scholarly journals Use of a Machine Learning Method in Predicting Refraction after Cataract Surgery

2021 ◽  
Vol 10 (5) ◽  
pp. 1103
Author(s):  
Tomofusa Yamauchi ◽  
Hitoshi Tabuchi ◽  
Kosuke Takase ◽  
Hiroki Masumoto
Keyword(s):  
Machine Learning ◽  
Cataract Surgery ◽  
Test Data ◽  
Training Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Power Calculation ◽  
Iol Power Calculation ◽  
Significant Difference ◽  
Iol Power

The present study aims to describe the use of machine learning (ML) in predicting the occurrence of postoperative refraction after cataract surgery and compares the accuracy of this method to conventional intraocular lens (IOL) power calculation formulas. In total, 3331 eyes from 2010 patients were assessed. The objects were divided into training data and test data. The constants for the IOL power calculation formulas and model training for ML were optimized using training data. Then, the occurrence of postoperative refraction was predicted using conventional formulas, or ML models were calculated using the test data. We evaluated the SRK/T formula, Haigis formula, Holladay 1 formula, Hoffer Q formula, and Barrett Universal II formula (BU-II); similar to ML methods, we assessed support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR), and neural network (NN). Among the conventional formulas, BU-II had the lowest mean and median absolute error of prediction. Therefore, we compared the accuracy of our method with that of BU-II. The absolute errors of some ML methods were lower than those of BU-II. However, no statistically significant difference was observed. Thus, the accuracy of our method was not inferior to that of BU-II.

scholarly journals Improving clinical refractive results of cataract surgery by machine learning

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7202 ◽  
Author(s):  
Martin Sramka ◽  
Martin Slovak ◽  
Jana Tuckova ◽  
Pavel Stodulka
Keyword(s):  
Clinical Results ◽  
Support Vector ◽  
Power Calculation ◽  
Prediction Errors ◽  
Neural Network Ensemble ◽  
Clinical Workflow ◽  
Clinical Method ◽  
Iol Power Calculation ◽  
Significant Difference ◽  
Iol Power

Aim To evaluate the potential of the Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) to improve the intraocular lens (IOL) power calculation for clinical workflow. Background Current IOL power calculation methods are limited in their accuracy with the possibility of decreased accuracy especially in eyes with an unusual ocular dimension. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery there is a risk of re-operation or further refractive correction. This may create potential complications and discomfort for the patient. Methods A dataset containing information about 2,194 eyes was obtained using data mining process from the Electronic Health Record (EHR) system database of the Gemini Eye Clinic. The dataset was optimized and split into the selection set (used in the design for models and training), and the verification set (used in the evaluation). The set of mean prediction errors (PEs) and the distribution of predicted refractive errors were evaluated for both models and clinical results (CR). Results Both models performed significantly better for the majority of the evaluated parameters compared with the CR. There was no significant difference between both evaluated models. In the ±0.50 D PE category both SVM-RM and MLNN-EM were slightly better than the Barrett Universal II formula, which is often presented as the most accurate calculation formula. Conclusion In comparison to the current clinical method, both SVM-RM and MLNN-EM have achieved significantly better results in IOL calculations and therefore have a strong potential to improve clinical cataract refractive outcomes.

The Machine Learning's Classification Methods Comparison to Estimate Electrofacies Type, Lithology and Hydrocarbon Fluids from Geophysical Well Log Data

2021 ◽  
Author(s):  
D. A. Panggabean
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Data Evaluation ◽  
Training Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Classification Methods ◽  
Evaluation Result ◽  
Qualitative And Quantitative ◽  
Shape Prediction

