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Imaging IT

Researchers Develop AI Algorithm to Predict Immunotherapy Response

A team of French researchers have designed an algorithm and developed it to analyze Computed Tomography (CT) scan images, establishing for the first time that artificial intelligence (AI) can process medical images to extract biological and clinical information. The researchers have created a so-called radiomic signature, which defines the level of lymphocyte infiltration of a tumor and provides a predictive score for the efficacy of immunotherapy in the patient. More...
12 Sep 2018
Image: Researchers are working to develop the application of AI in medical imaging diagnostics (Photo courtesy of Digital Health).

Fujifilm and IU School of Medicine to Study AI in Diagnostic Imaging

Fujifilm Corporation has entered into a joint research agreement with Indiana University School of Medicine to develop the application of artificial intelligence (AI) in medical imaging diagnostic support systems. More...
12 Sep 2018
Radcal
Image: The Virtual Brain Segmenter could increase the efficiency of brain scan analysis (Photo courtesy of Dominique Duncan).

VR Tool Increases Efficiency of Brain Scan Analysis

Researchers at the Mark and Mary Stevens Neuroimaging and Informatics Institute at the Keck School of Medicine of University of Southern California have designed a virtual reality (VR) tool for correcting errors in brain scan data. The tool named Virtual Brain Segmenter (VBS) transforms a tedious step in the scientific process into an immersive experience and significantly increases the efficiency of brain scan analysis. More...
06 Sep 2018

Machine Learning Shows Promise for Supporting Medical Decisions

A number of studies presented at the 67th Annual Scientific Session of the American College of Cardiology demonstrated how machine learning can be used to accurately predict clinical outcomes in patients with known or potential heart problems. The findings of these studies indicate that machine learning can usher in a new era in digital health care tools capable of enhancing health care delivery by aiding routine processes and helping physicians to assess the patients’ risk. More...
05 Apr 2018
Image: The HealthSuite Insights platform is designed to give data scientists, software developers, clinicians and healthcare providers access to advanced analytic capabilities to curate and analyze healthcare data, and offers them tools and technologies to build, maintain, deploy and scale AI-based solutions (Photo courtesy of Philips Healthcare).

Philips Launches Artificial Intelligence Platform for Healthcare

Royal Philips has launched HealthSuite Insights, including the Insights Marketplace, to support the advancing adoption of analytics and artificial intelligence (AI) in key healthcare domains. HealthSuite Insights, which made its debut at the 2018 HIMSS Conference & Exhibition (HIMSS18), gives data scientists, software developers, clinicians and healthcare providers access to advanced analytic capabilities to curate and analyze healthcare data, and offers them tools and technologies to build, maintain, deploy and scale AI-based solutions. Insights Marketplace will provide the healthcare industry's first ecosystem where curated AI assets from Philips and others are readily available for license. More...
05 Apr 2018
Image: syngo Dynamics facilitates CVIS access across the healthcare continuum (Photo courtesy of Siemens Healthineers).

Next-Generation CVIS Upgrades Communicability and Security

An updated cardiovascular imaging and Information system (CVIS) enhances communication and efficiency across the care continuum. More...
20 Mar 2018
Image: The scatterplot shows the machine learning model score compared to a random number in the independent test set (Photo courtesy of RSNA).

