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Accuracy of 7 artificial intelligence based intraocular lens power calculation formulas in extremely long Caucasian eyes

ABSTRACT

PURPOSE

: To compare 7 AI-based IOL power calculation formulas in extremely long eyes

DESIGN

: Retrospective accuracy and validity analysis.

METHODS

: SETTING: Kyiv Clinical Ophthalmology Hospital Eye Microsurgery Center, Ukraine

STUDY POPULATION

: Patients with highly myopic eyes, who underwent uneventful phacoemulsification

OBSERVATION PROCEDURES

Prior to cataract surgery IOL power was calculated. The power of the implanted IOL was randomly selected from the outcomes of SRK/T, Holladay 2 or Barrett Universal II. Three months after phacoemulsification, refraction was measured. Post-surgery IOL power calculations were performed utilizing the following formulas: Hill-RBF 3.0, Kane, PEARL-DGS, Ladas Super Formula AI (LSF AI), Hoffer QST, Karmona and Zhu-Lu.

MAIN OUTCOME MEASURES

root mean square absolute error (RMSAE), median absolute error (MedAE) and percentage of eyes with prediction error (PE) within ±0.50 D

RESULTS

: Forty eight eyes with axial length exceeding 30.00 mm were studied. Hill-RBF 3.0 yielded the lowest RMSAE (0.788) with statistical superiority only over Karmona (0.956, p=0.021). In terms of MedAE, outcomes obtained by Hoffer QST (0.442) and Hill-RBF (0.490) were statistically significant vs LSF AI (0.800, p=0.013, p=0.008, respectively). The highest percentage of eyes with PE within ±0.50 D was achieved by Hill-RBF 3.0, Kane and Hoffer QST (54.17% each) statistically significant as follows: both Hill-RBF and Kane vs LSF AI (27.08%) and Karmona (39.58%), and Hoffer QST vs LSF AI.

CONCLUSION

: All tested formulas demonstrated comparable trueness, with Hill-RBF 3.0 being more accurate than Karmona (RMSAE), and LSF AI being less accurate than Hoffer QST and Hill-RBF 3.0 (MedAE).

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Speciality

Ophthalmology Healthcare AI

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