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

Purpose: Artificial neural networks (ANNs) are one type of artificial intelligence. Here, we use an ANN-based machine learning algorithm to automatically predict visual outcomes after ranibizumab treatment in diabetic macular edema. Methods: Patient data were used to optimize ANNs for regression calculation. The target was established as the final visual acuity at 52, 78, or 104 weeks. The input baseline variables were sex, age, diabetes type or condition, systemic diseases, eye status and treatment time tables. Three groups were randomly devised to build, test and demonstrate the accuracy of the algorithms. Results: At 52, 78 and 104 weeks, 512, 483 and 464 eyes were included, respectively. For the training group, testing group and validation group, the respective correlation coefficients were 0.75, 0.77 and 0.70 (52 weeks); 0.79, 0.80 and 0.55 (78 weeks); and 0.83, 0.47 and 0.81 (104 weeks), while the mean standard errors of final visual acuity were 6.50, 6.11 and 6.40 (52 weeks); 5.91, 5.83 and 7.59; (78 weeks); and 5.39, 8.70 and 6.81 (104 weeks). Conclusions: Machine learning had good correlation coefficients for predicating prognosis with ranibizumab with just baseline characteristics. These models could be the useful clinical tools for prediction of success of the treatments.

[1]  L. Aiello,et al.  Vascular endothelial growth factor in ocular fluid of patients with diabetic retinopathy and other retinal disorders. , 1994, The New England journal of medicine.

[2]  I. Dimopoulos,et al.  Application of neural networks to modelling nonlinear relationships in ecology , 1996 .

[3]  T. Gardner,et al.  Vascular Endothelial Growth Factor Induces Rapid Phosphorylation of Tight Junction Proteins Occludin and Zonula Occluden 1 , 1999, The Journal of Biological Chemistry.

[4]  Igor Kononenko,et al.  Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.

[5]  L. Pelletier,et al.  Report summary. Diabetes in Canada: facts and figures from a public health perspective. , 2012, Chronic diseases and injuries in Canada.

[6]  L. Aiello,et al.  Intravitreal ranibizumab for diabetic macular edema with prompt versus deferred laser treatment: three-year randomized trial results. , 2012, Ophthalmology.

[7]  Kenneth W. Tobin,et al.  Exudate-based diabetic macular edema detection in fundus images using publicly available datasets , 2012, Medical Image Anal..

[8]  N. Bressler,et al.  Exploratory analysis of the effect of intravitreal ranibizumab or triamcinolone on worsening of diabetic retinopathy in a randomized clinical trial. , 2013, JAMA ophthalmology.

[9]  J. Marrero,et al.  Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma , 2013, The American Journal of Gastroenterology.

[10]  N. Bressler,et al.  Prevalence of and risk factors for diabetic macular edema in the United States. , 2014, JAMA ophthalmology.

[11]  P. Mitchell,et al.  Three-year outcomes of individualized ranibizumab treatment in patients with diabetic macular edema: the RESTORE extension study. , 2014, Ophthalmology.

[12]  A. Statnikov,et al.  Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application. , 2014, Journal of psychiatric research.

[13]  Jennifer K. Sun,et al.  Aflibercept, bevacizumab, or ranibizumab for diabetic macular edema. , 2015, The New England journal of medicine.

[14]  M. Thiel,et al.  Comparison of Outcomes and Costs of Ranibizumab and Aflibercept Treatment in Real-Life , 2015, PloS one.

[15]  N. Bressler,et al.  RANIBIZUMAB PLUS PROMPT OR DEFERRED LASER FOR DIABETIC MACULAR EDEMA IN EYES WITH VITRECTOMY BEFORE ANTI-VASCULAR ENDOTHELIAL GROWTH FACTOR THERAPY , 2015, Retina.

[16]  Na Li,et al.  Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image , 2016, Comput. Intell. Neurosci..

[17]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[18]  Lei Zhang,et al.  A novel mathematical model to predict prognosis of burnt patients based on logistic regression and support vector machine. , 2016, Burns : journal of the International Society for Burn Injuries.

[19]  P. Ojiambo,et al.  Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models , 2016, Front. Plant Sci..

[20]  A. Fursova,et al.  [Effectiveness of diffuse diabetic macular edema treatment in relation to structural changes in macular region]. , 2016, Vestnik oftalmologii.

[21]  S. Sivaprasad,et al.  Real-World Outcomes of Ranibizumab Treatment for Diabetic Macular Edema in a United Kingdom National Health Service Setting. , 2016, American journal of ophthalmology.

[22]  N. Bressler,et al.  Persistent Macular Thickening After Ranibizumab Treatment for Diabetic Macular Edema With Vision Impairment. , 2016, JAMA ophthalmology.

[23]  N. Bressler,et al.  Five-Year Outcomes of Ranibizumab With Prompt or Deferred Laser Versus Laser or Triamcinolone Plus Deferred Ranibizumab for Diabetic Macular Edema. , 2016, American journal of ophthalmology.

[24]  Meizi Wang,et al.  Predictors of short-term outcomes related to central subfield foveal thickness after intravitreal bevacizumab for macular edema due to central retinal vein occlusion. , 2016, International journal of ophthalmology.

[25]  Katechan Jampachaisri,et al.  Analysis of significant factors for dengue fever incidence prediction , 2016, BMC Bioinformatics.

[26]  O. Lunacsek,et al.  Comorbidity and health care visit burden in working-age commercially insured patients with diabetic macular edema , 2016, Clinical ophthalmology.

[27]  M. Şekeroğlu,et al.  Objective evaluation of lens clarity after the intravitreal injection of sustained‐release dexamethasone implant , 2016, Journal of cataract and refractive surgery.

[28]  Chandima Gomes,et al.  Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer , 2017, Comput. Math. Methods Medicine.

[29]  Sabato Marco Siniscalchi,et al.  Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Haibo Mi,et al.  Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image , 2017, Molecules.

[31]  A. Fursova,et al.  [Clinical associations between photoreceptor status and visual outcomes in diabetic macular edema]. , 2017, Vestnik oftalmologii.

[32]  Kevin Jeffay,et al.  Scientific Training in the Era of Big Data: A New Pedagogy for Graduate Education , 2017, Big Data.

[33]  D. Călugăru,et al.  Real-World Outcomes of Ranibizumab Treatment for Diabetic Macular Edema in a United Kingdom National Health Service Setting. , 2017, American journal of ophthalmology.

[34]  N. Bhagat,et al.  Practical Lessons from Protocol T for the Management of Diabetic Macular Edema. , 2017, Developments in ophthalmology.

[35]  Yudong Zhang,et al.  A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm. , 2017, CNS & neurological disorders drug targets.

[36]  K. Suzuma,et al.  Relation between macular morphology and treatment frequency during twelve months with ranibizumab for diabetic macular edema , 2017, PloS one.

[37]  N. Bhagat,et al.  Practical Lessons from Protocol I for the Management of Diabetic Macular Edema. , 2017, Developments in ophthalmology.

[38]  V. M. Salerno,et al.  An Extreme Learning Machine Approach to Effective Energy Disaggregation , 2018, Electronics.