EOS reports revenue increase in Q1, net loss drop in 2020

AuntMinnie.com staff writers

Apr 20, 2021

2021 04 21 18 21 0229 Financials Growth Revenue Business2 400

Orthopedic imaging technology developer EOS Imaging reported a 48% spike in total revenues for the first quarter of 2021, while the company's net loss fell in 2020.

For the first quarter of 2021 (end-March 31), the company reported total revenues of 5.6 million euros ($6.7 million U.S.), compared with 3.8 million euros ($4.6 million) in the first quarter of 2020.

EOS sales were 23.8 million euros ($28.6 million) for the full year of 2020 (end-December 31), representing a 19% increase over sales of 20.1 million euros ($24.2 million) in 2019. EOS reported a net loss for the year of 11.2 million euros ($13.5 million), compared with a net loss of 18.4 million euros ($22.1 million) in 2019.

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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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An image illustrating the use of novel x-ray technology in crash tests.

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The tumor localization/visualization results obtained by the proposed AI algorithm. Original chest x-ray images with a lesion (red circle = radiologists’ annotation of the lesion location) and AI's lesion localization result. These examples demonstrate that the proposed technique can successfully detect humeral tumors. Image and caption courtesy of Radiology: Artificial Intelligence.

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Image of patient (upper-left) in which, according to the radiologist's report, there is only consolidation, but the algorithm detects an abnormal rib (upper-right), consolidation (lower-left), and two nodules (lower-right). It is worth noting the confusion of a consolidation with mammary tissue and of two nodules with the two mammary areolae. Image courtesy of Scientific Reports.

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