Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study
BackgroundRetinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular endothelial growth factor (anti-VEGF) is required to prevent retinal detachment with consequent vision impairment and loss. Howe...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-04-01
|
| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e60367 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850155265168506880 |
|---|---|
| author | Rong Wu Yu Zhang Peijie Huang Yiying Xie Jianxun Wang Shuangyong Wang Qiuxia Lin Yichen Bai Songfu Feng Nian Cai Xiaohe Lu |
| author_facet | Rong Wu Yu Zhang Peijie Huang Yiying Xie Jianxun Wang Shuangyong Wang Qiuxia Lin Yichen Bai Songfu Feng Nian Cai Xiaohe Lu |
| author_sort | Rong Wu |
| collection | DOAJ |
| description |
BackgroundRetinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular endothelial growth factor (anti-VEGF) is required to prevent retinal detachment with consequent vision impairment and loss. However, anti-VEGF has been reported to be associated with ROP reactivation. Therefore, an accurate prediction of reactivation after treatment is urgently needed.
ObjectiveTo develop and validate prediction models for reactivation after anti-VEGF intravitreal injection in infants with ROP using multimodal machine learning algorithms.
MethodsInfants with ROP undergoing anti-VEGF treatment were recruited from 3 hospitals, and conventional machine learning, deep learning, and fusion models were constructed. The areas under the curve (AUCs), accuracy, sensitivity, and specificity were used to show the performances of the prediction models.
ResultsA total of 239 cases with anti-VEGF treatment were recruited, including 90 (37.66%) with reactivation and 149 (62.34%) nonreactivation cases. The AUCs for the conventional machine learning model were 0.806 and 0.805 in the internal validation and test groups, respectively. The average AUC, sensitivity, and specificity in the test for the deep learning model were 0.787, 0.800, and 0.570, respectively. The specificity, AUC, and sensitivity for the fusion model were 0.686, 0.822, and 0.800 in a test, separately.
ConclusionsWe constructed 3 prediction models for ROP reactivation. The fusion model achieved the best performance. Using this prediction model, we could optimize strategies for treating ROP in infants and develop better screening plans after treatment. |
| format | Article |
| id | doaj-art-0a6f2583710f4ae7bed5df3dbc834a68 |
| institution | OA Journals |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-0a6f2583710f4ae7bed5df3dbc834a682025-08-20T02:24:59ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-04-0127e6036710.2196/60367Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model StudyRong Wuhttps://orcid.org/0000-0001-8714-1508Yu Zhanghttps://orcid.org/0000-0003-2057-8814Peijie Huanghttps://orcid.org/0009-0004-3963-3742Yiying Xiehttps://orcid.org/0009-0008-9922-4851Jianxun Wanghttps://orcid.org/0000-0002-6386-1807Shuangyong Wanghttps://orcid.org/0000-0003-1465-4200Qiuxia Linhttps://orcid.org/0009-0004-3646-6221Yichen Baihttps://orcid.org/0009-0009-6358-2363Songfu Fenghttps://orcid.org/0000-0002-3494-4013Nian Caihttps://orcid.org/0000-0002-7826-5055Xiaohe Luhttps://orcid.org/0000-0003-2173-7290 BackgroundRetinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular endothelial growth factor (anti-VEGF) is required to prevent retinal detachment with consequent vision impairment and loss. However, anti-VEGF has been reported to be associated with ROP reactivation. Therefore, an accurate prediction of reactivation after treatment is urgently needed. ObjectiveTo develop and validate prediction models for reactivation after anti-VEGF intravitreal injection in infants with ROP using multimodal machine learning algorithms. MethodsInfants with ROP undergoing anti-VEGF treatment were recruited from 3 hospitals, and conventional machine learning, deep learning, and fusion models were constructed. The areas under the curve (AUCs), accuracy, sensitivity, and specificity were used to show the performances of the prediction models. ResultsA total of 239 cases with anti-VEGF treatment were recruited, including 90 (37.66%) with reactivation and 149 (62.34%) nonreactivation cases. The AUCs for the conventional machine learning model were 0.806 and 0.805 in the internal validation and test groups, respectively. The average AUC, sensitivity, and specificity in the test for the deep learning model were 0.787, 0.800, and 0.570, respectively. The specificity, AUC, and sensitivity for the fusion model were 0.686, 0.822, and 0.800 in a test, separately. ConclusionsWe constructed 3 prediction models for ROP reactivation. The fusion model achieved the best performance. Using this prediction model, we could optimize strategies for treating ROP in infants and develop better screening plans after treatment.https://www.jmir.org/2025/1/e60367 |
| spellingShingle | Rong Wu Yu Zhang Peijie Huang Yiying Xie Jianxun Wang Shuangyong Wang Qiuxia Lin Yichen Bai Songfu Feng Nian Cai Xiaohe Lu Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study Journal of Medical Internet Research |
| title | Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study |
| title_full | Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study |
| title_fullStr | Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study |
| title_full_unstemmed | Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study |
| title_short | Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study |
| title_sort | prediction of reactivation after antivascular endothelial growth factor monotherapy for retinopathy of prematurity multimodal machine learning model study |
| url | https://www.jmir.org/2025/1/e60367 |
| work_keys_str_mv | AT rongwu predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT yuzhang predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT peijiehuang predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT yiyingxie predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT jianxunwang predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT shuangyongwang predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT qiuxialin predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT yichenbai predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT songfufeng predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT niancai predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy AT xiaohelu predictionofreactivationafterantivascularendothelialgrowthfactormonotherapyforretinopathyofprematuritymultimodalmachinelearningmodelstudy |