Search Results for: mri world

he announcement that Agfa will investigate options to separate its healthcare IT business from the rest of the healthcare unit is not surprising

Cone-beam technology is a relative newcomer to medical diagnostic imaging; until recently it has predominately been used in the world of dental healthcare. It has been marketed to the medical world on a small scale so far, but could it be the ‘next big thing' in medical imaging?

Advances in technology and the increase in population continue to apply pressure to radiologists across the world. Although the quality of the images in increasing, so is the quantity; there were ~22.5 million x-ray images taken by the UK NHS in 2016.

For companies developing machine learning solutions for radiology, strategic collaborations and partnerships with healthcare providers are king.

Rapid advancements in machine learning, most notably deep learning techniques, are fuelling renewed growth for medical image analysis software tools. We estimate that the global market1 for these products will be worth nearly $300 million this year and will more than double in size by 2021. But which clinical applications are driving this growth?

The market for Quantitative Imaging Software1 comprises a wide range of software tools that provide automated measurements of anatomical structures, for various body sites and across multiple modalities. These tools vary in complexity, from tools that provide size measurements, to tools that provide more advanced metrics, such as perfusion analysis and texture analysis.

Machine learning was undoubtedly one of the hottest topics in radiology last year, with a steady stream of academic research papers highlighting how machine learning, particularly deep learning, can outperform traditional algorithms or manual processes in certain use-cases.

Artificial Intelligence was undoubtedly one of the key themes at this year’s RSNA, featuring prominently on the exhibition floor and in several scientific sessions. At least 20 companies displayed products featuring AI technologies and a handful more used AI as a key part of their marketing messages, even if the use-case wasn’t entirely clear.

The application of machine learning in radiology is evolving at a rapid pace and whilst fully automated diagnostic systems are still several years away, there is a growing number of machine learning tools available now that are helping radiologists to improve their efficiency and to make better diagnoses.