FDA clears new Samsung DR system

AuntMinnie.com staff writers

Aug 5, 2015

Samsung Electronics America has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its GC85A digital radiography (DR) unit.

GC85A is a ceiling-mounted DR system that includes Samsung's S-Vue image reconstruction engine; a wireless, lightweight S-Detector with high detective quantum efficiency and a portable grid; the company's Smart Control operator control protocol; and Samsung's S-Align feature, which improves imaging quality during free exams by displaying the detector's angle to the system's tube head unit.

Other features of GC85A include Samsung's Smart Stitching protocol for stitched imaging and the company's S-Share compatibility concept, in which S-Detectors are compatible between Samsung DR systems. When a detector is discharged, it can be used with detectors from other compatible Samsung DR equipment.

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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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