Assessment of IOP After Corneal Refractive Surgery Based on AI

Condition(s) studied

Study summary

Significance, Background, and Current Status Studies show the global average prevalence of myopia is 22%, with hyperopia incidence being similar. In China, the myopia prevalence is 31%, making it one of the countries with the highest rates of myopia. Currently, the safety and efficacy of corneal refractive surgery (CRS), such as LASIK and SMILE, for correcting myopia, hyperopia, and other refractive errors are well-established. An increasing number of patients undergo CRS to alleviate the inconveniences caused by refractive errors. While LASIK has long been regarded as a classic procedure, since the first report of Small Incision Lenticule Extraction (SMILE) for myopia correction in 2008, it has evolved into one of the mainstream surgical techniques. With the rapid advancement of refractive surgery, minimizing postoperative complications while maintaining excellent visual outcomes has become a major focus for clinicians. Postoperative intraocular pressure (IOP) monitoring is a crucial observation index.

Theoretically, IOP should not change significantly after CRS, as the surgery does not affect aqueous humor dynamics or intraocular volume. However, numerous studies indicate that alterations in corneal shape and biomechanical properties, particularly corneal thinning, lead to artificially low IOP readings with various tonometers, especially those dependent on corneal thickness. Furthermore, postoperative management often requires prolonged use of corticosteroid eye drops to suppress inflammation and promote wound healing. Extended steroid use can increase aqueous outflow resistance, elevating IOP, particularly in steroid responders, and potentially leading to steroid-induced glaucoma. Additionally, high myopia is a known risk factor for primary open-angle glaucoma. Therefore, based on preoperative and postoperative corneal parameter changes, rapidly and effectively determining the actual IOP range after CRS is of great significance for guiding clinical medication and screening for steroid-induced glaucoma.

Big Data and Artificial Intelligence (AI) are increasingly applied in medicine. AI primarily includes two technical branches: machine learning (ML) and deep learning. ML, a novel AI technology, has garnered significant interest in medical applications in recent years. It typically involves computer simulations that integrate human-like learning, refine knowledge structures, and continuously improve performance to aid diagnosis and intelligent decision-making, becoming a pivotal method in AI. Resembling neural network processes, ML systems are trained on selected input data using appropriate algorithms to produce corresponding outputs. It is now widely used to solve complex problems in engineering and science. In ophthalmology, AI/ML has gained attention for assisting in disease detection and monitoring, demonstrating advantages in fundus image diagnosis, keratoconus screening, and glaucoma classification. In corneal refractive surgery, ML has been applied to preoperative parameter design and outcome optimization, showing good safety, efficacy, and predictability. Preliminary attempts have been made to use AI decision trees to evaluate the safety and efficacy of CRS.

Building on this advanced technology and our previous research findings-which suggest that IOPcc and Pentacam-derived correction formulas (with the Shah correction method being preferable) provide relatively reliable IOP estimates after SMILE-this study aims to establish a data-driven model. Using Shah-corrected IOP as a reference to define postoperative IOP status, we will train and iteratively optimize a model by incorporating all relevant preoperative and postoperative parameters potentially affecting IOP. The goal is to predict the true IOP after CRS, thereby guiding postoperative follow-up, facilitating early detection of IOP elevation, and identifying potential glaucomatous tendencies.

Eligibility

Primary outcome measure(s)

• Accuracy of the Machine Learning Model in Predicting Postoperative Corneal-Compensated Intraocular Pressure (IOPcc) — preoperative and 3 months postoperative of corneal refractive surgery
  The primary outcome is the predictive performance of the machine learning model. Performance will be evaluated by comparing the model-predicted intraocular pressure values against the reference standard-the Pentacam-derived corneal-compensated IOP (IOPcc) corrected using the Shah formula. Key evaluation metrics include the mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²)

Trial sites (1)

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