OBJECTIVE: To describe a novel method called \'three variable optimization\' that entails a process of doing just one calculation to zero out the mean prediction error of an entire dataset (regardless of size), using only 3 variables: 1) the constant used, 2) the average intraocular lens (IOL) power and 3) the average PE.
METHODS: Development, evaluation, and testing of a method to optimize personal IOL constants.
METHODS: A dataset of 876 eyes was used as a training set, and another dataset of 1,079 eyes was used to test the method. The Barrett Universal II, Cooke K6, Haigis, RBF 3.0, Hoffer Q, Holladay 1, Holladay 2, SRK/T and T2 were analyzed. The same dataset was also divided into 3 subgroups (short, medium and long eyes). The three variable optimization process was applied to each dataset and subset, and the obtained optimized constants were then used to obtain the mean PE of each dataset. We then compared those results with those obtained by zeroing out the mean PE in the classical method.
RESULTS: The three variable optimization showed similar results to classical optimization with less data needed to optimize and no clinically significant difference. Dividing the dataset into subsets of short, medium and long eyes, also shows that the method is useful even in those situations. Finally, the method was tested in multiple formulas and it was able to reduce the PE with no clinical significant difference from classical optimization.
CONCLUSIONS: This method could then be applied by surgeons to optimize their constants by reducing the mean prediction error to zero without prior technical knowledge and it is available online for free at http://wwww.ioloptimization.com.

BACKGROUND: This study evaluates the impact of corneal power on the accuracy of 14 newer intraocular lens (IOL) calculation formulas in cataract surgery. The aim is to assess how these formulas perform across different corneal curvature ranges, thereby guiding more precise IOL selection.
METHODS: In this retrospective case series, 336 eyes from 336 patients who underwent cataract surgery were studied. The cohort was divided into three groups according to preoperative corneal power. Key metrics analyzed included mean prediction error (PE), standard deviation of PE (SD), mean absolute prediction error (MAE), median absolute error (MedAE), and the percentage of eyes with PE within ± 0.25 D, 0.50 D, ± 0.75 D, ± 1.00 D and ± 2.00 D.
RESULTS: In the flat K group (Km < 43 D), VRF-G, Emmetropia Verifying Optical Version 2.0 (EVO2.0), Kane, and Hoffer QST demonstrated lower SDs (± 0.373D, ± 0.379D, ± 0.380D, ± 0.418D, respectively) compared to the VRF formula (all P < 0.05). EVO2.0 and K6 showed significantly different SDs compared to Barrett Universal II (BUII) (all P < 0.02). In the medium K group (43 D ≤ Km < 46 D), VRF-G, BUII, Karmona, K6, EVO2.0, Kane, and Pearl-DGS recorded lower MAEs (0.307D to 0.320D) than Olsen (OLCR) and Castrop (all P < 0.03), with RBF3.0 having the second lowest MAE (0.309D), significantly lower than VRF and Olsen (OLCR) (all P < 0.05). In the steep K group (Km ≥ 46D), RBF3.0, K6, and Kane achieved significantly lower MAEs (0.279D, 0.290D, 0.291D, respectively) than Castrop (all P < 0.001).
CONCLUSIONS: The study highlights the varying accuracy of newer IOL formulas based on corneal power. VRF-G, EVO2.0, Kane, K6, and Hoffer QST are highly accurate for flat corneas, while VRF-G, RBF3.0, BUII, Karmona, K6, EVO2.0, Kane, and Pearl-DGS are recommended for medium K corneas. In steep corneas, RBF3.0, K6, and Kane show superior performance.

OBJECTIVE: To evaluate the prediction accuracy of 9 IOL power calculation formulas using a heteroscedastic statistical analysis and a novel method for IOL constant optimization.
METHODS: Retrospective case series.
METHODS: The LenStar LS900 (Haag-Streit, Koeniz, Switzerland) was used for the preoperative biometry. The predicted SE refraction of the implanted IOL were calculated for: Barrett Universal II, EVO-2.0, Hill RBF-3.0, Hill-RBF 2.0, Kane, PEARL-DGS, SRK-T, Hoffer-Q and Holladay-1. IOL constants were optimized prior to the analysis. A heteroscedastic statistical method was used to compare the standard deviation (SD) of prediction errors (PE).
RESULTS: Two hundred seventy-eight eyes of 278 patients were included. The SD of the Kane was 0.4214D and was the lowest in this database. The SD of the PE of the Kane and EVO 2.0 were significantly lower than the SRK-T, Holladay 1, and Hoffer-Q. The SD of the PE of the PEARL formula was significantly lower than the SRK-T and Hoffer-Q. The SD of the PE of the Hill-RBF 3.0 was not significantly different to the Hill-RBF 2.0, Kane, EVO 2.0, Barrett Universal II and PEARL. No significant difference was found between the SD of the PE of the new generation formulas analysed.
CONCLUSIONS: the lowest SD of the prediction error was provided by Kane, followed by EVO 2.0 and PERL-DGS formulas. However, no statistically significant differences were found between the SD of the PE of new generation formulas. Further studies are necessary to evaluate the accuracy of these formulas in extreme eyes.

