Histopathology is the gold standard in cancer diagnosis. However, attenuated total reflectance (ATR)-Fourier transform ...

A diagnostic model based on delayed post-gadolinium enhancement magnetic resonance imaging (DEMRI) improves the accuracy of ...

An artificial intelligence tool outperforms human readers in diagnostic mammography but shows lower accuracy at the lesion ...

Drs. Fatemeh Ardeshir-Larijani and Mohamed Abazeed discuss a validated AI model that can predict how patients with advanced NSCLC will respond to immunotherapy.

Aim This study evaluates if characteristics (eg, location, size, volume) of clusters of defects on an initial visual field ...

Indicators such as the pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were computed to evaluate the predictive effect of ...

Methods Predictive models were developed using logistic regression based on data from several large previously published datasets; for all 31 possible combinations of five biomarkers – ...

Objective: The manual interpretation of electroencephalogram (EEG) signals for detecting epileptic seizures is time-consuming and labor-intensive, highlighting the critical importance of exploring ...

The proposed clinical cutoff for mSCAT and mVOMS >= 2.Abstract 27 (9A) Figure 1Conclusion These results suggest a mSCAT symptom list should be considered, possibly to 6 questions for an AUC ...

Bruker Launches Novel timsMetabo™ Mass Spectrometer for Breakthrough 4D-Metabolomics and 4D-Lipidomics Sensitivity, Specificity and Annotation Confidence - at Speed, Depth, and Scale ...

Diagnostic accuracy was assessed using a bivariate model (mada::reitsma), incorporating sensitivity, specificity and the area under the curve (AUC; a quantification of overall performance, where ...

After 10-fold cross-validation and model refinement, the XGBoost model achieved the highest AUC values with sensitivity, specificity, and accuracy of 95.4, 93.8, and 94.5%, respectively. In the test ...