Lunit enters supply contract with Samsung Electronics

AuntMinnie.com staff writers

Jan 16, 2024

Lunit has inked a supply contract with Samsung Electronics that will see the integration of its AI technology into Samsung's x-ray devices.

Through the contract, Lunit will supply Samsung Electronics with two AI-powered chest screening software products, Lunit Insight CXR and Lunit Insight CXR Triage. The beginning phase of this collaboration will see the Lunit-enhanced x-ray devices sold in the U.S., Canadian, and European markets. Future phases aim to expand this to the Middle East, South America, and Southeast Asia.

Dice similarity index measurements compare the x-ray pulsatility index (XPI) to digital subtraction angiography in patients with suspected chronic thromboembolic pulmonary hypertension. XPI is green, pulmonary angiography is pink and the overlap between the two is white. Image courtesy of the ARRS.

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ARRS: Noncontrast fluoroscopy method can image chronic hypertension

By Amerigo Allegretto

A fluoroscopy method incorporating a noncontrast x-ray pulsatility index can be a screening or diagnostic test for suspected chronic hypertension.

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Dice similarity index measurements compare the x-ray pulsatility index (XPI) to digital subtraction angiography in patients with suspected chronic thromboembolic pulmonary hypertension. XPI is green, pulmonary angiography is pink and the overlap between the two is white. Image courtesy of the ARRS.

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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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