49 y/o with Renal Failure, Pain after Fall

In external validation testing, a deep learning model demonstrated an average AUC of 87.6 percent for detecting clinically significant prostate cancer (csPCA) on prostate MRI for patients with PI-RADS 3 assessments.

An interview with two experts to discuss newly released guidance for using standardized MRI protocols for patients with this condition.

For patients with newly diagnosed obstructive coronary artery disease (CAD), a multimodal machine learning model offered an 86 percent AUC for predicting MACE, which was 10 percent higher than CCTA alone and over 35 percent higher than the Framingham Risk Score.

Drs. Charles Liu and Jonathan Russin discuss the benefits and potential of fPACT, the alternative to functional MRI.

An emerging deep learning radiomics model based on biparametric MRI (bpMRI) offered a 14 to 17 percent higher AUC range than PI-RADS scoring for predicting the aggressiveness of prostate cancer, according to new research findings.

Emerging research suggests that a deep learning model may offer 92 percent sensitivity in lung tumor detection on CT scans and up to a 59 percent reduction in tumor segmentation time.