Search Results

CINA-iPE is a radiological computer-aided triage and notification software indicated for use in patients undergoing contrast-enhanced CT scans (not dedicated CTPA protocol) for other clinical indications than pulmonary embolism suspicion, including at least a part of the lung. The device is intended to assist hospital networks and appropriately trained radiologists in workflow triage by flagging and communicating suspected positive findings for incidental Pulmonary Embolism (iPE). The device is indicated for adults and transitional adolescents (18 to 21 years old but treated as adults).

CINA-iPE uses an artificial intelligence algorithm to analyze images and highlight cases with detected incidental PE on a standalone application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected incidental PE findings. The device is not designed to detect PE in subsegmental arteries.

Notifications include compressed preview images that are meant for informational purposes only and are not intended for diagnostic use beyond notification. The device does not alter the original medical image, and it is not intended to be used as a diagnostic device.

The results of CINA-iPE are intended to be used in conjunction with other patient information and based on their professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are ultimately responsible for reviewing full images per the standard of care.

CINA-iPE is a radiological computer aided triage and notification software device.

CINA-iPE runs on a standard "off the shelf" server/workstation and consists of an Image Processing Application, which can be integrated, deployed, and used with the CINA Platform (cleared under K200855) or other medical image communications devices. CINA-iPE receives contrast-enhanced CT scans (not dedicated CTPA protocol) including at least a part of the lung identified by the CINA Platform or other medical image communications device, processes them using deep learning algorithms involving the execution of multiple computational steps to identify the suspected presence of an incidental pulmonary embolism and generates results files to be transferred by CINA Platform or a similar medical image communications device for output to a PACS system or worklist prioritization.

To identify the suspected presence of pulmonary embolisms, the device uses a deep learning model trained end-to-end on 5.429 cases acquired from US and France, representing a distribution of PE sizes, locations and acquisition protocols, including multiple scanner models from Siemens, Philips, GE and Canon/Toshiba. Additional models are used to locate the aorta and main pulmonary artery, enabling assessment of the contrast timing. The lung's parenchyma is segmented to evaluate both the presence of the lungs in the field of view and to limit the region of interest for detecting the presence of pulmonary embolisms.

DICOM images are received, recorded and filtered before processing. The series are processed chronologically by running algorithms on each series to detect suspected positive findings of an incidental Pulmonary Embolism (iPE), then active notifications on the flagged series are sent to the Worklist Application.

The Worklist Application displays the active notification of new studies with suspected findings when they come in. All the contrast-enhanced CT studies received by CINA-iPE device are displayed in the worklist and those on which the algorithms have detected finding are marked with an icon (i.e., passive notification). In addition, a compressed, grayscale, unannotated image that is marked "not for diagnostic use" is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for diagnostic use beyond notification.

Presenting the radiologist with notification facilitates earlier triage by allowing prioritization of images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care image interpretation practice alone.

The CINA platform is an example of medical image communications platform for integrating and deploying the CINA-iPE image processing application. The medical image communications device (i.e., the technical platform) provides the necessary requirements for interoperability based on the standardized DICOM protocol and services to communicate with existing systems in the hospital radiology department such as CT modalities or other DICOM nodes (DICOM router or PACS for example). It is responsible for transferring, storing, converting formats, notifying of suspected findings and displaying medical device data such as radiological data. The medical image communications server includes the Worklist client application in which notifications from the CINA-iPE Image Processing application are received.

The provided text describes the acceptance criteria and the study conducted to prove that the CINA-iPE device meets these criteria.

Here's an organized breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The primary acceptance criteria for the CINA-iPE device were its Sensitivity and Specificity in identifying incidental Pulmonary Embolism (iPE), measured against a performance goal of 80%.

Additional Performance Data (Sub-group Analysis):

Time-to-Notification:

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: 381 clinical anonymized cases.
• Data Provenance: Retrospective, multinational study.
  • Country of Origin: Data was acquired from multiple U.S. and OUS (Outside US) clinical sites. Specifically, 56.4% (215) of cases came from U.S. clinical sources.
  • Retrospective/Prospective: Retrospective.
  • Independence: The independence of the standalone validation dataset from the training data was ensured using data from independent sites and different time periods.
  • Patient Demographics: 53.5% Male and 46.7% Female. Mean ± SD age: 64.5 ± 15.8 years (range: 18 - 99 years).
  • Scanner Diversity: Acquired primarily by 4 different scanner makers (GE-31.5%, Philips-28.3%, Siemens-26%, and Canon-13.9%) and 39 different scanner models.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: Three (3)
• Qualifications of Experts: US-board-certified expert radiologists.

4. Adjudication Method for the Test Set

The ground truth was established by consensus of the three US-board-certified expert radiologists. While the specific mechanism of reaching consensus (e.g., 2 majority, discussion, etc.) is not detailed, the term "consensus" implies agreement among the experts.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? The provided text does not explicitly state that a multi-reader multi-case (MRMC) comparative effectiveness study was done to evaluate how human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the algorithm and its time-to-notification effectiveness for triage/prioritization.
• Effect Size of Human Improvement: Not applicable, as an MRMC comparative effectiveness study was not described.

6. Standalone (Algorithm Only) Performance Study

• Was a standalone study done? Yes, a standalone performance testing study was conducted.
• Details: The study evaluated the CINA-iPE application's performance in identifying incidental pulmonary embolisms (iPE) on contrast-enhanced CT images. The primary endpoint was the device's Sensitivity and Specificity.

7. Type of Ground Truth Used

• Ground Truth Type: Expert Consensus. The ground truth was established by the consensus of three US-board-certified expert radiologists.

8. Sample Size for the Training Set

• Sample Size for Training Set: 5,429 cases.

9. How the Ground Truth for the Training Set Was Established

• Method: The deep learning model was "trained end-to-end on 5.429 cases acquired from US and France, representing a distribution of PE sizes, locations and acquisition protocols." The precise method for establishing ground truth for training is not explicitly detailed but it's implied that these cases were labeled with PE sizes and locations, likely through expert review similar to the test set, but this is not explicitly stated.

Ask a specific question about this device