Differences Between Simulated Keratometry and Total Corneal Power in Eyes With Keratoconus and a Formula to Improve IOL Power Calculation Results.

PURPOSE: To compare simulated keratometry (SimK) and total corneal power (TCP) in keratoconic eyes, to determine whether the differences are systematic and predictable and to evaluate an adjusted TCP-based formula for intraocular lens (IOL) power calculation. METHODS: In a consecutive series of keratoconic eyes, measurements of SimK, TCP, posterior keratometry, and anterior and posterior corneal asphericities (Q-values) were retrospectively collected. The difference between SimK and TCP was linearly correlated to the biometric parameters. In a separate sample of keratoconic eyes that had undergone cataract surgery, IOL power was calculated with the Barrett Universal II, Hoffer QST, Holladay 1, Kane, and SRK/T formulas using the SimK and an adjusted TCP power. The respective prediction errors were calculated. RESULTS: A total of 382 keratoconic eyes (271 patients) were enrolled. An increasing overestimation of SimK by TCP was detected from stage I to III, with a significant correlation between the SimK and TCP difference and SimK in the whole sample (P < .0001, r2 = 0.1322). Approximately 7% of cases presented an underestimation of SimK by TCP. IOL power calculation with the adjusted TCP improved outcomes, achieving a maximum of 80% of eyes with a prediction error within ±0.50 diopters with the Hoffer QST, Holladay 1, and Kane formulas. CONCLUSIONS: Overall, SimK overestimated TCP. Such a difference could not be predicted by any variable. The proposed TCP-adjustment formula (TCPadj = TCP + 0.56 diopters) in keratoconic eyes for IOL power calculation might be valuable for improving refractive outcomes. [J Refract Surg. 2024;40(4):e253-e259.].

Vector analysis of corneal astigmatism in cataractous eyes based on IOLMaster 700 biometry.

PURPOSE: The purpose of this study was to investigate the effect of the corneal back surface by comparing the keratometric astigmatism (K, derived from the corneal front surface) of a modern optical biometer against astigmatism of Total Keratometry (TK, derived from both corneal surfaces) in a large population with cataractous eyes. The results were then used to define linear prediction models to map K to TK. METHODS: From a large dataset containing bilateral biometric measurements (IOLMaster 700) in 9736 patients prior to cataract surgery, the total corneal astigmatism was decomposed into vectors for K, corneal back surface (BS), and TK. A multivariate prediction model (MV), simplified model with separation of vector components (SM) and a constant model (CM) were defined to map K to TK vector components. RESULTS: The K centroid (X/Y) showed some astigmatism with-the-rule (0.1981/-0.0211 dioptre (dpt)) whereas the TK centroid was located around zero (-0.0071/-0.0381 dpt against-the-rule) and the BS centroid showed systematic astigmatism against-the-rule (-0.2367/-0.0145 dpt). The respective TK-K centroid was located at -0.2052/-0.0302 dpt. The MV model showed the same performance (i.e. mean absolute residuum) as the SM did (0.1098 and 0.1099 dpt respectively) while the CM performed only slightly worse (0.1121 dpt mean absolute residuum). CONCLUSION: In cases where tomographic data are unavailable statistical models could be used to consider the overall contribution of the back surface to the total corneal astigmatism. Since the performance of the CM is sufficiently close to that of MV and SM we recommend using the CM which can be directly considered e.g. as surgically induced astigmatism.

Interchangeability in Automated Corneal Diameter Measurements Across Different Biometric Devices: A Systematic Review of Agreement Studies.

