Assessment of Actual Intraocular Pressure After Corneal Refractive Surgery Based on Big Data and Artificial Intelligence
Conditions: Myopia, Myopia; Astigmatism, Refractive Surgery, Intraocular Pressure Changes
Sponsor: Tianjin Eye Hospital

 
 
Study Protocol 
Assessment of Actual Intraocular Pressure after Corneal Refractive 
Surgery Based on Big Data and Artificial Intelligence  
ClinicalTrails.gov ID: 
Date:10 January 2025 
 
 
 
 
 
 
 
 
 
 

Assessment of Actual Intraocular Pressure after Corneal Refractive 
Surgery Based on Big Data and Artificial Intelligence  
1. 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.  
2. Research Plan (Objectives, Content, Methodology, Key Challenges, and 
Solutions) 
2.1 Study 1: IOP Changes and Value Prediction after FS-LASIK and SMILE 
A total of 350 patients undergoing SMILE and 350 undergoing FS-LASIK between 
October 1, 2023, and January 2, 2025, will be enrolled. Parameters including spherical 
equivalent (SE), central corneal thickness (CCT), mean keratometry (Km), residual 
stromal bed thickness (RST), percent tissue altered (PTA), and IOP values 
preoperatively and at 1 week, 1 month, and 2 months postoperatively will be recorded. 
These parameters will be included in a multiple linear regression model to analyze 
factors influencing postoperative IOP and derive a preliminary estimation formula for 
postoperative IOP measurements. Differences in IOP changes between the two surgical 
groups will be compared, and other influencing factors will be explored.  
2.2 Study 2: Assessment of IOP Measurement Levels after CRS Based on Machine 
Learning 
Data from approximately 10,300 SMILE and 11,000 FS-LASIK patients treated 
between October 1, 2020, and April 1, 2025, will be collected. Parameters affecting 
IOP, such as SE, CCT, Km, RST, and PTA, will be included to explore a predictive ML 
model for post-CRS IOP. The information gain algorithm will quantify the influence of 
various factors on postoperative IOP. Follow-up IOP measurements (preoperative, 1 
week, 1 month, 2 months) and Pentacam Shah-corrected IOP will be recorded. The 
estimation formula from Study 1 will also be used to calculate postoperative IOP. 
Several different ML models will be constructed based on features with higher weights 
to build predictive models for judging IOP levels at follow-up timepoints. Clinical 
validation will be performed.  
A cross-validation method will be employed to verify the model's effectiveness. 
Internal validation will first be conducted using the training dataset. The entire 
modeling dataset will be randomly divided into 10 equal folds. Nine folds will be used 

for model training, and the remaining fold will serve as the test set. The trained model 
will predict outcomes on the test set, and prediction errors will be calculated (e.g., sum 
of squared errors). This process will be repeated 10 times, each time with a different 
fold as the test set. The average performance across all 10 iterations will be reported.  
3. Technical Roadmap  
Detailed Protocol: 3.1 Inclusion Criteria  
1. Normal preoperative IOP, no glaucoma or suspected glaucoma.  
2. Corneal thickness ≥ 480 μm.  
3. Discontinuation of rigid gas permeable contact lenses for ≥3 months and soft  
contact lenses for ≥2 weeks prior to examination.  
4. Clear cornea, no corneal leukoma, no history of ocular trauma.  
5. No previous ocular surgery.  
6. Willingness to participate and comply with all study examinations and  
procedures.  
3.2 Exclusion Criteria  
1. Severe psychiatric disorders.  
2. Ocular hypertension, suspected glaucoma, or glaucoma.  
3. Concurrent other ocular diseases (e.g., corneal opacity, uveitis).  
4. History of ocular surgery, trauma, or contact lens wear (as per inclusion 
criterion #3).  
5. Nystagmus or inability to cooperate with examinations.  
6. Presence of other ocular surface diseases, dry eye syndrome, fundus diseases,  
or systemic diseases affecting the study.  
3.3 Criteria for Withdrawal, Dropout, or Study Termination  
1. Inability to complete the study protocol after enrollment.  
2. Poor quality of research-related ophthalmic examinations.  
3.4 Data Collection and Other Measures 1. Data Collection:  
1. Demographic data: Name, gender, age. 
2. Medical history: Documented systemic diseases. 
3. Comprehensive ophthalmic examinations: Visual acuity, refraction, IOP, corneal 
topography (Pentacam), slit-lamp examination, ocular biometry, axial length, lens 
status, intraoperative parameters, postoperative IOP, and postoperative refraction.  
4. Ensuring Patient Compliance: The study details will be thoroughly explained to 
patients and their families. Informed consent will be obtained. Good communication 
and patient care will be maintained throughout the study to enhance trust and 
compliance.  

3.5 Study Endpoints  
Primary endpoints include preoperative and postoperative computerized IOP 
measurements.  
3.6 Statistical Analysis  
All analyses will be performed using SPSS version 29.0 (SPSS Inc.). The normality of 
data distribution will be assessed using the Kolmogorov-Smirnov test. Normally 
distributed data will be presented as mean ± standard deviation (x ± s), and Pearson 
correlation analysis will be used for continuous variables. Non-normally distributed 
data will be presented as median (Q1, Q3), and Spearman correlation analysis will be 
employed. A P-value < 0.05 will be considered statistically significant.  
3.7 Key Challenges and Proposed Solutions 
1. Study 1: The potential confounding effects of different refractive errors and corneal 
thickness will be addressed. Participants will be stratified into three groups based on 
SE: < -3.00 D, -3.00 D to -6.00 D, and > -6.00 D. Inter-group differences will be 
analyzed to control for their impact on the final results.  
2. Study 2: Using the formula from Study 1 and the Shah-corrected IOP to define IOP 
status may still contain judgment errors, leading to predictive bias in the ML model. 
This will be mitigated by validating against historical data with confirmed 
postoperative ocular hypertension.  
3. Overall Goal: Establishing a deep learning-based model to predict post- refractive 
surgery IOP and accurately judge its level aims to resolve clinicians' difficulties in 
interpreting IOP readings after surgery.  
4. References  
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