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Predicting Postoperative Anterior Chamber Depth, Intraocular Lens Tilt, and Decentration Using an Internally

Klemens Waser^1,2,3, Klaus Straßmair^1,2, Haidar Khalil^1,2

¹Department of Ophthalmology and Optometry, Kepler University Clinic, Krankenhausstraße 9, 4020, Linz, Austria.

Ophthalmology and Therapy

|April 30, 2026

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This summary is machine-generated.

Machine learning accurately predicts intraocular lens (IOL) positioning after cataract surgery. This technology can optimize IOL placement for better visual results and fewer complications.

Area of Science:

Ophthalmology
Biomedical Engineering
Data Science

Background:

Precise intraocular lens (IOL) positioning is crucial for successful cataract surgery outcomes.
IOL misalignment can cause refractive errors, astigmatism, and higher-order aberrations.
Advanced IOLs require even more accurate placement for optimal visual acuity.

Purpose of the Study:

To develop and validate machine learning models for predicting postoperative anterior chamber depth (ACD), IOL tilt, and decentration.
To identify key preoperative biometric variables influencing IOL positioning.
To assess the clinical applicability of predictive modeling in cataract surgery.

Main Methods:

Prospective, single-center study involving 49 patients undergoing cataract surgery with a specific IOL type.

Keywords:

Clareon IOL IOL decentration IOL tilt Machine learning OCT biometry

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Preoperative biometric data collected using swept-source optical coherence tomography.

Partial least squares regression and random forest models used for prediction and validation.

Main Results:

Machine learning models achieved low prediction errors: 0.07 mm for ACD, 1.17° for IOL tilt, and 0.02 mm for IOL decentration.
Key predictors for ACD included preoperative ACD, axial length (AL), and lens tilt.
Preoperative lens tilt, thickness, decentration, AL, and keratometry were significant predictors for IOL tilt and decentration.

Conclusions:

Machine learning models demonstrate excellent predictability for postoperative ACD and IOL tilt.
Good predictability was achieved for IOL decentration, supporting clinical use.
Predictive modeling can optimize IOL positioning, enhancing visual outcomes in cataract surgery.