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Industry News

Image: DeepRay creates clear, undistorted views of the real world from a damaged or obscured moving image (Photo courtesy of Cambridge Consultants).

New AI Software Reconstructs Images in Real-Time

Scientists at Cambridge Consultants have developed an Artificial Intelligence (AI) technology that creates clear, undistorted views of the real world from a damaged or obscured moving image. Based on recent advances in deep learning, the solution named DeepRay has the power to see clearly in difficult, unpredictable situations and could transform numerous machine vision and imaging applications, from autonomous driving to empowering healthcare professionals with more accurate medical imaging. More...
25 Dec 2018
Image: ProFound AI is a deep-learning cancer detection and workflow solution for DBT (Photo courtesy of iCAD).

Cancer Detection Technology Built on AI Receives FDA Clearance

A new deep-learning cancer detection software solution for digital breast tomosynthesis (DBT) has received clearance from the US FDA for commercial sales and clinical use in US. The solution named ProFound AI is built on artificial intelligence (AI) and has been developed by iCAD, Inc, a provider of cancer detection and therapy solutions. More...
25 Dec 2018
Radcal
Image: The AI-CT Stroke Screening System is designed to help radiologists detect and diagnose stroke faster than ever (Photo courtesy of Infervision).

New AI Platform Helps Radiologists Diagnose Stroke Faster Using CT Scans

A new artificial intelligence- (AI) driven stroke detection solution could help radiologists detect and diagnose stroke faster than ever, resulting in patients receiving lifesaving treatment when time is of the essence. More...
18 Dec 2018
Image: The MDR-AI Incubator is designed to stimulate innovation and product development in radiology (Photo courtesy of MEDNAX).

Launch of AI Incubator to Stimulate Innovation and Drive Adoption

MEDNAX, Inc, and MEDNAX Radiology Solutions has launched the MEDNAX Radiology Solutions Artificial Intelligence (MDR-AI) Incubator, which brings together radiologists, a rich clinical dataset, and a select group of technology partners to stimulate innovation and product development in radiology, thereby improving radiologist’s accuracy and efficiency and quality of patient care. More...
18 Dec 2018
Image: NYU School of Medicine Department of Radiology chair, Michael Recht, MD; Daniel Sodickson, MD, PhD, vice chair for research and director of the Center for Advanced Imaging Innovation and Research; and Yvonne Lui, MD, director of artificial intelligence, watch an MRI exam take place with at NYU Langone Health in New York in August 2018.

Largest-Ever Open-Source Dataset Released to Speed Up MRIs Using AI

The NYU School of Medicine’s Department of Radiology is releasing the first large-scale MRI dataset of its kind as part of fastMRI, a collaborative effort with Facebook AI Research to speed up MRI scans with artificial intelligence (AI). More...
18 Dec 2018
Image: Example of full-dose, 10 percent low-dose and algorithm-enhanced low-dose (Photo courtesy of RSNA).

Artificial Intelligence Could Help Reduce Gadolinium Dose in MRI

Researchers from Stanford University are using artificial intelligence (AI) to reduce the dose of a contrast agent that may be left behind in the body after MRI exams. More...
18 Dec 2018
Image: New research presented at RSNA addressed the prevention of medical imaging cyberattacks (Photo courtesy of RSNA).

Deep Learning Model Designed to Prevent Medical Imaging Cyberattacks

Researchers presented two new studies at the recent annual meeting of the Radiological Society of North America (RSNA) that addressed the potential risk of cyberattacks in medical imaging. More...
18 Dec 2018
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MedImaging Industry News channel updates the reader on mergers and takeovers, on cooperative agreements and on trends in the healthcare imaging industry worldwide.
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86 (25.033950805664): SELECT show_live FROM jos_com_show_setting LIMIT 1
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95 (66.041946411133): SELECT dp.id, dp.name, dp.title, dp.video, dp.video_source, dp.created FROM jos_deeppockets AS dp WHERE dp.id = 294800513 LIMIT 1
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99 (51.021575927734): SELECT dp.id, dp.name, dp.title, dp.video, dp.video_source, dp.created FROM jos_deeppockets AS dp WHERE dp.id = 294800361 LIMIT 1
100 (43.869018554688): SELECT dp.id, dp.name, dp.title, dp.video, dp.video_source, dp.created FROM jos_deeppockets AS dp WHERE dp.id = 294800361 LIMIT 1
101 (70.810317993164): SELECT name FROM jos_deeppockets WHERE id=217
102 (40.054321289062): SELECT name FROM jos_deeppockets WHERE id=217
103 (748.1575012207): SELECT *, DATE_FORMAT(publish_up,'%d %b %Y') AS date_publish_up, DATE_FORMAT(publish_down,'%d %b %Y') AS date_publish_down FROM jos_events WHERE state=1 AND publish_up > NOW() and extra_info REGEXP '((;)|(^))217((;)|($))' AND catid IN (0,110) ORDER BY publish_up ASC LIMIT 3
104 (586.03286743164): SELECT *, DATE_FORMAT(publish_up,'%d %b %Y') AS date_publish_up, DATE_FORMAT(publish_down,'%d %b %Y') AS date_publish_down FROM jos_events WHERE state=1 AND publish_up > NOW() and extra_info REGEXP '((;)|(^))217((;)|($))' AND catid IN (0,110) ORDER BY publish_up ASC LIMIT 3
105 (!!!!! 9616.1365509033): TRUNCATE TABLE jos_trademed_module
106 (!!!!! 1505.8517456055): SELECT jos_item.* FROM jos_item INNER JOIN jos_number ON jos_item.numberID=jos_number.id WHERE 1=1 AND (jos_item.imgMini<>'') AND jos_item.type='L' AND jos_item.numberID= ( SELECT jos_number.id FROM jos_number WHERE jos_number.journalID IN (10) and jos_number.id in (select distinct jos_number.id from jos_number join jos_item on jos_item.numberid=jos_number.id where jos_item.type='P') ORDER BY (CONVERT(SUBSTRING(issue,1,2),SIGNED) + (CONVERT(SUBSTRING(issue,3,2),SIGNED)*100)) DESC LIMIT 0,1 ) ORDER BY RAND() DESC LIMIT 6
107 (73.909759521484): select distinct links.* from jos_textlinks links join jos_textlinks_placement placement on links.id=placement.link_id where journal_id=(10) and (channel_id=217 or channel_id is null)
108 (43.869018554688): select distinct links.* from jos_textlinks links join jos_textlinks_placement placement on links.id=placement.link_id where journal_id=(10) and (channel_id=217 or channel_id is null)
109 (43.153762817383): SELECT livesite FROM jos_dp_categories WHERE id=10 AND published='1' LIMIT 1
110 (42.915344238281): select jos_twitters.link from jos_twitters where cat=10
111 (70.095062255859): select jos_facebook.link from jos_facebook where cat=10
112 (36.001205444336): select jos_google_plus.link from jos_google_plus where cat=10
113 (36.001205444336): select jos_linkedin.link from jos_linkedin where cat=10
114 (368.11828613281): update jos_bannersmanager_banner set impmade=impmade+1 where id in(14615,14202,14203)
115 (!!!!! 1302.95753479): insert into jos_banners_delta values (14615,'2025-05-02 11:43:42','18.118.147.65','view'),(14202,'2025-05-02 11:43:42','18.118.147.65','view'),(14203,'2025-05-02 11:43:42','18.118.147.65','view')