Risk factors for biometry prediction error by Barrett Universal II intraocular lens formula in Chinese patients

AIM:To investigate the influence of postoperative intraocular lens(IOL)positions on the accuracy of cataract surgery and examine the predictive factors of postoperative biometry prediction errors using the Barrett Universal II(BUII)IOL formula for calculation.METHODS:The prospective study included patients who had undergone cataract surgery performed by a single surgeon from June 2020 to April 2022.The collected data included the best-corrected visual acuity(BCVA),corneal curvature,preoperative and postoperative central anterior chamber depths(ACD),axial length(AXL),IOL power,and refractive error.BUII formula was used to calculate the IOL power.The mean absolute error(MAE)was calculated,and all the participants were divided into two groups accordingly.Independent t-tests were applied to compare the variables between groups.Logistic regression analysis was used to analyze the influence of age,AXL,corneal curvature,and preoperative and postoperative ACD on MAE.RESULTS:A total of 261 patients were enrolled.The 243(93.1%)and 18(6.9%)had postoperative MAE<1 and>1 D,respectively.The number of females was higher in patients with MAE>1 D(χ^(2)=3.833,P=0.039).The postoperative BCVA(logMAR)of patients with MAE>1 D was significantly worse(t=-2.448;P=0.025).After adjusting for gender in the logistic model,the risk of postoperative refractive errors was higher in patients with a shallow postoperative anterior chamber[odds ratio=0.346;95% confidence interval(CI):0.164,0.730,P=0.005].CONCLUSION:Risk factors for biometry prediction error after cataract surgery include the patient’s sex and postoperative ACD.Patients with a shallow postoperative anterior chamber are prone to have refractive errors.

Intraocular lens power calculation following laser refractive surgery

Refractive outcomes following cataract surgery in patients that have previously undergone laser refractive surgery have traditionally been underwhelming.This is related to several key issues including the preoperative assessment(keratometry)and intraocular lens power calculations.Peer-reviewed literature is overwhelmed by the influx of methodology to manipulate the corneal or intraocular lens(IOL)powers following refractive surgery.This would suggest that the optimal derivative formula has yet been introduced.This review discusses the problems facing surgeons approaching IOL calculations in these post-refractive laser patients,the existing formulae and programs to address these concerns.Prior published outcomes will be reviewed.

Machine learning adaptation of intraocular lens power calculation for a patient group

Background:To examine the effectiveness of the use of machine learning for adapting an intraocular lens(IOL)power calculation for a patient group.Methods:In this retrospective study,the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL(SN60WF,Alcon)at Miyata Eye Hospital were reviewed and analyzed.Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients,constants of the SRK/T and Haigis formulas were optimized.The SRK/T formula was adapted using a support vector regressor.Prediction errors in the use of adapted formulas as well as the SRK/T,Haigis,Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct patients.Mean prediction errors,median absolute errors,and percentages of eyes within±0.25 D,±0.50 D,and±1.00 D,and over+0.50 D of errors were compared among formulas.Results:The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas(P<0.001).In the absolute errors,the Hill-RBF and adapted methods were better than others.The performance of the Barrett Universal II was not better than the others for the patient group.There were the least eyes with hyperopic refractive errors(16.5%)in the use of the adapted formula.Conclusions:Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.

Machine learning adaptation of intraocular lens power calculation for a patient group

Background:To examine the effectiveness of the use of machine learning for adapting an intraocular lens(IOL)power calculation for a patient group.Methods:In this retrospective study,the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL(SN60WF,Alcon)at Miyata Eye Hospital were reviewed and analyzed.Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients,constants of the SRK/T and Haigis formulas were optimized.The SRK/T formula was adapted using a support vector regressor.Prediction errors in the use of adapted formulas as well as the SRK/T,Haigis,Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct patients.Mean prediction errors,median absolute errors,and percentages of eyes within±0.25 D,±0.50 D,and±1.00 D,and over+0.50 D of errors were compared among formulas.Results:The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas(P<0.001).In the absolute errors,the Hill-RBF and adapted methods were better than others.The performance of the Barrett Universal II was not better than the others for the patient group.There were the least eyes with hyperopic refractive errors(16.5%)in the use of the adapted formula.Conclusions:Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.

Comparison of a new optical biometer and a standard biometer in cataract patients

Background:Cataract surgery is the most common surgical procedure in ophthalmology.Biometry data and accurate intraocular lens(IOL)calculations are very important in achieving the desired refractive outcomes.The aim of this study was to compare measurements using a new optical low coherence reflectometry(OLCR)biometer(OA-2000)and the gold standard partial coherence interferometry(PCI)optical biometer(IOLMaster 500).Methods:Ocular biometry of cataract patients were measured by the OA-2000 and IOLMaster 500 to compare keratometry(K),axial length(AL),anterior chamber depth(ACD),white-to-white(WTW)diameter,and IOL power using the SRK/T formula.Results:One hundred and two eyes of 68 cataract patients were evaluated with the two optical biometers.The mean values of the AL,K,ACD,and WTW differed very little(OCLR biometer,23.12 mm,44.50 diopters(D),3.06,and 11.64 mm,respectively;PCI biometer,23.18 mm,44.6 D,3.15,and 11.86 mm,respectively),but the differences were significant(all,p≤0.05).The AL,K,and ACD showed excellent correlations(r=0.999,0.980,and 0.824,respectively;all p<0.001);however,there was a weak correlation of the WTW diameter between the two devices(r=0.256).The IOL powers using the SRK-T formula derived from both instruments were very similar,with an excellent correlation(r=0.989).The mean difference between the two instruments was 0.32 D.Conclusions:The OLCR biometer showed very a strong agreement with the standard PCI optical biometer for almost all ocular biometry measurements,except for the WTW diameter.Trial registration:TCTR20160614003;date 06/09/2016;‘retrospectively registered’.