AIM: To compare the Barrett True-K formula with other formulas integrated in Lenstar 900 to predict intraocular lens(IOL) power after small-incision lenticule extraction(SMILE).METHODS: A theoretical prospective study...AIM: To compare the Barrett True-K formula with other formulas integrated in Lenstar 900 to predict intraocular lens(IOL) power after small-incision lenticule extraction(SMILE).METHODS: A theoretical prospective study was performed to predict the ratio of equivalent IOL power before and after SMILE using the SRK/T(Sanders, Retzlaff, Kraff/theoretical), Holladay 1, Haigis, and Barrett True-K formulas and compare the stability of their predictions. The study included 54 eyes(54 cases) with a manifest refraction spherical equivalent(MRSE) of-4.99±1.45 D. They were divided into two groups: 27 eyes with axial length of 24-26 mm in Group A, and 27 eyes with axial length >26 mm in Group B. All subjects enrolled in this study were examined with the Lenstar 900 before and 6 mo after SMILE including measurements of axial length, corneal curvature, and anterior chamber depth(ACD). RESULTS: The prediction of equivalent IOL power of the two groups was more stable for the Barrett True-K formula, especially in long axial length eyes(Group B). The respective percentages for the SRK/T, Holladay 1, Haigis, and Barrett True-K formulas were 7.4%, 7.4%, 85.19%, and 88.89% for a margin of error within 0.5 D;25.92%, 51.84%, 100%, and 100% for a margin of error within 1.0 D in Group A;33.33%, 40.74%, 44.44%, and 81.48% for a margin of error within 0.5 D;and 44.44%, 59.26%, 66.66%, and 92.59% for a margin of error within 1.0 D in Group B. The respective percentages for Barrett True-K formulas were 100% for a margin of error within 2.0 D in Group B.CONCLUSION: Theoretically, the Barrett True-K formula provides more stable predictions than other formulas for cataract eyes after SMILE.展开更多
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 ...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.展开更多
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 ...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.展开更多
This review summarises the current evidence base and provides guidelines for obtaining good refractive outcomes following cataract surgery. Important background information is also provided. In summary, the requiremen...This review summarises the current evidence base and provides guidelines for obtaining good refractive outcomes following cataract surgery. Important background information is also provided. In summary, the requirements are:(1) standardisation of biometry equipment used for axial length and keratometry measurement and the use of optical or immersion ultrasound biometry;(2) sutureless cataract surgery with "in the bag" intraocular lens(IOL) placement;(3) an appropriate 3rd, 4th or 5th Generation IOL power formula should be used;(4) IOL formula constants must be optimized;(5) under certain conditions, the refractive outcome of the 2nd eye can be improved based on the refractive error of the first eye; and(6) results should be audited for refinement and to ensure that standards are met.展开更多
文摘AIM: To compare the Barrett True-K formula with other formulas integrated in Lenstar 900 to predict intraocular lens(IOL) power after small-incision lenticule extraction(SMILE).METHODS: A theoretical prospective study was performed to predict the ratio of equivalent IOL power before and after SMILE using the SRK/T(Sanders, Retzlaff, Kraff/theoretical), Holladay 1, Haigis, and Barrett True-K formulas and compare the stability of their predictions. The study included 54 eyes(54 cases) with a manifest refraction spherical equivalent(MRSE) of-4.99±1.45 D. They were divided into two groups: 27 eyes with axial length of 24-26 mm in Group A, and 27 eyes with axial length >26 mm in Group B. All subjects enrolled in this study were examined with the Lenstar 900 before and 6 mo after SMILE including measurements of axial length, corneal curvature, and anterior chamber depth(ACD). RESULTS: The prediction of equivalent IOL power of the two groups was more stable for the Barrett True-K formula, especially in long axial length eyes(Group B). The respective percentages for the SRK/T, Holladay 1, Haigis, and Barrett True-K formulas were 7.4%, 7.4%, 85.19%, and 88.89% for a margin of error within 0.5 D;25.92%, 51.84%, 100%, and 100% for a margin of error within 1.0 D in Group A;33.33%, 40.74%, 44.44%, and 81.48% for a margin of error within 0.5 D;and 44.44%, 59.26%, 66.66%, and 92.59% for a margin of error within 1.0 D in Group B. The respective percentages for Barrett True-K formulas were 100% for a margin of error within 2.0 D in Group B.CONCLUSION: Theoretically, the Barrett True-K formula provides more stable predictions than other formulas for cataract eyes after SMILE.
文摘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.
文摘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.
文摘This review summarises the current evidence base and provides guidelines for obtaining good refractive outcomes following cataract surgery. Important background information is also provided. In summary, the requirements are:(1) standardisation of biometry equipment used for axial length and keratometry measurement and the use of optical or immersion ultrasound biometry;(2) sutureless cataract surgery with "in the bag" intraocular lens(IOL) placement;(3) an appropriate 3rd, 4th or 5th Generation IOL power formula should be used;(4) IOL formula constants must be optimized;(5) under certain conditions, the refractive outcome of the 2nd eye can be improved based on the refractive error of the first eye; and(6) results should be audited for refinement and to ensure that standards are met.
