ArticleOphthalmology and therapy2026
Predicting Postoperative Anterior Chamber Depth, Intraocular Lens Tilt, and Decentration Using an Internally Validated Machine Learning Model.
Article in Ophthalmology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06595693 (Measurement and Prediction of Tilt and Decentration of the Clareon Intraocular Lens and Evaluation of It's Effect of Visual Acuity), which is not on this map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Measurement and Prediction of Tilt and Decentration of the Clareon Intraocular Lens and Evaluation of It's Effect of Visual Acuity
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
introductionPrecise intraocular lens (IOL) positioning is critical for optimal visual outcomes in cataract surgery, particularly with advanced IOLs. Misalignment can lead to refractive errors, astigmatism, and higher-order aberrations. This study aimed to predict postoperative anterior chamber depth (ACD), IOL tilt, and decentration using machine learning.
methodsA prospective, single-center study was conducted at the Kepler University Clinic, Linz, Austria, involving 49 patients undergoing cataract surgery with implantation of a hydrophobic single-piece IOL (Clareon CNA0T0). Preoperative biometric data were collected using swept-source optical coherence tomography. Prediction models were developed using partial least squares regression to identify key predictive variables, followed by validation with a random forest model. Postoperative outcomes were assessed 8 weeks after surgery.
resultsThe mean out-of-bag (OOB) error was 0.07 mm for ACD, 1.17° for IOL tilt, and 0.02 mm for IOL decentration. Key predictive variables for ACD included preoperative ACD, axial eye length (AL), and preoperative lens tilt. IOL tilt prediction was mainly influenced by preoperative lens tilt, thickness, and decentration. For IOL decentration, preoperative lens tilt, AL, and keratometry were significant predictors.
conclusionsThe study demonstrated excellent predictability of postoperative ACD and IOL tilt and good predictability of IOL decentration using machine learning models. These findings support the clinical applicability of predictive modeling in optimizing IOL positioning for improved visual outcomes in cataract surgery.
trial registrationClinicalTrials. gov identifier, NCT06595693.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.