A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema

Chen, Shaochun; Chiu, Hung-Wen; Chen, Chun‐Chen; Woung, Lin-Chung; Lo, Chung-Ming

doi:10.3390/jcm7120475

Cited by 21 publications

(20 citation statements)

References 39 publications

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“…So, there is an increasing need to develop precise prognostic predictions in DME patients after anti-VEGF therapy due to increasing number of both DME patients and anti-VEGF medications used (37). With the development of ML, more opportunities than ever were obtained to accurately predict the posttreatment outcomes of anti-VEGF therapy in DME patients.…”

Section: Discussionmentioning

confidence: 99%

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning

Zhang¹,

Hu²,

Cao³

et al. 2021

Ann Transl Med

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Background: This study aimed to predict the treatment outcomes in patients with diabetic macular edema (DME) after 3 monthly anti-vascular endothelial growth factor (VEGF) injections using machine learning (ML) based on pretreatment optical coherence tomography (OCT) images and clinical variables.Methods: An ensemble ML system consisting of four deep learning (DL) models and five classical machine learning (CML) models was developed to predict the posttreatment central foveal thickness (CFT) and the best-corrected visual acuity (BCVA). A total of 363 OCT images and 7,587 clinical data records from 363 eyes were included in the training set (304 eyes) and external validation set (59 eyes). The DL models were trained using the OCT images, and the CML models were trained using the OCT images features and clinical variables. The predictive posttreatment CFT and BCVA values were compared with true outcomes obtained from the medical records.Results: For CFT prediction, the mean absolute error (MAE), root mean square error (RMSE), and R 2 of the best-performing model in the training set was 66.59, 93.73, and 0.71, respectively, with an area under receiver operating characteristic curve (AUC) of 0.90 for distinguishing the eyes with good anatomical response. The MAE, RMSE, and R 2 was 68.08, 97.63, and 0.74, respectively, with an AUC of 0.94 in the external validation set. For BCVA prediction, the MAE, RMSE, and R 2 of the best-performing model in the training set was 0.19, 0.29, and 0.60, respectively, with an AUC of 0.80 for distinguishing eyes with a good functional response. The external validation achieved a MAE, RMSE, and R 2 of 0.13, 0.20, and 0.68, respectively, with an AUC of 0.81.

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Section: Discussionmentioning

confidence: 99%

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning

Zhang¹,

Hu²,

Cao³

et al. 2021

Ann Transl Med

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“…For most clinicians, a well estimated noninvasive diagnostic model or disease outcome classifier would help to make a correct decision instead of invasive detection. Based on these clinical demands, the BP-ANN training process had the ability to deal with unrecognized confounders for constructing the more accurate classifier, which could transfer training achievements to the unknown information between input variables and clinical outcomes [78][79][80].…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

“…In this process, the weights between neurons were adjusted gradually according to the direction of local improvement, which may enable the algorithm and the weights into local extremum convergence [85]. In addition, BP-ANN was sensitive to initial weights in the network and different initialized networks tend to converge to the related local minimum and many researchers constructed different models after training [79,[86][87][88][89].…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

Wei

Yang

2021

Computational and Mathematical Methods in Medicine

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Introduction. A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure. Methods. We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria. Results. Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer. Conclusion. The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.

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“…Diagnostic equipment helps the doctor to make a diagnosis, but at the same time provides a huge amount of digital material. Only using interdisciplinary knowledge and directions, such as mathematical modeling and programming, it is possible to analyze large amounts of digital data and to predict the course of diseases [22][23][24][25].…”

Section: Digital Medicine In Ophthalmologymentioning

confidence: 99%

Assessment of the development of primary open-angle glaucoma and diabetic retinopathy using digital medicine

Vorobyeva

2020

E3S Web Conf.

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The leading place in the structure of blindness and low vision is occupied by two diseases: primary open-angle glaucoma and diabetic retinopathy. The combination of two diseases in one eye gives an even more severe prognosis for vision. Modern diagnostic equipment helps in accurate and early diagnosis of the disease by analyzing digital data of the optic nerve head and macular area of the retina according to the results of an optical coherence tomograph. A mathematical model for predicting the development of diabetic retinopathy and primary open-angle glaucoma according to the proposed mathematical criteria has been developed. The criteria represent the ratio of the integral area of the neuroretinal rim to the integral volume of the neuroretinal rim; the retinal thickness criterion and criterion of the primary open-angle glaucoma and diabetic retinopathy are calculated. A computer program has been developed. The algorithm of the program, which includes windows for entering digital data of the macular zone and parameters of the optic nerve head, is presented. The result of the program is the statement of the risk of developing combined pathology of the fundus in a particular patient.

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A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema

Cited by 21 publications

References 39 publications

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

Assessment of the development of primary open-angle glaucoma and diabetic retinopathy using digital medicine

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