(67 days)
EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM (EFAI AASCTA) is a radiological computer aided triage and notification software indicated for use in the analysis of chest-abdomen CTA in adults aged 22 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of aortic dissection (AD) or aortic intramural hematoma (IMH) pathology.
EFAI AASCTA uses an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI AASCTA is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decisionmaking nor is it intended to rule out AAS or otherwise preclude clinical assessment of computed tomography cases.
EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM (EFAI AASCTA) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze chest or chest-abdomen CTA and alerts the PACS/RIS workstation once images with features suggestive of AD or IMH are identified.
Through the use of EFAI AASCTA, a radiologist is able to review studies with features suggestive of AD or IMH earlier than in standard of care workflow.
The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original chest or chest-abdomen CTA. The device aims to aid in prioritization and triage of radiological medical images only.
Here's a breakdown of the acceptance criteria and study details for the EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM, based on the provided document:
Acceptance Criteria and Device Performance
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Metric | Acceptance Criteria (Lower Bound of 95% CI) | Reported Device Performance (95% CI) |
|---|---|---|
| Sensitivity | > 0.8 | 0.929 (0.878 - 0.960) |
| Specificity | > 0.8 | 0.915 (0.871 - 0.945) |
| Processing Time | Not explicitly stated as an AC | 37.86 seconds (35.22 - 40.50) |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: 380 CTA studies (156 positive cases, 224 negative cases).
- Data Provenance: Retrospective, multisite clinical validation study. The data was collected in the United States. None of the studies in the test set were used for model development or analytical validation. The study population included 51.58% females and 48.42% males, with a mean age of 62.90 years. CT scanner manufacturers included Philips, Toshiba, Siemens, GE, and others.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- Number of Experts: Three.
- Qualifications of Experts: U.S. board-certified radiologists.
4. Adjudication Method for the Test Set
- Adjudication Method: Majority agreement between the three experts. (Described as "the reference standard (ground truth) was generated by the majority agreement between the three experts.")
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not reported. The study focused on the standalone performance of the AI algorithm.
6. Standalone Performance Study
- Yes, a standalone performance study was conducted. The results reported (sensitivity and specificity) are for the EFAI AASCTA by itself, "in the absence of any interaction with a clinician."
7. Type of Ground Truth Used
- Ground Truth Type: Expert consensus. Specifically, the "majority agreement between the three experts" (U.S. board-certified radiologists) determined the presence of AD or IMH for each case.
8. Sample Size for the Training Set
- The document does not explicitly state the sample size for the training set. It only mentions that none of the 380 studies in the validation test set were used for model development (training) or analytical validation.
9. How the Ground Truth for the Training Set Was Established
- The document does not explicitly state how the ground truth for the training set was established. It only discusses the ground truth establishment for the test set.
§ 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.