Researchers at the State University of New York at Stony Brook have demonstrated a deep-learning algorithm that can quickly diagnose early-stage lung cancer on CT scans by combining computerized self-trained tumor identification with engineered identification of specific tumor features.

A model based on radiomic features extracted from CT scans can help predict which ground glass nodule (GGN) cases require surgery and may reduce overtreatment, according to researchers at the Affiliated Suzhou Hospital of Nanjing Medical University in Suzhou, China.

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A machine learning algorithm based on perfusion-weighted MRI accurately differentiated between benign and malignant tumors in the uterus, according to researchers at Tehran University of Medical Sciences (TUMS) in Iran.

Artificial intelligence (AI) technologies are advancing at a rapid rate and starting to make a direct impact on breast imaging. There is still a lot of work to be done, however, before AI can truly be trusted with making decisions that may impact a patient’s survival, according to a new commentary published in the American Journal of Roentgenology.

Fujifilm Medical Solutions USA will host an educational symposium highlighting the influence of artificial intelligence (AI) and present its new AI initiative at RSNA 2018 in Chicago.

Radiologists outperformed a convolutional neural network (CNN) and radiomic analysis (RA) at classifying contrast-enhancing lesions on multiparametric breast MRI, according to a Nov. 13 study published in Radiology. With more training, however, CNNs may soon close that gap.

The National Institutes of Health has awarded a $3.5 million grant to help researchers develop a robotic system that “destroys” metastatic brain tumors with high-intensity therapeutic ultrasound and real-time imaging.