PURPOSE: To provide an up-to-date review of the agreement in automated white-to-white (WTW) measurement between the latest topographic and biometric devices. METHODS: In this systematic review, PubMed, Web of Science, and Scopus databases were searched for articles published between 2017 and 2023, focusing on WTW agreement studies on adult, virgin eyes, with or without cataract and no other ocular comorbidities. Studies evaluating WTW measurements performed with autokeratometers, manual calipers, or manual image analysis were excluded. When available, the following metrics for the agreement of WTW measurements between pairs of devices were included: mean difference ± standard deviation, 95% limits of agreement (LoA), LoA width, 95% confidence interval (95 CI%), and intraclass correlation coefficient (ICC). RESULTS: Forty-one studies, covering comparisons for 19 devices, were included. Altogether, 81 paired comparisons were performed for 4,595 eyes of 4,002 individuals. The mean difference in WTW measurements between devices ranged from 0.01 mm up to 0.96 mm, with varying CI. The 95% LoA width ranged from 0.31 to 2.45 mm (median: 0.65 mm). The majority of pairwise comparisons reported LoA wider than 0.5 mm, a clinically significant value for phakic intraocular lens sizing. CONCLUSIONS: Nearly all analyzed studies demonstrated the lack of interchangeability of the WTW parameter. The corneal diameter, assessed by means of grayscale en-face image analysis, tended to demonstrate the lowest agreement among devices compared to other measured biometric parameters. [J Refract Surg. 2024;40(3):e182-e194.].

Modified intraocular lens power selection method according to biometric subgroups Eom IOL power calculator.

This study evaluates the accuracy of a newly developed intraocular lens (IOL) power calculation method that applies four different IOL power calculation formulas according to 768 biometric subgroups based on keratometry, anterior chamber depth, and axial length. This retrospective cross-sectional study was conducted in at Korea University Ansan Hospital. A total of 1600 eyes from 1600 patients who underwent phacoemulsification and a ZCB00 IOL in-the-bag implantation were divided into two datasets: a reference dataset (1200 eyes) and a validation dataset (400 eyes). Using the reference dataset and the results of previous studies, the Eom IOL power calculator was developed using 768 biometric subgroups. The median absolute errors (MedAEs) and IOL Formula Performance Indexes (FPIs) of the Barrett Universal II, Haigis, Hoffer Q, Holladay 1, Ladas Super, SRK/T, and Eom formulas using the 400-eye validation dataset were compared. The MedAE of the Eom formula (0.22 D) was significantly smaller than that of the other four formulas, except for the Barrett Universal II and Ladas Super formulas (0.24 D and 0.23 D, respectively). The IOL FPI of the Eom formula was 0.553, which ranked first, followed by the Ladas Super (0.474), Barrett Universal II (0.470), Holladay 1 (0.444), Hoffer Q (0.396), Haigis (0.392), and SRK/T (0.361) formulas. In conclusion, the Eom IOL power calculator developed in this study demonstrated similar or slightly better accuracy than the Barrett Universal II and Ladas Super formulas and was superior to the four traditional IOL power calculation formulas.

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments.

In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

Predictive accuracy of Barrett TK toric calculator compared to measured posterior corneal astigmatism using Pentacam in toric IOL power calculation.

PURPOSE: To compare the predictive accuracy of Barrett total keratometry (Barrett TK) toric calculator with the measured posterior corneal astigmatism (PCA) by using Pentacam in toric intraocular lens (IOL) power calculation. METHODS: A prospective analysis was done on 118 eyes requiring toric IOL power implantation. The absolute prediction error of the Barrett TK toric calculator and the measured PCA in the online toric calculator were assessed and compared to the standard Alcon toric calculator (with Barrett toric calculator incorporated). RESULTS: The mean absolute prediction error of the online toric calculator (0.32 D) (with Barrett toric calculator incorporated), Barrett TK Toric (0.34 D), and measured PCA of Pentacam in Barrett toric calculator (0.33 D) were found to be similar with no statistically significant difference. Subanalysis in eyes with with-the-rule astigmatism, against-the-rule astigmatism, and oblique astigmatism showed similar results. Alpins analysis showed that all three methods overcorrected corneal astigmatism. CONCLUSION: The Barrett TK toric calculator and the measured PCA of Pentacam in the Barrett toric calculator have similar predictive accuracy to the online toric calculator (with Barrett toric calculator incorporated).

Association of refractive outcome with postoperative anterior chamber depth measured with 3 optical biometers.

