Comparative accuracy of IOL power calculation formulas in nanophthalmic eyes undergoing cataract surgery
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.7.12Keywords:
Nanophthalmos, IOL power calculation, short axial length, cataract surgery, Accuracy of IOL PowerDimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Aim: To compare the predictive accuracy of three widely used IOL power calculation formulas—Hoffer Q, SRK/T, and SRK II—in adult patients with nanophthalmos undergoing cataract surgery or clear lens extraction. Methods: This retrospective observational study included 45 eyes with axial lengths ≤ 20.5 mm diagnosed with nanophthalmos. All patients underwent uncomplicated cataract surgery or clear lens extraction with posterior chamber IOL implantation. Preoperative biometry was performed using ZEISS IOL Master 700 or NANO AXIS A-scan. IOL power was calculated using Hoffer Q, SRK/T, and SRK II formulas. Postoperative spherical equivalent was recorded at one month, and prediction error was calculated as the difference between actual and predicted refraction. Mean absolute error (MAE) and percentage of eyes within ±0.25 D, ±0.50 D, ±1.00 D, and ±2.00 D were assessed. Statistical analysis included one-sample t-tests and descriptive statistics using SPSS version 26. Results: The Hoffer Q formula showed the lowest mean absolute prediction error (−0.44 ± 0.30 D), followed by SRK/T (+0.68 ± 0.73 D), while SRK II exhibited the highest error (+3.28 ± 0.52 D). The Hoffer Q formula demonstrated superior accuracy, with 75.6% of eyes within ±0.50 D and 93.3% within ±1.00 D of the target refraction. SRK II showed a statistically significant hyperopic shift (p < 0.001), whereas Hoffer Q and SRK/T did not show statistically significant differences from zero prediction error. Conclusion: Among the three formulas studied, the Hoffer Q formula provided the most accurate IOL power prediction in nanophthalmic eyes, with the lowest refractive error and highest consistency. These findings support the use of Hoffer Q in managing cataract patients with nanophthalmos and highlight the need for further evaluation of advanced formulas in this subgroup.Abstract
How to Cite
Downloads
Similar Articles
- Jadhav Girish Vasantrao, Chirag Patel, AT&C and non-technical loss reduction in smart grid using smart metering with AI techniques , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- V. Manibabu, M. Gomathy, Data Quality Management and Risk Assessment of Dairy Farming with Feed Behaviour Analysis Using Big Data Analytics with YOLOv5 Algorithm , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- S. Deepa, I.S. Arafat, M. Sathya Priya, S. Saravanan, An improved spectrum sharing strategy evaluation over wireless network framework to perform error free communications , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- S. TAMIL FATHIMA, K. FATHIMA BIBI, Early diagnosis of cardiac disease using Xgboost ensemble voting-based feature selection, based lightweight recurrent neural network approach , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Archana Verma, Application of metaverse technologies and artificial intelligence in smart cities , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Olivia C. Gold, Jayasimman Lawrence, Enhanced LSTM for heart disease prediction in IoT-enabled smart healthcare systems , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- R. Mercy, T. Lucia Agnes Beena, CATSEM: A Climate-Aware Time-Series Ensemble Model for Enhanced Paddy Yield Prediction , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- G. Hemamalini, V. Maniraj, Enhanced otpmization based support vector machine classification approach for the detection of knee arthritis , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Worku Masho, Habtamu Arega, Elias Bayou, Regasa Begna, The Effect of estrus synchronization with prostaglandin (PGF2α) hormone on reproductive performances of Bonga sheep ewes flushed with different local forages in Kaffa zone, Ethiopia , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- S. Munawara Banu, M. Mohamed Surputheen, M. Rajakumar, Bio-Inspired and Machine Learning-Driven Multipath Routing Protocol for MANETs Using Predictive Link Analytics , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
<< < 3 4 5 6 7 8 9 10 11 12 > >>
You may also start an advanced similarity search for this article.

