FDA clears Medicatech DR system

AuntMinnie.com staff writers

Jun 6, 2019

The U.S. Food and Drug Administration (FDA) has cleared the MasteRad digital radiography (DR) system from Medicatech.

MasteRad is a modular system that is completely manufactured in the U.S., according to the company. This reduces shipping taxes, import taxes, and long shipping lead times.

Customers have the choice of an elevating or nonelevating table, manual or motorized movement, and a wide range of DR flat panels in both regular and high definition. What's more, the length and size of MasteRad components can be customized based on available space and individual needs of clients.

The system also includes Voyance, acquisition software that contains advanced tools, one-page design, and fast database search.

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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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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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