Supervised learning methods from machine learning are starting to be widely used in oil & gas data management. The usage of the method is adjusted to the purpose of data processing, including data classification and regression. In this research, there are six classification methods to estimate the electrofacies shape, lithology type, and fluids, namely Decision Tree (DT), Gaussian Naïve Bayes (GNB), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (XGB). This research compared those six methods qualitatively and quantitatively to obtain the best method. This research was conducted in the Maju Royal Field using one oil well data for training data and another one well as testing data. For validation purposes, 85% of the data was split for training and 15% for validation, aiming to evaluate the machine learning model through the correlation coefficient value. In the test data, qualitative and quantitative analyzes were also conducted. Qualitative analysis was performed by comparing the results of the electrofacies shape prediction with the original interpretation, lithology prediction with shale volume data, and prognosis of fluids with test zone data. Meanwhile, quantitatively, it is done by comparing the correct predictive data with the actual amount of data on each parameter. The training data evaluation result shows that KNN and XGB are suitable for electrofacies shape prediction. Meanwhile, lithology and fluid estimation are good with DT, KNN, and XGB methods. The qualitative and quantitative analysis result from the test data shows that the DT and GNB methods are suitable for estimating the electrofacies shape. In contrast, all methods are considered good at predicting and have good correlation values for calculating the lithology and fluids. Hence, both training and test data evaluation result has good correlation values

Abstract 1122‐000047: Machine Learning to Predict Stroke Outcomes after Mechanical Thrombectomy

2021 ◽  
Vol 1 (S1) ◽  
Author(s):  
Mehdi Bouslama ◽  
Leonardo Pisani ◽  
Diogo Haussen ◽  
Raul Nogueira
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Cerebral Artery ◽  
Training Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Anterior Circulation ◽  
Discriminative Performance ◽  
Post Procedure ◽  
Procedural Models

Introduction : Prognostication is an integral part of clinical decision‐making in stroke care. Machine learning (ML) methods have gained increasing popularity in the medical field due to their flexibility and high performance. Using a large comprehensive stroke center registry, we sought to apply various ML techniques for 90‐day stroke outcome predictions after thrombectomy. Methods : We used individual patient data from our prospectively collected thrombectomy database between 09/2010 and 03/2020. Patients with anterior circulation strokes (Internal Carotid Artery, Middle Cerebral Artery M1, M2, or M3 segments and Anterior Cerebral Artery) and complete records were included. Our primary outcome was 90‐day functional independence (defined as modified Rankin Scale score 0–2). Pre‐ and post‐procedure models were developed. Four known ML algorithms (support vector machine, random forest, gradient boosting, and artificial neural network) were implemented using a 70/30 training‐test data split and 10‐fold cross‐validation on the training data for model calibration. Discriminative performance was evaluated using the area under the receiver operator characteristics curve (AUC) metric. Results : Among 1248 patients with anterior circulation large vessel occlusion stroke undergoing thrombectomy during the study period, 1020 had complete records and were included in the analysis. In the training data (n = 714), 49.3% of the patients achieved independence at 90‐days. Fifteen baseline clinical, laboratory and neuroimaging features were used to develop the pre‐procedural models, with four additional parameters included in the post‐procedure models. For the preprocedural models, the highest AUC was 0.797 (95%CI [0.75‐ 0.85]) for the gradient boosting model. Similarly, the same ML technique performed best on post‐procedural data and had an improved discriminative performance compared to the pre‐procedure model with an AUC of 0.82 (95%CI [0.77‐ 0.87]). Conclusions : Our pre‐and post‐procedural models reliably estimated outcomes in stroke patients undergoing thrombectomy. They represent a step forward in creating simple and efficient prognostication tools to aid treatment decision‐making. A web‐based platform and related mobile app are underway.

Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports

2020 ◽  
pp. 865-874
Author(s):  
Enrico Santus ◽  
Tal Schuster ◽  
Amir M. Tahmasebi ◽  
Clara Li ◽  
Adam Yala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Systems ◽  
High Performance ◽  
Feature Model ◽  
Training Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Extreme Gradient Boosting ◽  
Pathology Reports

