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NIH grants over $1 million to development of non-contrast imaging approaches Will be used to diagnose peripheral arterial disease

China's Infervision brings AI tech to 200th hospital Now in use with approx. 20,000 lung cancer screening scans daily

Philips to manage medical imaging equipment for Aussie providers for 20 years First-of-its-kind partnership in Australia and ASEAN Pacific region

New AI system detects hard-to-see tiny tumors on lung CT scans Teaches itself how to locate tiny tumors

Fractional flow reserve CT can reduce invasive heart procedures: study First clinical study on benefits of FFRCT for moderate stenosis

Fox Chase Cancer Center gets $673K grant to develop early-stage lung cancer test Taking aim at false positive CT lung scans

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

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

by 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.

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“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.
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