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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study

Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study

Univariate analysis was performed using 2-tailed t tests and chi-square tests to identify significant differences between the p CR and non-p CR groups. Features that exhibited statistical significance (P The performance of the model was evaluated using accuracy and the area under the receiver operating characteristic curve (AUROC).

Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

JMIR Cancer 2025;11:e64685

Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization

Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization

The P values, calculated using a paired t test, compare the area under the curve (AUC) of each method with that of our model, with P a Not available. The AI-driven management platform developed in this study not only enhances the precision of strabismus eye region cropping but also integrates functionalities that improve overall patient care (as shown in Figure 5). The interface of the artificial intelligence-driven management platform for the care of patients with strabismus.

Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu

J Med Internet Res 2025;27:e74402