DOTmed Home MRI Oncology Ultrasound Molecular Imaging X-Ray Cardiology Health IT Business Affairs
News Home Parts & Service Operating Room CT Women's Health Proton Therapy Endoscopy HTMs Mobile Imaging
SEARCH
当前地点:
>
> This Story

starstarstarstarstar (1)
注册记数器 to rate this News Story
Forward Printable StoryPrint Comment

 

 

CT Homepage

Philips partners with Intel on CPU efficiency for medical imaging use cases Pairs Philips' OpenVINO toolkit with the Intel Xeon Scalable processors

Study: AI detects neurological issues on CT scans in under two seconds 150 times shorter than average reading time of a physician

Richardson Healthcare obtains ISO 13485:2016 certification Strengthens its status as a CT and power grid tube manufacturer

Berlin institute sets world record for fastest 3D tomographic images Produces an image every 40 milliseconds with more affordable system

MaxQ-AI seeks $8 million public IPO listing on Nasdaq Will help FDA regulatory processes for Accipio software products

New brands, proven experts lead a new era in the CT tube market The CT market has changed a lot over the last couple years

Is a CT tube crisis on the horizon? As more scanners enter the market, will tubes be sufficiently available?

Siemens and NuVasive collab to enhance spinal surgery Combines NuVasive's Pulse system with Siemens Cios Spin

NIH grants $1.5 million to Magnetic Insight for development of new imaging modality Uses non-radioactive, iron oxide nanoparticle tracer

China hits back with new tariffs affecting medical imaging equipment More than 5,000 items affected, including X-ray tubes and gamma-ray equipment

Glassbeam has expanded its technology for
detecting anomalies in components of
CT scanners such as tube temperature

Glassbeam unveils AI anomaly detection for imaging modality maintenance

John R. Fischer , Staff Reporter
Maintenance and repair for CT scanners may soon be more immediate, less frequent and more affordable following the upcoming expansion of Glassbeam Inc.’s anomaly detection technology.

The machine data analytics company elaborated on the development at the AAMI 2018 Conference and Expo in Long Beach, California, referring to it as a part of its approach for utilizing AI capabilities to detect and alert providers to changes in components of computed tomography scanners from tube temperature to waterflow. They plan to eventually include other critical imaging modalities such as MR.

Story Continues Below Advertisement

RaySafe helps you avoid unnecessary radiation

RaySafe solutions are designed to minimize the need for user interaction, bringing unprecedented simplicity & usability to the X-ray room. We're committed to establishing a radiation safety culture wherever technicians & medical staff encounter radiation.



“Instead of a human being saying that the temperature pressure has shot beyond portable range, the machine alerts you by looking up the historical data of the temperature reading and saying the temperature should be between this high range and this low range. That is the anomaly direction model,” Puneet Pandit, president and CEO of Glassbeam, told HCB News. “The machine will look at the historical data, create the threshold and then alert the engineers when the threshold is crossed.”

CT scanners are equipped with sensors for monitoring different variables such as water temperature, waterflow, air temperature, fan speed, and tube temperature. Though each sensor periodically records its readings to determine if tracked variables are in the normal range, the task of accurately identifying which sensor readings are in the normal range and which ones are not is complex, often leading many to use a rule of thumb to form manually-defined thresholds.

ML-based AD techniques use historical data to train a model that can be used for detecting anomalous sensor values.

With Glassbeam’s technology, providers can utilize machine learning-based AD techniques to predict anomalies from historical data sets and address issues earlier, saving millions in maintenance costs, as well as being able to plan out more efficiently strategic actions for the management of their imaging modalities.

In addition to detecting single abnormal readings, the technology may be used to detect combinations of these readings from two or more different sensors, further helping Glassbeam raise mean time between failures and machine uptime from the industry standard range of 96-97 percent to more than 99.5 percent.

The expansion is the second phase of an initiative launched in February in which machine learning was deployed to detect with high accuracy tube failure in CTs, seven to ten days prior to the actual occurrence of such events.
  Pages: 1 - 2 >>

CT Homepage


You Must Be Logged In To Post A Comment

做广告
提升您的品牌知名度
拍卖+私人销售
获得最好的价格
买设备/配件
找到最低价格
每日新闻
阅读最新信息
目录
浏览所有的DOTmed用户
DOTmed上的伦理
查看我们的伦理计划
金子分开供营商节目
接收PH要求
金子服务经销商节目
接收请求
提供保健服务者
查看所有的HCP(简称医疗保健提供商)的工具
工作/训练
查找/申请工作
Parts Hunter +EasyPay
获取配件报价
最近证明
查看最近通过认证的用户
最近额定
查看最近通过认证的用户
出租中央
租用设备优惠
卖设备/配件
得到最划算
服务技术员论坛
查找帮助和建议
简单的征求建议书
获取设备报价
真正商业展览
查找对设备的服务
对这个站点的通入和用途是受期限和条件我们支配 法律公告 & 保密性通知
物产和业主对 DOTmed.com,公司 Copyright ©2001-2018 DOTmed.com, Inc.
版权所有