(111 days)
CINA CHEST is a radiological computer aided triage and notification software indicated for use in the analysis of Chest and Thoraco-abdominal CT angiography. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communicating suspected positive findings of (1) Chest CT angiography for Pulmonary Embolism (PE) and (2) Chest or Thoraco-abdominal CT angiography for Aortic Dissection (AD).
CINA CHEST uses an artificial intelligence algorithm to analyze images and highlight cases with detected PE and AD on a standalone Web application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected PE or AD findings. 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 CHEST are intended to be used in conjunction with other patient information and based on 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 CHEST is a radiological computer-assisted triage and notification software device.
The software system is based on algorithm-programmed components and is comprised of a standard off-the-shelf operating system and additional image processing applications.
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 a pulmonary embolism (PE) or an aortic dissection (AD), then notifications on the flagged series are sent to the Worklist Application.
The Worklist Application (on premise) displays the pop-up notifications of new studies with suspected findings when they come in, and provides both active and passive notifications. Active notifications are in the form of a small pop-up containing patient name, accession number and the type of suspected findings (PE or AD). All the chest and thoraco-abdominal CT angiography studies received by CINA CHEST device are displayed in the worklist and those on which the algorithms have detected a suspected finding (PE or AD) are marked with an icon (i.e., passive notification). In addition, a compressed, small black and white 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 primary diagnosis beyond notification. Presenting the radiologist with notification facilitates earlier triage by allowing one to prioritize images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.
Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) summary for CINA CHEST:
Acceptance Criteria and Reported Device Performance
| Parameter | Acceptance Criteria (Performance Goal) | Reported Device Performance (CINA CHEST) | Comparison to Predicate (BriefCase) |
|---|---|---|---|
| Pulmonary Embolism (PE) Detection | |||
| Sensitivity | ≥ 80% | 91.1% [95% CI: 86.1% - 94.7%] | Predicate: 90.6% [95% CI: 82.2% - 95.9%] |
| Specificity | ≥ 80% | 91.8% [95% CI: 87.1% - 95.1%] | Predicate: 89.9% [95% CI: 82.2% - 95.1%] |
| Accuracy | Not explicitly stated as a minimum goal, but reported. | 91.4% | Not explicitly stated for predicate. |
| Time-to-Notification (PE) | Not explicitly stated as a minimum/maximum goal, but comparable to predicate. | 63 ± 16.1 seconds (Mean) | |
| 60.8 seconds (Median) | |||
| [95% CI: 61.5 – 64.6] seconds | Predicate: 3.9 [95% CI: 3.7 - 4.1] minutes (234 seconds) | ||
| Aortic Dissection (AD) Detection | |||
| Sensitivity | ≥ 80% | 96.4% [95% CI: 91.7% - 98.8%] | Not applicable (Predicate is for PE/ICH, not AD) |
| Specificity | ≥ 80% | 97.5% [95% CI: 93.8% - 99.3%] | Not applicable |
| Accuracy | Not explicitly stated as a minimum goal, but reported. | 97% | Not applicable |
| Time-to-Notification (AD) | Not explicitly stated as a minimum/maximum goal, but comparable to reference. | 36.5 ± 9.1 seconds (Mean) | |
| 34.1 seconds (Median) | |||
| [95% CI: 35.4 – 37.5] seconds | Reference (CINA, ICH/LVO): 21.6 ± 4.4 sec (ICH), 34.7 ± 10.7 sec (LVO) |
Study Details
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Sample sizes used for the test set and the data provenance:
- Pulmonary Embolism (PE): 396 clinical anonymized cases.
- Aortic Dissection (AD): 298 clinical anonymized cases.
- Data Provenance: Retrospective, multicenter study. Data was provided from multiple US clinical sites (230 US cities for PE, and 200 US cities for AD).
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: "Several US-board-certified radiologist readers." The exact number is not specified beyond "several".
- Qualifications: US-board-certified radiologists. No specific years of experience are mentioned.
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Adjudication method for the test set:
- The ground truth was established by "concurrence of several US-board-certified radiologist readers." This implies a consensus-based adjudication, but the specific method (e.g., majority vote, unanimous agreement, or an independent adjudicator in case of disagreement) is not explicitly detailed.
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If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- No, an MRMC comparative effectiveness study was not reported. The study described is a standalone performance evaluation of the CINA CHEST software against a ground truth. It assesses the device's ability to identify PE and AD cases for triage, not the improvement of human readers with AI assistance.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone study was done. The document explicitly states: "Avicenna.Al conducted a retrospective, multicenter and blinded study with the CINA CHEST software with the primary endpoint to evaluate the software's performance..." and later refers to "The results of the standalone assessment study demonstrated an overall agreement (Accuracy)..." This confirms the study evaluated the algorithm's performance in isolation.
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The type of ground truth used:
- Expert Consensus. The ground truth was "established by concurrence of several US-board-certified radiologist readers."
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The sample size for the training set:
- The document does not specify the sample size for the training set. It only details the test set used for performance evaluation.
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How the ground truth for the training set was established:
- Since the training set sample size is not provided, the method for establishing its ground truth is also not detailed in this document. It is common for AI algorithms to be trained on data with ground truth established by expert radiologists or pathology, but this specific information is absent here.
§ 892.2080 Radiological computer aided triage and notification software.
(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.