PURPOSE Literature on clinical note mining has highlighted the superiority of machine learning (ML) over hand-crafted rules. Nevertheless, most studies assume the availability of large training sets, which is rarely the case. For this reason, in the clinical setting, rules are still common. We suggest 2 methods to leverage the knowledge encoded in pre-existing rules to inform ML decisions and obtain high performance, even with scarce annotations. METHODS We collected 501 prostate pathology reports from 6 American hospitals. Reports were split into 2,711 core segments, annotated with 20 attributes describing the histology, grade, extension, and location of tumors. The data set was split by institutions to generate a cross-institutional evaluation setting. We assessed 4 systems, namely a rule-based approach, an ML model, and 2 hybrid systems integrating the previous methods: a Rule as Feature model and a Classifier Confidence model. Several ML algorithms were tested, including logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGB). RESULTS When training on data from a single institution, LR lags behind the rules by 3.5% (F1 score: 92.2% v 95.7%). Hybrid models, instead, obtain competitive results, with Classifier Confidence outperforming the rules by +0.5% (96.2%). When a larger amount of data from multiple institutions is used, LR improves by +1.5% over the rules (97.2%), whereas hybrid systems obtain +2.2% for Rule as Feature (97.7%) and +2.6% for Classifier Confidence (98.3%). Replacing LR with SVM or XGB yielded similar performance gains. CONCLUSION We developed methods to use pre-existing handcrafted rules to inform ML algorithms. These hybrid systems obtain better performance than either rules or ML models alone, even when training data are limited.

scholarly journals An innovative approach for determining the customized refractive index of ectatic corneas in cataractous patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shiva Pirhadi ◽  
Keivan Maghooli ◽  
Khosrow Jadidi
Keyword(s):  
Refractive Index ◽  
Cataract Surgery ◽  
Ray Tracing ◽  
Prediction Error ◽  
Power Calculation ◽  
Corneal Power ◽  
Iol Power Calculation ◽  
Iol Power ◽  
Hyperopic Shift ◽  
The Difference

Abstract The aim of this study is to determine the customized refractive index of ectatic corneas and also propose a method for determining the corneal and IOL power in these eyes. Seven eyes with moderate and severe corneal ectatic disorders, which had been under cataract surgery, were included. At least three months after cataract surgery, axial length, cornea, IOL thickness and the distance between IOL from cornea, and aberrometry were measured. All the measured points of the posterior and anterior parts of the cornea converted to points cloud and surface by using the MATLAB and Solidworks software. The implanted IOLs were designed by Zemax software. The ray tracing analysis was performed on the customized eye models, and the corneal refractive index was determined by minimizing the difference between the measured aberrations from the device and resulted aberrations from the simulation. Then, by the use of preoperative corneal images, corneal power was calculated by considering the anterior and posterior parts of the cornea and refractive index of 1.376 and the customized corneal refractive index in different regions and finally it was entered into the IOL power calculation formulas. The corneal power in the 4 mm region and the Barrett formula resulted the prediction error of six eyes within ± 1 diopter. It seems that using the total corneal power along with the Barrett formula can prevent postoperative hyperopic shift, especially in eyes with advanced ectatic disorders.

scholarly journals Cataract Surgery following Sequential Myopic and Hyperopic LASIK

2018 ◽  
Vol 9 (2) ◽  
pp. 264-268
Author(s):  
Tao Ming Thomas Chia ◽  
Hoon C. Jung
Keyword(s):  
Cataract Surgery ◽  
Intraocular Lens ◽  
Calculation Method ◽  
Cataract Extraction ◽  
Power Calculation ◽  
Iol Implantation ◽  
Patient Dissatisfaction ◽  
Iol Power Calculation ◽  
Iol Power

We report a case of patient dissatisfaction after sequential myopic and hyperopic LASIK in the same eye. We discuss the course of management for this patient involving eventual cataract extraction and intraocular lens (IOL) implantation with attention to the IOL power calculation method used.

scholarly journals Improving Intraocular Lens Calculation in Fuchs Endothelial Dystrophy According To Preoperative Degree of Corneal Edema

