Agfa inks digital x-ray deal with Hitachi for U.S. sales

AuntMinnie.com staff writers
Apr 6, 2015

Agfa HealthCare and Hitachi Medical Systems America have entered into a sales and marketing agreement covering sales of digital x-ray systems in the U.S.

Hitachi will sell Agfa's complete portfolio of digital radiography (DR) and computed radiography (CR) devices to its MRI and CT customers in the U.S., which include hospitals, imaging centers, and orthopedic clinics, Agfa said. Agfa will provide support services for its devices to Hitachi customers, according to the firm.

The deal enables Hitachi to add digital x-ray to a product portfolio that already includes MRI, CT, and ultrasound systems.

Latest in Digital X-Ray
Stroke Skull Xray 400
Can ChatGPT ‘data mine’ patient reports for stroke registries?
April 19, 2024
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.
AI proposed to help radiologists detect child abuse injuries
April 17, 2024
Transgender Heart
Bone health appears normal in transgender youth on hormone therapy
April 5, 2024
Gavel3
Abortion care limitations raise medicolegal concerns in IR
April 3, 2024
Related Stories
2015 12 07 17 30 29 988 Hitachi Supria Ct 400
MRI
New CT scanner drives Hitachi RSNA launches
2015 03 02 12 47 30 246 3d Printer Stent 200
Digital X-Ray
SIR: 3D printer fashions tools for interventional radiology
2015 02 25 16 49 34 947 Dsa Bottom 200
Digital X-Ray
Protocol tweaks reduce radiation dose for pediatric DSA
2015 02 04 10 46 59 745 Diabetes 200
Digital X-Ray
Arab Health: 5 tips for handling diabetes foot problems
More in Digital X-Ray
Can ChatGPT ‘data mine’ patient reports for stroke registries?
By Will Morton
ChatGPT-4 appears useful for extracting details from reports on thrombectomy procedures in stroke patients, a labor-intensive and error-prone task currently performed by human readers.
April 19, 2024
Stroke Skull Xray 400
AI proposed to help radiologists detect child abuse injuries
By Will Morton
A team at Michigan State University has developed an AI model designed to detect rib fractures in children under 3 years old.
April 17, 2024
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.
Bone health appears normal in transgender youth on hormone therapy
By Will Morton
Bone mineral density is only slightly below the normal average among transgender youth undergoing gender-affirming hormone therapy.
April 5, 2024
Transgender Heart
Abortion care limitations raise medicolegal concerns in IR
By Will Morton
Interventional radiology (IR) specialists should be aware of shifting individual state laws on abortion so as to not be blindsided when called on to provide care to pregnant patients.
April 3, 2024
Gavel3
Can AI help chest DDR achieve clinical adoption?
By Will Morton
Chest dynamic digital radiography (DDR) may have received a boost toward clinical use in patients with lung disorders, with researchers developing AI to perform time-consuming analysis involved in the technology.
April 1, 2024
(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.
Powering down idle IR systems can reduce environmental impact
By Will Morton
Powering down idle interventional radiology (IR) imaging systems can substantially decrease carbon emissions and electricity costs.
March 21, 2024
Planet
Mercedes-Benz employs x-ray technology in crash tests
By Will Morton
Mercedes-Benz said it has performed the world's first crash with a real car using x-ray imaging technology.
March 21, 2024
An image illustrating the use of novel x-ray technology in crash tests.
Hysterectomies may be averted with UAE
By Will Morton
A minimally invasive treatment called uterine artery embolization (UAE) may be a promising alternative to hysterectomy in women due to severe bleeding after childbirth.
March 20, 2024
Uae
Training technique in AI helps preserve patient privacy
By Will Morton
The privacy of patient data in AI models trained on chest x-rays can be guaranteed – importantly, without significantly reducing the accuracy of models on large “real-world” data sets.
March 18, 2024
Ai Bias
AI flags rare humeral bone tumors on chest x-rays
By Will Morton
An AI algorithm can help identify rare tumors on upper arm bones in patients undergoing chest x-rays – findings not typically identified in daily reading practice.
March 13, 2024
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.
Dark-field chest x-ray takes step forward
By Will Morton
German developers of dark-field chest x-ray appear to have overcome a technical limitation of the technology – namely, adjusting for photon scattering caused by interferometers used in the experimental system.
March 12, 2024
Chest X Ray
Study calls for continuous improvement of AI algorithms
By Will Morton
An external validation study conducted in a primary care clinic in Spain has demonstrated the value – as well as important limitations – of AI software for interpreting chest x-rays in clinical practice.
March 8, 2024
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.
Page 1 of 373
Next Page