PURPOSE: This study evaluated the relationship between refractive outcomes and postoperative anterior chamber depth (ACD, measured from corneal epithelium to lens) measured by swept-source optical coherence tomography (SS-OCT), optical low-coherence reflectometry (OLCR), and Scheimpflug devices under the undilated pupil. METHODS: Patients undergoing cataract phacoemulsification with intraocular lens (IOL) implantation in a hospital setting were enrolled. Postoperative ACD (postACD) was performed with an SS-OCT device, an OLCR device, and a Scheimpflug device at least 1 month after cataract surgery. After adjusting the mean predicted error to 0, differences in refractive outcomes were calculated with the Olsen formula using actual postACD measured from 3 devices and predicted value. RESULTS: Overall, this comparative case study included 69 eyes of 69 patients, and postACD measurements were successfully taken using all 3 devices. The postACD measured with the SS-OCT, OLCR, and Scheimpflug devices was 4.59 ± 0.30, 4.50 ± 0.30, and 4.54 ± 0.32 mm, respectively. Statistically significant differences in postACD were found among 3 devices (P < 0.001), with intraclass correlation coefficients (ICCs) and Bland-Altman showing good agreement. No significant difference in median absolute error was found with the Olsen formula using actual postACD obtained with 3 devices. Percentage prediction errors were within ± 0.50 D in 65% (OLCR), 70% (Scheimpflug), and 67% (SS-OCT) calculated by actual postACD versus 64% by predicted value. CONCLUSION: Substantial agreement was found in postACD measurements obtained from the SS-OCT, OLCR, and Scheimpflug devices, with a trend toward comparable refractive outcomes in the Olsen formula. Meanwhile, postACD measurements may be potentially superior for the additional enhancement of refractive outcomes.

The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review.

BACKGROUND: Objective measures of screen time are necessary to better understand the complex relationship between screen time and health outcomes. However, current objective measures of screen time (e.g., passive sensing applications) are limited in identifying the user of the mobile device, a critical limitation in children's screen time research where devices are often shared across a family. Behavioral biometrics, a technology that uses embedded sensors on modern mobile devices to continuously authenticate users, could be used to address this limitation. OBJECTIVE: The purpose of this scoping review was to summarize the current state of behavioral biometric authentication and synthesize these findings within the scope of applying behavioral biometric technology to screen time measurement. METHODS: We systematically searched five databases (Web of Science Core Collection, Inspec in Engineering Village, Applied Science & Technology Source, IEEE Xplore, PubMed), with the last search in September of 2022. Eligible studies were on the authentication of the user or the detection of demographic characteristics (age, gender) using built-in sensors on mobile devices (e.g., smartphone, tablet). Studies were required to use the following methods for authentication: motion behavior, touch, keystroke dynamics, and/or behavior profiling. We extracted study characteristics (sample size, age, gender), data collection methods, data stream, model evaluation metrics, and performance of models, and additionally performed a study quality assessment. Summary characteristics were tabulated and compiled in Excel. We synthesized the extracted information using a narrative approach. RESULTS: Of the 14,179 articles screened, 122 were included in this scoping review. Of the 122 included studies, the most highly used biometric methods were touch gestures (n = 76) and movement (n = 63), with 30 studies using keystroke dynamics and 6 studies using behavior profiling. Of the studies that reported age (47), most were performed exclusively in adult populations (n = 34). The overall study quality was low, with an average score of 5.5/14. CONCLUSION: The field of behavioral biometrics is limited by the low overall quality of studies. Behavioral biometric technology has the potential to be used in a public health context to address the limitations of current measures of screen time; however, more rigorous research must be performed in child populations first. SYSTEMATIC REVIEW REGISTRATION: The protocol has been pre-registered in the Open Science Framework database ( https://doi.org/10.17605/OSF.IO/92YCT ).

Central anterior chamber depth correlated with white-to-white distance in normal, long, and short eyes.

