Easy Installation

On site installation, once approved for deployment by hospital IT, entails setting up a virtual machine according to ClearRead specifications. Once ClearRead software is installed and deployed on the hospital network based on a VM or physical server, DICOM data is received and selectively processed, according to site established protocols and routed to the PACs archive. Image content can be routed directly from the acquisition device through a parallel push process or routed from the PACS to the ClearRead server.

ClearRead content is read on the institutions existing PACS review station where all standard PACS review station image manipulation tools are available for ClearRead content manipulation.

Installation and configuration of ClearRead software typically take as little as four hours. The simplicity of the ClearRead installation on site is based on Riverain’s image normalization technology that eliminates the need for tuning to specific manufacturers or imaging protocols. Utilization of the existing PACS review station for image interpretation also greatly simplifies deployment at specific sites.

To maintain throughput, ClearRead incorporates highly efficient computational models based on state of the art low level math libraries. Advanced image normalization tools provide the capability to rapidly scale across different acquisition devices, on site or remote. Although high volume centers throughput is typically handled adequately by a single four core, intel processing unit based on a built in queuing and prioritization process, further scalability is easily achieved. For highly demanding throughput with high case volume, ClearRead has a built-in load balancing software capable of leveraging multiple virtual machines (VMs) as necessary. All routing, processing and delivery is automated to ensure ClearRead content is available to clinician without any reading delay.

Workflow

ClearRead software can be installed on a stand-alone server, delivered by Riverain, or installed on a virtual machine (VM) using institutional server hardware. Once installed and deployed on the hospital network, DICOM data is received and selectively processed, according to site established protocols, and routed to the existing PACs archive. The ClearRead content is read on the institutions existing PACS review station where all standard PACS review station image manipulation tools are available for ClearRead content. Content can be routed directly from the acquisition device through a parallel push process or routed from the PACS to the ClearRead server once the native content is received at the PACS.

To maintain throughput, ClearRead incorporates highly efficient computational models based on state of the art low level math libraries. Advanced image normalization tools provide the capability to rapidlyscale across different acquisition devices, on site or remote. Although high volume centers throughput is typically handled adequately by a single four core, intel processing unit based on a built in queuing and prioritization process, further scalability is easily achieved. For highly demanding throughput with high case volume, ClearRead has a built-in load balancing software capable of leveraging multiple virtual machines (VMs) as necessary.All routing, processing and delivery is automated to ensure ClearRead content is available to clinician without any reading delay.

ClearRead content is read on institutions existing PACS review station, eliminating the need for costly dedicated workstations and disjoint workflows. Desired reading, hanging protocols are easily realized based on flexible tools.

Duke University Medical Center

Adoption of Inventive, Innovative, ClearRead CT Technology to Provide Patients with the Best Possible Care

Duke University Medical Center is a leading academic institution in thoracic imaging. As one of the first facilities in the country to institute a Lung Cancer Screening Program, Duke University Medical Center is looked to as a thought leader and reference site for institutions initiating similar programs. As an American College of Radiology (ACR) designated center for Lung Cancer Screening, Duke’s Lung Cancer Screening program is recognized for providing safe, effective care for at risk lung cancer patients, while maintaining the highest possible standards.

The shift toward the implementation of lung cancer screening programs began in 2011 with the release of the National Lung Cancer Screening Trial (NLST) results. The study concluded that annual screening with low-dose computed tomography (CT) could detect lung cancer in its earliest stages, reducing lung cancer deaths by 20 percent.

Duke University Medical Center prides itself on providing its patients with the highest quality of care through state of the art technology, programs and the implementation of ground breaking technologies such as Riverain’s ClearRead CT, an aid for detecting lung nodules more efficiently and accurately.

Extending Early Detection Beyond Lung Cancer Screening
According to Dr. Jared Christensen, Division Chief of Cardiothoracic Imaging and Director of Duke’s Lung Cancer Screening Program, one of their biggest challenges in thoracic imaging is searching for lung nodules. For every chest CT exam that is taken, radiologists are obligated to search for lung nodules. Due to the lack of symptoms in early stage lung cancer, the detection of incidental pulmonary nodules serves as a critically important opportunity for early lung cancer detection. In an effort to help their radiologists detect more, more efficiently, Duke University Medical Center has deployed Riverain’s ClearRead CT software throughout their entire health network across all chest CTs, whether taken for screening or not.

“Riverain’s ClearRead CT has been deployed as a part of our routine Chest CT exams, including patients in our Lung Cancer Screening Program,” said Dr. Christensen. “The ClearRead CT technology has helped us to detect lung nodules that may have otherwise been missed. Based upon our early experience, the workflow is faster and more accurate than existing technologies.”

Improved Detection, Decreased Reading Time, Standardized Patient Care
ClearRead CT is the only FDA cleared device to support concurrent reading, allowing for faster reading with proven superior automatic nodule detection performance for all primary nodule types, including: solid, subsolid and ground glass nodules. With the use of ClearRead CT, facilities like Duke are able to achieve a 26% reduction in reading time while improving nodule detection by 29%.

Duke University Medical center has deployed Riverain’s ClearRead CT software throughout their entire healthcare network, providing a standard of care to their entire patient population. The ClearRead technology seamlessly processes CT scans from all 15 of Duke’s CT scanners regardless of manufacturer or acquisition protocols.

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Einstein Medical Center

ClearRead CT Adopted Network Wide to Improve Patient Care

Einstein Medical Center in Philadelphia is a leading academic institution for thoracic imaging. As a leading institution serving it’s Philadelphia patient population, Einstein has a history of quickly adopted innovative technology to improve patient case. Einstein Medical Center was the first U.S. site to adopt ClearRead CT enterprise wide. Einstein Medical Center is an American College of Radiology (ACR) designated center for Lung Cancer Screening, recognizing Einstein’s Lung Cancer Screening program for providing safe, effective care for at risk lung cancer patients.

Einstein Medical Center quickly adopted ClearRead CT and broadly deployed the technology to not only patients participating in Lung Cancer Screening to have the broadest possible patient impact of early detection of lung cancer screening as many lung cancers are detected incidentally on chest CTs performed for reasons outside lung cancer screening.

The ClearRead technology seamlessly processes CT scans from all 13 of the Einstein Medical Centers CT scanners regardless of manufacturer or acquisition protocols.

According to Dr. Terrence Matalon, Chairman of Einstein Medical Center Philidelphia’s Department of Radiology, “The application enables identification of small nodules faster and more reliably, particularly for oncological patients.”

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