iCRco

Kate Madden Yee

Oct 22, 2007

2007 10 22 11 35 39 706

(Booth 4754) Computed radiography (CR) vendor iCRco of Torrance, CA, plans to use this year's RSNA meeting to debut its work-in-progress iDR unit, a sealed-scanning system based on storage phosphor technology.

The system delivers fully processed images to a review station in 30 seconds, and can be configured to wirelessly transmit an image over an internal network or Internet connection. iDR offers 50-micron resolution, 508 dpi, and 61 megapixels.

Pending clearance from the U.S. Food and Drug Administration, iCRco hopes to begin shipments of iDR in November of this year. The company hopes to package iDR with an OEM vendor's x-ray system.

2024 04 30 Scoliosis Ai Thumbnail

AI brings scoliosis monitoring on x-rays into modern era

May 1, 2024

Risk of knee osteoarthritis may depend on muscle mass

April 30, 2024

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

AI brings scoliosis monitoring on x-rays into modern era

An AI model has delivered a long overdue update of pediatric bone growth predictions used in x-ray imaging to monitor scoliosis.

May 1, 2024

2024 04 30 Scoliosis Ai Thumbnail

Risk of knee osteoarthritis may depend on muscle mass

Physical activities like running may increase the risk of osteoarthritis in individuals with lower muscle mass surrounding the knee joints.

April 30, 2024

Can ChatGPT ‘data mine’ patient reports for stroke registries?

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

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

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

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

Can AI help chest DDR achieve clinical adoption?

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

Powering down idle interventional radiology (IR) imaging systems can substantially decrease carbon emissions and electricity costs.

March 21, 2024

Mercedes-Benz employs x-ray technology in crash tests

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

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

Training technique in AI helps preserve patient privacy

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 flags rare humeral bone tumors on chest x-rays

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.

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