PURPOSE: To explore the associations between central anterior chamber depth (CACD) and other anterior segment biometric parameters and to determine the possible determinants of CACD in short, normal, and long eyes. METHODS: The biometric data of pre-operation patients aged 50-80 years with coexisting cataract and primary angle-closure disease or senile cataract were reviewed. Axial length (AL), CACD, lens thickness (LT), central corneal thickness (CCT), and white-to-white distance (WTW) were measured by Lenstar optical biometry (Lenstar 900). The data of 100 normal eyes (AL = 22 to 26 mm), 100 short eyes (AL ≤ 22 mm), and 100 long eyes (AL ≥ 26 mm) were consecutively collected for subsequent analyses. RESULTS: The mean age of the subjects was 66.60 ± 7.85 years, with 25.7% of the sample being men. Both CACD and WTW were found to be smallest in short eyes and were smaller in normal eyes than in long eyes (F = 126.524, P < 0.001; F = 28.458, P < 0.001). The mean LT was significantly thicker in short eyes than in normal and long eyes (4.66 mm versus 4.49 mm versus 4.40 mm; F = 18.099, P < 0.001). No significant differences were observed in CCT between the three AL groups (F = 2.135, P = 0.120). Stepwise regression analysis highlighted AL, LT, and WTW as three independent factors associated with CACD in the normal AL group. In the short AL group and long AL group, LT and WTW were independent factors associated with CACD. CONCLUSIONS: CACD increases as AL elongates and reaches a peak when AL exceeds 26 mm. Furthermore, CACD showed inverse correlation with LT and positive correlation with WTW. A relatively small WTW results in an anteriorly positioned lens, and thus, a decrease in CACD.

Fingerprint Recognition in Forensic Scenarios.

Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.

Comparison of Pre- and Post-DMEK Keratometry and Total Keratometry Values for IOL Power Calculations in Eyes Undergoing Triple DMEK.

PURPOSE: To evaluate prediction accuracy of pre- and post-DMEK keratometry (K) and total keratometry (TK) values for IOL power calculations in Fuchs endothelial corneal dystrophy (FECD) eyes undergoing DMEK with cataract surgery (triple DMEK). METHODS: Retrospective cross-sectional multicenter study of 55 FECD eyes (44 patients) that underwent triple DMEK between 2019 and 2022 between two centers in USA and Europe. Swept-source optical coherence tomography biometry (IOLMaster 700) was used for pre- and post-DMEK measurements. K and TK values were used for power calculations with ten formulae (Barrett Universal II (BUII), Castrop, Cooke K6, EVO 2.0, Haigis, Hoffer Q, Hoffer QST, Holladay I, Kane, and SRK/T). Mean error, mean absolute error (MAE), standard deviation, and percentage of eyes within ±0.50/±1.00 diopters (D) were calculated. Studied formulae were additionally adjusted using a method published previously (IOLup1D Method), which increases the IOL power by 1D. While both eyes from the same patient were considered for descriptive statistics, we restricted to one eye per individual (44 eyes for statistical comparisons. RESULTS: MAEs for all formulae were lower for post-DMEK K and TK than pre-DMEK K and TK by an average of 0.24 and 0.47 D, respectively. The lowest MAE was 0.49 D for Kane using post-DMEK TK, and the highest MAE was 1.05 D for BUII using pre-DMEK TK. Most IOLup1D formulae had lower MAEs than pre-DMEK K and TK formulae. CONCLUSIONS: The IOLup1D Method should be used instead of pre-DMEK K and TK values for triple DMEK in FECD eyes. Using post-DMEK TK values for cataract surgery after DMEK provides better refractive accuracy than any of the three studied methods used for triple DMEK procedures.

Refractive Outcomes With New Generation Formulas for Intraocular Lens Power Calculation in Radial Keratotomy Patients.

