Report: DR market to reach $13B in 2018

AuntMinnie.com staff writers

Aug 28, 2012

The increasing adoption of telemedicine and medical telemetry will boost the digital radiology market over the next six years, according to a new market report published by Transparency Market Research. The market is expected to reach $13.3 billion globally in 2018.

The report, "Digital Radiology Market -- Global Industry Size, Market Share, Trends, Analysis and Forecast, 2012-2018," finds that the current global radiology systems market is estimated at $9.7 billion and is expected to increase at a compound annual growth rate of 5.4% from 2012 to 2018.

North America holds about 41.9% of the global digital radiology market revenue share, with Europe coming in second; the report finds that the North American market will maintain its lead position in terms of revenue until 2018. As for volume, the global digital radiology market is expected to reach 158,000 units by 2018.

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Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red). Predictions from the 6x-YOLOv5 ensemble trained on histogram equalized input images with a y = 0.2 avalanche scheme, achieving 0.536 precision, 0.795 recall, and 0.723 F2 score. Image and caption courtesy of Scientific Reports.

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(a) Raw example of a dynamic digital radiograph. (b) Contrast equalization using contrast-limited adaptive equalization shows improved anatomical visualization. (c) Training data is generated through manually labeling using SLEAP’s graphical user interface. (d-e) Neural network detection schematic. For each quadrant a series of two networks were used. The first identifies the quadrant center, the second using input of the first, then identifies individual anatomical landmarks within a quadrant. (d) Four neural networks were trained, each with the objective of identifying a chest quadrant centroid. The four separate quadrants are visualized as colored boxes (right upper quadrant, green; right lower quadrant, blue; left upper quadrant, red; left lower quadrant, yellow). (e) Serial centroid then anatomical identification. Two quadrant examples showing confidence maps of trained neural networks identifying quadrant centroids (left) and then anatomical confidence maps (right). Image and caption courtesy of Chest Pulmonary.

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