2021 ◽  
Author(s):  
Beatriz Gargallo-Martinez ◽  
Amanda Ortiz-Gomariz ◽  
Ana Maria Gomez-Ramirez ◽  
Angel Ramon Gutiérrez-Ortega ◽  
Jose Javier Garcia-Medina
Keyword(s):  
Cataract Surgery ◽  
Intraocular Lens ◽  
Refractive Error ◽  
Target Selection ◽  
Corneal Transplantation ◽  
Corneal Edema ◽  
Power Calculation ◽  
Iol Power Calculation ◽  
Iol Power ◽  
Fuchs Endothelial Dystrophy

Abstract Fuchs endothelial dystrophy (FED) is a bilateral, asymmetric, progressive corneal endothelium disorder that causes corneal edema. Resolution of corneal edema is only possible by corneal transplantation. Cataract surgery is a common surgery that replaces the natural lens of the eye by an artificial intraocular lens (IOL). The IOL-power calculation depends mainly on the anterior corneal keratometry and the axial length. In patients with FED, anterior keratometry may be affected by corneal edema and calculations may be less accurate. Therefore, the aim of this study is to establish the theorical postoperative refractive error due to corneal edema resolution after Descemet stripping endothelial keratoplasty combined with cataract surgery and IOL implantation. For this, anterior keratometry was measure preoperatively with edematous cornea and postoperatively after corneal edema resolution. Both keratometries were compared and used to calculate the respective theorical IOL-powers. The difference between target IOLs was used to establish the theorical refractive error due to corneal edema resolution. The results showed that corneal edema resolution induces a change in anterior keratometry, which affects IOL-power calculations and causes a hyperopic shift. The patients with moderate-to-severe preoperative corneal edema had higher theorical refractive error so their target selection should be adjusted for additional − 0.50D.

scholarly journals Improving the prediction of effective lens position for intraocular lens power calculations

2020 ◽  
Vol 17 (2) ◽  
pp. 233-242
Author(s):  
Juanita Noeline Chui ◽  
Keith Ong
Keyword(s):  
Cataract Surgery ◽  
Intraocular Lens ◽  
Sagittal Plane ◽  
Anterior Chamber Depth ◽  
Posterior Chamber ◽  
Power Calculation ◽  
Iol Power Calculation ◽  
Lens Position ◽  
Iol Power ◽  
Corneal Apex

Purpose: Achieving the desired post-operative refraction in cataract surgery requires accurate calculations for intraocular lens (IOL) power. Latest-generation formulae use anterior-chamber depth (ACD)—the distance from the corneal apex to the anterior surface of the lens—as one of the parameters to predict the post-operative IOL position within the eye, termed the effective lens position (ELP). Significant discrepancies between predicted and actual ELP result in refractive surprise. This study aims to improve the predictability of ELP. We hypothesise that predictions based on the distance from the corneal apex to the mid-sagittal plane of the cataractous lens would more accurately reflect the position of the principal plane of the non-angulated IOL within the capsular bag. Accordingly, we propose that predictions derived from ACD + ½LT (length thickness) would be superior to those from ACD alone. Design: Retrospective cohort study, comparing ELP predictions derived from ACD to aproposed prediction parameter. Method: This retrospective study includes data from 162 consecutive cataract surgery cases, with posterior-chamber IOL (AlconSN60WF) implantation. Pre- and postoperative biometric measurements were made using the IOLMaster700 (ZEISS, Jena, Germany). The accuracy and reliability of ELP predictions derived from ACD and ACD + ½LT were compared using software-aided analyses. Results: An overall reduction in average ELP prediction error (PEELP) was achieved using the proposed parameter (root-mean-square-error [RMSE] = 0.50 mm), compared to ACD (RMSE = 1.57 mm). The mean percentage PEELP, comparing between eyes of different axial lengths, was 9.88% ± 3.48% and −34.9% ± 4.79% for predictions derived from ACD + ½LT and ACD, respectively. A 44.10% ± 5.22% mean of differences was observed (p < 0.001). Conclusion: ACD + ½LT predicts ELP with greater accuracy and reliability than ACD alone; its use in IOL power calculation formulae may improve refractive outcomes.

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