PURPOSE: Radial keratotomies (RKs) are responsible for corneal irregularities resulting in biometric errors and lower best-corrected visual acuity (BCVA) due to lower-order and higher-order optical aberrations. The aim of the study was to compare performances of new and old generation formulas in a population of RK patients. METHODS: RK patients who underwent phacoemulsification with intraocular lens (IOL) implantation were retrospectively recruited. Inclusion criteria were availability of preoperative and 6-month postoperative BCVA assessment, topography, and tomography. Documented refraction instability, corneal ectasia, and previous ocular surgery except for RK were exclusion criteria. Mean prediction error (ME), mean absolute prediction error (MAE), and incidence of MAE > 0.25D were calculated for SRK-T, Barrett True K, EVO 2.0, Kane, and PEARL-DGS. RESULTS: Twenty-seven patients with a mean baseline BCVA of 0.32 ± 0.18 logMAR and a mean corneal root mean square (RMS) value of 1.59 ± 0.91 µm were included. EVO 2.0, Kane, and PEARL-DGS showed a significantly lower MAE and lower ME compared with all other formulas ( P < 0.001 and P < 0.001) and a significant lower incidence of MAE >0.25D ( P < 0.001). Significant differences were still detected when using 3-mm mean keratometry for IOL calculation. CONCLUSIONS: PEARL-DGS, Kane, and EVO 2.0 formulas show superior accuracy in IOL power calculation compared with SRK-T and Barrett True K in RK patients, with no significant differences between the 3.

The predictive accuracy of Barrett toric calculator using measured posterior corneal astigmatism derived from swept source-OCT and Scheimpflug camera.

PURPOSE: To compare the performance of Barrett toric calculator incorporated with measured posterior corneal astigmatism (PCA) derived from IOL Master 700 and Pentacam HR versus predicted PCA. METHODS: The predicted residual astigmatism using Barrett toric IOL calculator with predicted PCA, measured PCA from IOL Master 700 and measured PCA from Pentacam were calculated with the preoperative keratometry and intended IOL axis with modification. The vector analysis was performed to calculate the mean absolute prediction error (MAE), the centroid of the prediction error and the percentage of eyes with a prediction error within ±0.50 D, ±0.75 D, and ±1.00 D. RESULTS: In 57 eyes of 57 patients with mean age of 70.42 ± 10.75 years, the MAE among the three calculation methods were 0.59 ± 0.38 D (Predicted PCA), 0.60 ± 0.38 D (Measured PCA from IOL Master 700) and 0.60 ± 0.36 D (Measured PCA from Pentacam) with no significant difference, either in the whole sample, the WTR eyes and the ATR eyes (F = 0.078, 0.306 and 0.083, p = 0.925, 0.739 and 0.920, respectively). Measured PCA obtained from IOL Master 700 resulted in one level reduction (from Tn to Tn-1) in 49.12% eyes in cylindrical model selection, while measured PCA obtained from Pentacam resulted in one level reduction of toric model selection in 18.18% eyes. CONCLUSION: The present study suggested that the incorporation of measured PCA values derived from IOL Master 700 and Pentacam produce comparable clinical outcome with the predicted PCA mode in Barrett toric calculator.

Accuracy of the PEARL-DGS Formula for Intraocular Lens Power Calculation in Post-Myopic Laser Refractive Corneal Surgery Eyes.

PURPOSE: To investigate the accuracy of the PEARL-DGS formula for intraocular lens (IOL) power calculation in post-myopic laser refractive corneal surgery eyes. DESIGN: Retrospective case series. METHODS: A total of 139 eyes of 139 patients (mean axial length: 27.4 ± 2.1 mm) who had prior myopic laser refractive corneal surgery and subsequent cataract surgery using Tecnis ZCB00 from March 2018 to February 2023 were included. Refractive outcomes of 5 formulas (Barrett True K, Haigis-L, Hoffer-QST, PEARL-DGS, and Shammas-PL) were evaluated. Prediction error was defined as the difference between the measured and predicted postoperative refractive spherical equivalent using the IOL power actually implanted. Mean prediction error (MPE), median absolute prediction error (MedAE), and mean absolute prediction error were calculated. RESULTS: Without constant optimization, the PEARL-DGS resulted in a MPE of +0.05 ± 0.65 diopters (D), whereas the other formulas resulted in myopic shifts. The MedAEs of the formulas were 0.39, 0.53, 0.65, 0.85, and 1.11 D for the PEARL-DGS, Hoffer-QST, Barrett True K, Shammas-PL, and Haigis-L, respectively, in order of magnitude (P < .05). With constant optimization, there were no statistically significant differences in the MedAEs among the 5 formulas (P = .388). CONCLUSIONS: In comparison to other IOL formulas, the PEARL-DGS resulted in better refractive outcomes after cataract surgery in post-myopic laser refractive corneal surgery eyes without constant optimization. We suggest that PEARL-DGS be considered as the first choice for IOL power calculation in these eyes when the clinicians do not have their optimized constants.

