Effectiveness of diffuse diabetic macular edema treatment in relation to structural changes in macular region

Фурсова, А. Ж.; Chubar, N V; Tarasov, M S; Sayfullina, I. F.; Pustovaya, G. G.

doi:10.17116/oftalma2016132435-42

Cited by 2 publications

(2 citation statements)

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“…Several studies have investigated approaches for predicting DME treatment outcomes after IVI anti-VEGF therapy and report that an important factor is visual acuity at baseline [7,20,30,31,32,33]. Our study using machine learning also found that baseline visual acuity had a significant role in predicting outcomes at three different time points.…”

Section: Discussionsupporting

confidence: 59%

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

Chen

Chiu

Chen

et al. 2018

JCM

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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.

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Section: Discussionsupporting

confidence: 59%

“…In contrast to previous reports, we did not find that baseline OCT parameters influenced visual outcomes. This difference is likely because we only included baseline OCT measurements in our models, while prior studies also included follow-up OCT measurements [30,32,36,37].…”

Section: Discussionmentioning

confidence: 99%