Better data annotation—more accurate, detailed or contextually rich—can drastically improve an AI system’s performance, adaptability and fairness.

The aim of the current study is to develop and train a machine learning algorithm to create a prediction model to be used in clinical practice—as an online prediction tool—to estimate recurrence rates ...

Conclusion Prediction models of EC recurrence built with ML and DL analytics had better performance than models with clinical and pathologic data alone. Prospective validation is required to determine ...

Unrepresentative pre-clinical models contribute to poor translation of glioblastoma research. We present an induced-recurrence xenograft model validated in longitudinal samples to uncover ...

Researchers sought to test the performance of a deep learning model system using longitudinal magnetic resonance imaging (MRI) in predicting early recurrence in patients with hepatocellular carcinoma.

Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model ...

The current state-of-the-art time series modeling architectures include Recurrent Neural Networks (RNN), ordinary differential equation (ODE) based, and flow-matching methods. They have successfully ...

Repeat modeling was performed with four of the highest scoring features, and ROC analyses demonstrated minimal loss in accuracy (AUC = 0.63). Conclusion: Machine-learning models based on 24-hour urine ...