Researchers Train Model to Identify Breast Lesions

Researchers have trained a machine-learning tool to identify high-risk, biopsy-diagnosed breast cancer lesions that are unlikely to become cancerous, and do not require immediate surgery. More...
24 Oct 2017
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The Imaging IT Channel of MedImaging speaks the language of PACS, RIS, and DICOM, and other healthcare IT elements without which contemporary clinical imaging would be impossible.
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114 (254.86946105957): SELECT * FROM trending_articles WHERE `journal_id` = 10 AND `timestamp` <= NOW() + INTERVAL 7 DAY ORDER BY `order`
115 (!!!!! 21814.107894897): SELECT * FROM jos_deeppockets WHERE jos_deeppockets.cat = 10 AND jos_deeppockets.type = 'deeppockets_item' AND jos_deeppockets.published = 1 AND jos_deeppockets.created + INTERVAL 14 DAY >= NOW() ORDER BY jos_deeppockets.hits DESC LIMIT 7
116 (56.982040405273): ( SELECT dp.id, dp.name, dp.title, dp.video, dp.video_source, dp.created FROM jos_deeppockets AS dp WHERE dp.video != '' AND dp.video_source != '' AND dp.cat = 10 AND dp.published = 1 AND dp.type != 'deeppockets_item_link' ORDER BY created DESC LIMIT 3 ) UNION ( SELECT dpa.id, dpa.name, dpa.title, dpa.video, dpa.video_source, dpa.created FROM jos_deeppockets_archive AS dpa WHERE dpa.video != '' AND dpa.video_source != '' AND dpa.cat = 10 AND dpa.published = 1 AND dpa.type != 'deeppockets_item_link' ORDER BY created DESC LIMIT 3 ) ORDER BY created DESC LIMIT 3
117 (36.001205444336): ( SELECT dp.id, dp.name, dp.title, dp.video, dp.video_source, dp.created FROM jos_deeppockets AS dp WHERE dp.video != '' AND dp.video_source != '' AND dp.cat = 10 AND dp.published = 1 AND dp.type != 'deeppockets_item_link' ORDER BY created DESC LIMIT 3 ) UNION ( SELECT dpa.id, dpa.name, dpa.title, dpa.video, dpa.video_source, dpa.created FROM jos_deeppockets_archive AS dpa WHERE dpa.video != '' AND dpa.video_source != '' AND dpa.cat = 10 AND dpa.published = 1 AND dpa.type != 'deeppockets_item_link' ORDER BY created DESC LIMIT 3 ) ORDER BY created DESC LIMIT 3
118 (57.93571472168): 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
119 (143.05114746094): 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
120 (99.18212890625): SELECT dp.id, dp.name, dp.title, dp.video, dp.video_source, dp.created FROM jos_deeppockets AS dp WHERE dp.id = 294800359 LIMIT 1
121 (31.948089599609): SELECT dp.id, dp.name, dp.title, dp.video, dp.video_source, dp.created FROM jos_deeppockets AS dp WHERE dp.id = 294800359 LIMIT 1
122 (29.087066650391): 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
123 (30.994415283203): 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
124 (430.10711669922): SELECT name FROM jos_deeppockets WHERE id=216
125 (43.869018554688): SELECT name FROM jos_deeppockets WHERE id=216
126 (699.04327392578): 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 '((;)|(^))216((;)|($))' AND catid IN (0,110) ORDER BY publish_up ASC LIMIT 3
127 (579.1187286377): SELECT *, DATE_FORMAT(publish_up,'%d %b %Y') AS date_publish_up, DATE_FORMAT(publish_down,'%d %b %Y') AS date_publish_down FROM jos_events WHERE state=1 AND publish_up > NOW() and extra_info REGEXP '((;)|(^))216((;)|($))' AND catid IN (0,110) ORDER BY publish_up ASC LIMIT 3
128 (!!!!! 9532.2132110596): TRUNCATE TABLE jos_trademed_module
129 (!!!!! 1197.0996856689): 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
130 (499.01008605957): 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=216 or channel_id is null)
131 (40.054321289062): 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=216 or channel_id is null)
132 (32.901763916016): SELECT livesite FROM jos_dp_categories WHERE id=10 AND published='1' LIMIT 1
133 (42.915344238281): select jos_twitters.link from jos_twitters where cat=10
134 (25.98762512207): select jos_facebook.link from jos_facebook where cat=10
135 (25.98762512207): select jos_google_plus.link from jos_google_plus where cat=10
136 (25.033950805664): select jos_linkedin.link from jos_linkedin where cat=10
137 (228.8818359375): update jos_bannersmanager_banner set impmade=impmade+1 where id in(14615,14202,14203)
138 (!!!!! 1152.0385742188): insert into jos_banners_delta values (14615,'2025-04-29 16:41:34','3.21.125.27','view'),(14202,'2025-04-29 16:41:34','3.21.125.27','view'),(14203,'2025-04-29 16:41:34','3.21.125.27','view')