without compromising safety or efficacy.2 In a large real-world study of more than 11,000 ortho-k fits, AI-assisted prescription systems significantly reduced the number of trial lenses required and narrowed the performance gaps between novice and experienced practitioners.3 This supports more consistent outcomes and lowers the barrier to entry for students and clinicians new to ortho-k fitting.
Beyond initial lens selection, these platforms are increasingly incorporating guided troubleshooting tools. Structured workflows help clinicians systematically address common issues such as decentration, under-correction, and fluctuating vision, reinforcing clinical decision-making and reducing variability in outcomes.
Importantly, these advancements should not be viewed solely through the lens of efficiency; they also present an opportunity to enhance safety with ortho-k. Successful outcomes depend not only on refractive and corneal metrics, but also on careful candidate selection, including ocular surface health and lid anatomy. Emerging research suggests that AI may detect subtle, localized changes in tear film quality that are not captured by traditional clinical assessments.4
Cloud-based platforms extend this safety focus into clinical workflow by prompting clinicians to evaluate these key risk factors prior to lens selection, while reinforcing structured follow-up and troubleshooting pathways. This can facilitate earlier identifications of complications such as dry eye, decentration or corneal staining. Notably, newer predictive models can proactively estimate the risk of lens decentration using pre-treatment corneal metrics, enabling earlier identification of higher-risk patients and more targeted clinical management.5 However, these tools should be viewed as adjuncts to, rather than replacements for thorough clinical evaluation. The responsibility for interpreting findings and initiating appropriate management, whether through ocular surface optimization, wear schedule adjustments, or lens modifications, remains with the clinician.
Positive Effects on Practice Workflow
From a practice perspective, these platforms also enhance efficiency and team integration. By streamlining topography uploads, lens design, ordering, and follow-up tracking within a single system, they reduce administrative burden and improve communication with laboratories. Staff can be trained to assist with data entry, order submission, and elements of patient education, allowing clinicians to focus on higher-level decision making and patient-facing care.
As demand for myopia management continues to grow, this combination of efficiency, consistency, and safety becomes increasingly important. Cloud-based platforms provide scalable infrastructure that supports practice growth while maintaining high standards of care. That said, these technologies are not without limitations. There is a learning curve, and clinicians must avoid over-reliance on automated recommendations. While algorithms enhance precision, they cannot replace clinical judgement, particularly in pediatric patients where ongoing monitoring and a cautious safety-first approach are critical.
Ultimately, intelligent cloud-based platforms are not replacing the art of ortho-k, they are refining it. By supporting more accurate initial fits, guiding troubleshooting, and reinforcing safe clinical practices, they can offer a meaningful step forward in delivering predictable, efficient, and patient-centred care.
References:
1. Koo S, et al. Development of a Machine-Learning-Based Tool for Overnight Orthokeratology Lens Fitting. Transl Vis Sci Technol. 2024;13(2):17.
2. Lan, Wei-Zhong et al. Artificial Intelligence–Assisted Prescription Determination for Orthokeratology Lens Fitting: From Algorithm to Clinical Practice. Eye & Contact Lens: Science & Clinical Practice 2024;50(7):297-304.
3. Sun Y, et al. Comparison of trial lens and computer-aided fitting in orthokeratology: a multi-center, randomized, examiner-masked, controlled study. Contact Lens and Anterior Eye. 2024 Oct 1;47(5):102172.
4. Wu, L.-Y., et al. AI-Based Estimate of the Regional Effect of Orthokeratology Lenses on Tear Film Quality. Bioengineering, 2025:12(10):1086.
5. Xiao K, et al. An integrative predictive model for orthokeratology lens decentration based on diverse metrics. Frontiers in Medicine. 2024;11:1490525.
This content is sponsored by Euclid