Diagnostic Imaging

Portable ultrasound maker Fujifilm SonoSite has turned to the nonprofit Allen Institute for Artificial Intelligence’s AI2 Incubator for help harnessing AI’s image-interpretation potential.

An AI-based smartphone app for detecting cancer in skin lesions has proven quite capable, achieving 95.1% sensitivity.

Terason, a portable ultrasound manufacturer based in Massachusetts, is partnering with DiA Imaging Analysis, which is headquartered in Israel, to bring AI to healthcare providers using Terason machines for heart imaging.

Gadolinium-based contrast agents (GBCAs) can often bring out the best in MRI, but they’re controversial and thus increasingly avoided. A pilot study in Germany shows how an algorithm might substitute for an injection to track tumors of the brain and spinal cord (aka gliomas).

Aided by augmented reality, AI and portable neuroimaging technology, physicians may soon be able to tease out images of patients’ brains—right there in the doctor’s office—to see how much pain each patient is suffering.

Health officials south of the border may soon be able to fight a nasty disease using just their smartphones and an AI tool for reverse image searches.

Radiology is the medical specialty most conducive to clinical AI applications. After all, the pre-AI technique of computer-aided detection has been used in mammography since 1998, for example. So it shouldn’t come as a surprise to find AI “app stores” rising in radiology.

Researchers in Texas and Taiwan have collaborated to develop a deep-learning tool that can precisely asses the risk of breast cancer—and with it the need for biopsy—in patients with lesions of questionable concern found in mammograms.

Radiology, the medical specialty into which AI has made the furthest initial inroads in the U.S., is embracing the technology in France. And this is so despite French radiologists feeling underinformed on AI up to now.

Physicians and scientists in Germany have developed an artificial neural network that’s capable of interpreting brain MRI scans to tell physicians how brain tumors are responding to chemotherapy and radiation therapy, according to a study published in The Lancet Oncology.

Researchers have trained an AI algorithm to identify and locate radius and ulna fractures on wrist radiographs with 98 percent sensitivity and 73 percent specificity, on a per study basis. Their findings were published in the Radiological Society of North America’s new journal Radiology: Artificial Intelligence.