In phakic patients Descemet stripping automated endothelial keratoplasty (DSAEK) or Descemet membrane endothelial keratoplasty (DMEK) are frequently combined with phacoemulsification and intraocular lens (IOL) implantation (triple procedure). This surgery might cause a refractive shift difficult to predict. Early DMEK and DSAEK results have shown a tendency toward a hyperopic shift. Myopic postoperative refraction is typically intended to correct this postoperative refractive defect and to bring all eyes as close to emmetropia as possible. We sought to understand the mechanism underlying the hyperopization and to identify predictive factors for poorer refractive outcomes, the most suitable target refraction and IOL calculation methods in patients undergoing combined cataract extraction and lamellar endothelial corneal transplantation (DSAEK or DMEK) for endothelial dysfunctions. Of the 407 articles analyzed, only 18 were included in the analysis. A myopic target between -0.50 D and -0.75 was the most common (up to -1.50 for DSAEK triple procedures), even though no optimum target was found. Hyperopic surprises appeared more frequently in corneas that were flatter in the center than in the periphery (oblate posterior profile). Among the numerous IOL calculation formulas, there was no apparent preference.

To describe the development of the post-myopic laser vision correction (LVC) version of the PEARL-DGS intraocular lens (IOL) calculation formula and to evaluate its outcomes on an independent test set.
Retrospective, single-center case series.
A modified lens position prediction algorithm was designed along with methods to predict the posterior corneal curvature radius and correct the corneal power measurement error. A different set of previously operated eyes that underwent LVC was used to evaluate the prediction precision of the post-LVC formula.
Post-LVC PEARL-DGS formula significantly reduced mean absolute error of prediction in comparison to Haigis-L, Shammas, and American Society of Cataract and Refractive Surgery (ASCRS) average formulas (P < .001). It exhibited similar postoperative refractive precision as the Barrett True-K No History formula (P = .61).
The post-LVC formula development process described in this article performed as well as the state-of-the-art post-LVC formula on the present test set. Further studies are required to assess its efficacy in other independent sets.

Myopia is the leading cause of visual impairment in the world. With ever-increasing prevalence in these years, it creates an alarming global epidemic. In addition to the difficulty in seeing distant objects, myopia also increases the risk of cataract and advances its onset, greatly affecting the productivity of myopes of working age. Cataract management in myopic eyes, especially highly myopic eyes is originally more complicated than that in normal eyes, whereas the growing population of cataract with myopia, increasing popularity of corneal and lens based refractive surgery, and rising demand for spectacle independence after cataract surgery all further pose unprecedented challenges to ophthalmologists. Previous history of corneal refractive surgery and existence of implantable collamer lens will both affect the accuracy of biometry including measurement of corneal curvature and axial length before cataract surgery, which may result in larger intraocular lens (IOL) power prediction errors and a compromise in the surgical outcome especially in a refractive cataract surgery. A prudent choice of formula for cataract patients with different characteristics is essential in improving this condition. Besides, the characteristics of myopic eyes might affect the long-term stability of IOL, which is important for the maintenance of visual outcomes especially after the implantation of premium IOLs, thus a proper selection of IOL accordingly is crucial. In this mini-review, we provide an overview of the impact of myopia epidemic on treatment for cataract and to discuss new challenges that surgeons may encounter in the foreseeable future when planning refractive cataract surgery for myopic patients.

OBJECTIVE: To analyze differences in refractive outcome Δ (difference between postoperative and expected refractive error) and in anterior segment changes between cataract surgery patients and combined phacovitrectomy patients. We also aimed to provide a corrective formula allowing to minimise the refractive outcome Δ in combined surgery patients.
METHODS: Candidates for phacoemulsification and combined phacovitrectomy (respectively PHACO and COMBINED groups) were prospectively enrolled in two specialised centres. Patients underwent best corrected visual acuity (BCVA) assessment, ultra-high speed anterior segment optical coherence tomography (OCT), gonioscopy, retinal OCT, slit lamp examination and biometry at baseline, 6 weeks postoperatively and 3 months postoperatively.
RESULTS: No differences in refractive Δ, refractive error and anterior segment parameters were noted between PHACO and COMBINED group (109 and 110 patients respectively) at 6 weeks. At 3 months, COMBINED group showed a spherical equivalent of -0.29 ± 0.10 D versus -0.03 ± 0.15 D in PHACO group (p = 0.023). COMBINED group showed a significantly higher Crystalline Lens Rise (CLR), angle-to-angle (ATA) and anterior chamber width (ACW) and a significantly lower anterior chamber depth (ACD) and refractive Δ with all 4 considered formulas at 3 months. For IOL power lower than 15, a hyperopic shift was observed instead.
CONCLUSIONS: Anterior segment OCT suggests anterior displacement of the effective lens position in patients undergoing phacovitrectomy. A corrective formula can be applied to IOL power calculation to minimize undesired refractive error.

