Xiaoxi Yao

2 months ago | nature.com | Demilade Adedinsewo |Bosede Afolabi |Oyewole A. Kushimo |Kehinde F. Ibiyemi |Hadijat Olaide Raji |Sadiq H. Ringim | +9 more

Correction to: Nature Medicine https://doi.org/10.1038/s41591-024-03243-9, published online 2 September 2024. In the version of this article initially published, the Acknowledgements did not include thanks to the Dalio Philanthropies. The error has been corrected in the HTML and PDF versions of the article.

Sep 2, 2024 | nature.com | Demilade Adedinsewo |Bosede Afolabi |Oyewole A. Kushimo |Kehinde F. Ibiyemi |Hadijat Olaide Raji |Sadiq H. Ringim | +9 more

AbstractNigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period.

Apr 16, 2024 | translational-medicine.biomedcentral.com | Yanzhu Chen |Haoyu Li |Xiaoxi Yao |Yongan Meng |Yuqian Hu |Dan Liu | +7 more

Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME. A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes. Clinical implications of the OCT-omics prediction model were assessed by decision curve analysis. Performance metrics (sensitivity, specificity, F1 score, and AUC) were calculated. The logistic, SVM, and BPNN classifiers demonstrated robust discriminative abilities in both the training and test sets. In the training set, the logistic classifier achieved a sensitivity of 0.904, specificity of 0.741, F1 score of 0.887, and AUC of 0.910. The SVM classifier showed a sensitivity of 0.923, specificity of 0.667, F1 score of 0.881, and AUC of 0.897. The BPNN classifier exhibited a sensitivity of 0.962, specificity of 0.926, F1 score of 0.962, and AUC of 0.982. Similar discriminative capabilities were maintained in the test set. The OCT-omics scores were significantly higher in the non-persistent DME group than in the persistent DME group (p < 0.001). OCT-omics scores were also positively correlated with the rate of decline in central subfield thickness after treatment (Pearson’s R = 0.44, p < 0.001). The developed OCT-omics model accurately assesses anti-VEGF treatment response in DME patients. The model’s robust performance and clinical implications highlight its utility as a non-invasive tool for personalized treatment prediction and retinal pathology assessment.