A comparative analysis of multi-biometrics performance in human and action recognition using silhouette thermal-face and skeletal data.

Biometrics is a field that has been given importance in recent years and has been extensively studied. Biometrics can use physical and behavioural differences that are unique to individuals to recognize and identify them. Today, biometric information is used in many areas such as computer vision systems, entrance systems, security and recognition. In this study, a new biometrics database containing silhouette, thermal face and skeletal data based on the distance between the joints was created to be used in behavioural and physical biometrics studies. The fact that many cameras were used in previous studies increases both the processing intensity and the material cost. This study aimed to both increase the recognition performance and reduce material costs by adding thermal face data in addition to soft and behavioural biometrics with the optimum camera. The presented data set was created in accordance with both motion recognition and person identification. Various data loss scenarios and multi-biometrics approaches based on data fusion have been tried on the created data sets and the results have been given comparatively. In addition, the correlation coefficient of the motion frames method to obtain energy images from silhouette data was tested on this dataset and yielded high-accuracy results for both motion and person recognition.

Refractive cataract surgery.

PURPOSE OF REVIEW: The aim of this study to provide an overview of recent publications and opinions in refractive cataract surgery. RECENT FINDINGS: With the advent of intraocular lenses (IOLs) on different platforms, the surgeon has a wide arena of types of IOL to choose, depending on the patient's visual requirement. Optimization of the tear film, integrating tomography and topography devices for appropriate keratometry values, biometry, use of advanced formulas for IOL power calculation and application of newer IOLs can help achieve target refraction in cases scheduled for cataract surgery. Intraoperative aberrometry can be a useful aid for cataract surgery in postrefractive cases and can help minimize residual postoperative astigmatism. SUMMARY: Evolvement and rapid advancement of technology allows to impart desired refractive outcomes in most of the cases postcataract surgery. Appropriate preoperative and intraoperative factors should be considered to achieve the desired postoperative outcome.

Cataract surgery after corneal refractive surgery: preoperative considerations and management.

PURPOSE OF REVIEW: Corneal refractive surgery (CRS) is one of the most popular eye procedures, with more than 40 million cases performed globally. As CRS-treated patients age and develop cataract, the number of cases that require additional preoperative considerations and management will increase around the world. Thus, we provide an up-to-date, concise overview of the considerations and outcomes of cataract surgery in eyes with previous CRS, including surface ablation, laser in-situ keratomileusis (LASIK), and small-incision lenticule extraction (SMILE). RECENT FINDINGS: Challenges associated with accurate biometry in eyes with CRS have been mitigated recently through total keratometry, ray tracing, intraoperative aberrometry, and machine learning assisted intraocular lens (IOL) power calculation formulas to improve prediction. Emerging studies have highlighted the superior performance of ray tracing and/or total keratometry-based formulas for IOL power calculation in eyes with previous SMILE. Dry eye remains a common side effect after cataract surgery, especially in eyes with CRS, though the risk appears to be lower after SMILE than LASIK (in the short-term). Recent presbyopia-correcting IOL designs such as extended depth of focus (EDOF) IOLs may be suitable in carefully selected eyes with previous CRS. SUMMARY: Ophthalmologists will increasingly face challenges associated with the surgical management of cataract in patients with prior CRS. Careful preoperative assessment of the ocular surface, appropriate use of IOL power calculation formulas, and strategies for presbyopia correction are key to achieve good clinical and refractive outcomes and patient satisfaction. Recent advances in CRS techniques, such as SMILE, may pose new challenges for such eyes in the future.