BACKGROUND: To develop a novel machine learning-based intraocular lens (IOL) power calculation formula for highly myopic eyes.
METHODS: A total of 1828 eyes (from 1828 highly myopic patients) undergoing cataract surgery in our hospital were used as the internal dataset, and 151 eyes from 151 highly myopic patients from two other hospitals were used as external test dataset. The Zhu-Lu formula was developed based on the eXtreme Gradient Boosting and the support vector regression algorithms. Its accuracy was compared in the internal and external test datasets with the Barrett Universal II (BUII), Emmetropia Verifying Optical (EVO) 2.0, Kane, Pearl-DGS and Radial Basis Function (RBF) 3.0 formulas.
RESULTS: In the internal test dataset, the Zhu-Lu, RBF 3.0 and BUII ranked top three from low to high taking into account standard deviations (SDs) of prediction errors (PEs). The Zhu-Lu and RBF 3.0 showed significantly lower median absolute errors (MedAEs) than the other formulas (all P < 0.05). In the external test dataset, the Zhu-Lu, Kane and EVO 2.0 ranked top three from low to high considering SDs of PEs. The Zhu-Lu formula showed a comparable MedAE with BUII and EVO 2.0 but significantly lower than Kane, Pearl-DGS and RBF 3.0 (all P < 0.05). The Zhu-Lu formula ranked first regarding the percentages of eyes within ± 0.50 D of the PE in both test datasets (internal: 80.61%; external: 72.85%). In the axial length subgroup analysis, the PE of the Zhu-Lu stayed stably close to zero in all subgroups.
CONCLUSIONS: The novel IOL power calculation formula for highly myopic eyes demonstrated improved and stable predictive accuracy compared with other artificial intelligence-based formulas.

This retrospective comparative study proposes a multi-formula approach by comparing no-history IOL power calculation methods after myopic laser-refractive-surgery (LRS). One-hundred-thirty-two eyes of 132 patients who had myopic-LRS and cataract surgery were examined. ALMA, Barrett True-K (TK), Ferrara, Jin, Kim, Latkany and Shammas methods were evaluated in order to back-calculate refractive prediction error (PE). To eliminate any systematic error, constant optimization through zeroing-out the mean error (ME) was performed for each formula. Median absolute error (MedAE) and percentage of eyes within ±0.50 and ±1.00 diopters (D) of PE were analyzed. PEs were plotted with corresponding mean keratometry (K), axial length (AL), and AL/K ratio; then, different ranges were evaluated. With optimized constants through zeroing-out ME (90 eyes), ALMA was better when K ≤ 38.00 D-AL > 28.00 mm and when 38.00 D < K ≤ 40.00 D-26.50 mm < AL ≤ 29.50 mm; Barrett-TK was better when K ≤ 38.00 D-AL ≤ 26.50 mm and when K > 40.00 D-AL ≤ 28.00 mm or AL > 29.50 mm; and both ALMA and Barrett-TK were better in other ranges. (p < 0.05) Without modified constants (132 eyes), ALMA was better when K > 38.00 D-AL ≤ 29.50 mm and when 36.00 < K ≤ 38.00 D-AL ≤ 26.50 mm; Barrett-TK was better when K ≤ 36.00 D and when K ≤ 38.00 D with AL > 29.50 mm; and both ALMA and Barrett-TK were better in other ranges (p < 0.05). A multi-formula approach, according to different ranges of K and AL, could improve refractive outcomes in post-myopic-LRS eyes.

In this era of cutting-edge research and digitalization, artificial intelligence (AI) has rapidly penetrated all subspecialties, including ophthalmology. Managing AI data and analytics is cumbersome, and implementing blockchain technology has made this task less challenging. Blockchain technology is an advanced mechanism with a robust database that allows the unambiguous sharing of widespread information within a business model or network. The data is stored in blocks that are linked together in chains. Since its inception in 2008, blockchain technology has grown over the years, and its novel use in ophthalmology has been less well documented. This section on current ophthalmology discusses the novel use and future of blockchain technology for intraocular lens power calculation and refractive surgery workup, ophthalmic genetics, payment methods, international data documentation, retinal images, global myopia pandemic, virtual pharmacy, and drug compliance and treatment. The authors have also provided valuable insights into various terminologies and definitions used in blockchain technology.