Cargando…

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 cal...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Shao-Chun, Chiu, Hung-Wen, Chen, Chun-Chen, Woung, Lin-Chung, Lo, Chung-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306861/
https://www.ncbi.nlm.nih.gov/pubmed/30477203
http://dx.doi.org/10.3390/jcm7120475
_version_ 1783382874273087488
author Chen, Shao-Chun
Chiu, Hung-Wen
Chen, Chun-Chen
Woung, Lin-Chung
Lo, Chung-Ming
author_facet Chen, Shao-Chun
Chiu, Hung-Wen
Chen, Chun-Chen
Woung, Lin-Chung
Lo, Chung-Ming
author_sort Chen, Shao-Chun
collection PubMed
description 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.
format Online
Article
Text
id pubmed-6306861
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-63068612019-01-02 A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema Chen, Shao-Chun Chiu, Hung-Wen Chen, Chun-Chen Woung, Lin-Chung Lo, Chung-Ming J Clin Med Article 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. MDPI 2018-11-24 /pmc/articles/PMC6306861/ /pubmed/30477203 http://dx.doi.org/10.3390/jcm7120475 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Shao-Chun
Chiu, Hung-Wen
Chen, Chun-Chen
Woung, Lin-Chung
Lo, Chung-Ming
A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema
title A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema
title_full A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema
title_fullStr A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema
title_full_unstemmed A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema
title_short A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema
title_sort novel machine learning algorithm to automatically predict visual outcomes in intravitreal ranibizumab-treated patients with diabetic macular edema
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306861/
https://www.ncbi.nlm.nih.gov/pubmed/30477203
http://dx.doi.org/10.3390/jcm7120475
work_keys_str_mv AT chenshaochun anovelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema
AT chiuhungwen anovelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema
AT chenchunchen anovelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema
AT wounglinchung anovelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema
AT lochungming anovelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema
AT chenshaochun novelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema
AT chiuhungwen novelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema
AT chenchunchen novelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema
AT wounglinchung novelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema
AT lochungming novelmachinelearningalgorithmtoautomaticallypredictvisualoutcomesinintravitrealranibizumabtreatedpatientswithdiabeticmacularedema