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FDA okays Philips' MR-only radiotherapy simulator, MRCAT pelvis Create treatment plans for bladder, rectal, anal and cervical cancer

Study calls for better factoring-in of patient complexity in head CT scans Should consider complexity of associated billed patient encounters

Study shows 30 percent drop in unnecessary head CTs with BrainScope One May help ensure appropriate use of imaging

The evolution of the medical physicist Part one in a series by Thomas J. Petrone

MR method could spare patients with skull lesions from CT, says study Could benefit children and pregnant women

FDA greenlights Samsung S-Vue 3.02 dose reduction solution Reduces X-ray dose exposure for pediatric patients

DR now makes up over 80 percent of US general radiography install base Up from only half in 2015

FDA clears GE’s AI-based CT image reconstruction technology Available as upgrade to Revolution Apex scanner

Philips Medical Systems sues ex-employee over alleged secrets theft Suit claims X-ray tube trade secrets were stolen before erasing hard drive

The upper extremity value of mini C-arms in the ER and OR Insights from Dr. Korsh Jafarnia

Zebra Medical Vision unveils deep learning research for automated chest X-ray

John R. Fischer , Staff Reporter

The unveiling of the Israeli enterprise’s research follows the recent CE markings for its seventh AI imaging algorithm, designed to detect suspected malignant lesions in mammograms, and its bleed detection algorithm.

In addition to the unveiling of its research, Zebra Medical Vision also announced its C round funding of $30 million, bring its total investment as a company to $50 million. AMoon Ventures led the investment, which included Aurum, Johnson & Johnson Innovation JJDC Inc., Intermountain Healthcare, and leading global AI scientists, professor Fei Fei Lee and Richard Socher.
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The company is currently exploring a similar technique for AP chest X-ray scans, and musculoskeletal and abdominal radiographies.

The findings are available on the arXiv.org e-Print archive and will be presented at the 21st International Conference on Medical Image Computing and Computer Assisted Intervention this September in Granada, Spain.

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