(158 days)
StrokeSENS ASPECTS is a computer-aided diagnosis (CADx) software device used to assist the clinician in the assessment and characterization of brain tissue abnormalities using CT image data.
The Software automatically registers images and uses an Atlas to segment and analyze ASPECTS Regions. StrokeSENS ASPECTS extracts image data from individual voxels in the image to provide analysis and computer analytics and relates the analysis to the atlas defined ASPECTS regions. The imaging features are then synthesized by an artificial intelligence algorithm into a single ASPECT (Alberta Stroke Program Early CT) Score.
StrokeSENS ASPECTS is indicated for evaluation of patients presenting for diagnostic imaging workup with known MCA or ICA occlusion, for evaluation of extent of disease. Extent of disease refers to the number of ASPECTS regions affected which is reflected in the total score. StrokeSENS ASPECTS provides information that may be useful in the characterization of ischemic brain tissue injury during image interpretation (within 12 hours from time last known well).
StrokeSENS ASPECTS provides a comparative analysis to the ASPECTS standard of care radiologist assessment by providing highlighted ASPECTS regions and an automated editable ASPECTS score for clinician review. StrokeSENS ASPECTS presents the original and annotated images for concurrent reads. StrokeSENS ASPECTS additionally provides a visualization of the voxels contributing to the automated ASPECTS score.
Limitations:
- StrokeSENS ASPECTS is not intended for primary interpretation of CT images. It is used to assist physician evaluation.
- StrokeSENS ASPECTS has been validated in patients with known MCA or ICA occlusion prior to ASPECTS scoring.
- Use of StrokeSENS ASPECTS in clinical settings other than brain ischemia within 12 hours from time last known well, caused by known ICA or MCA occlusions, has not been tested.
- StrokeSENS ASPECTS has only been validated and is intended to be used in patient populations aged over 21.
Contraindications:
- StrokeSENS ASPECTS is contraindicated for use on brain scans displaying neurological pathologies other than acute ischemic stroke, such as tumors or abscesses, hemorrhagic transformation, and hematoma.
Cautions:
- Patient Motion: Excessive patient motion leading to artifacts that make the scan technically inadequate.
StrokeSENS ASPECTS is a stand-alone software device that uses machine learning algorithms to automatically process NCCT (non-contrast computed tomography) brain image data to provide an output ASPECTS score based on the Alberta Stroke Program Early CT Score (ASPECTS) guidelines.
The post-processing image results and ASPECTS score are identified based on regional imaging features and overlayed onto brain scan images. StrokeSENS ASPECTS provides an automated ASPECTS score based on the input CT data for the physician. The score includes which ASPECTS regions are identified based on regional imaging features derived from non-contrast computed tomography (NCCT) brain image data. The results are generated based on the Alberta Stroke Program Early CT Score (ASPECTS) guidelines and provided to the clinician for review and verification. At the discretion of the clinician, the scores may be adjusted based on the clinician's judgement.
StrokeSENS ASPECTS can connect with other DICOM-compliant devices, to transfer NCCT scans for software processing.
Results and images can be sent to a PACS via DICOM transfer and can be viewed on a PACS workstation or via the StrokeSENS UI or other DICOM-compatible radiological viewer.
StrokeSENS ASPECTS provides an automated workflow which will automatically process image data received by the system in accordance with pre-configured user DICOM routing preferences.
StrokeSENS ASPECTS principal workflow for NCCT includes the following key steps:
- Receive NCCT DICOM Image
- Automated image analysis and processing to identify and visualize the voxels which have been included in the ASPECTS score (Also referred to as a 'heat map' or 'VCTA; Voxels Contributing to ASPECTS Score').
- Automated image analysis and processing to register the subject image to an atlas to segment and highlight ASPECTS regions and to display whether or not each region is qualified as contributing to the ASPECTS score.
- Generation of auto-generated results for review and analysis by users.
- Generation of verified/modified result summary for archiving, once the user verifies or modifies the results.
Once the auto-generated ASPECTS score results are available, the physician is asked to confirm that the case in question is for an ICA or MCA occlusion and is able to modify/verify the ASPECTS regional score. The ASPECTS auto-generated results, including the ASPECTS score, indication of affected side, affected ASPECTS regions and voxel-wise analysis (shown as a heatmap of voxels 'contributing to ASPECTS score'), along with the user-verified/modified result summary can be sent to the Picture Archiving and Communications System (PACS).
Here's an analysis of the acceptance criteria and study that proves the device meets those criteria, based on the provided FDA 510(k) Clearance Letter.
Acceptance Criteria and Device Performance
The provided text details two primary performance studies: Standalone Performance and Clinical Validation (MRMC study), along with a Clinical Validation of Voxels Contributing to ASPECTS (VCTA). The acceptance criteria are implicitly derived from the reported performance benchmarks for these studies.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Implicit) | Reported Device Performance (Standalone Study) | Reported Device Performance (MRMC Clinical Validation) | Reported Device Performance (VCTA Clinical Validation) |
|---|---|---|---|
| Standalone Performance: | |||
| AUC-ROC for region-level Clustered ROC Analysis | 90.9% (95% CI = [88.7%, 93.1%]) | N/A (Standalone study only) | N/A (Standalone study only) |
| Accuracy | 90.6% [89.7%, 91.5%] | N/A (Standalone study only) | N/A (Standalone study only) |
| Sensitivity | 70.6% [69.2%, 72.1%] | N/A (Standalone study only) | N/A (Standalone study only) |
| Specificity | 93.9% [93.2%, 94.7%] | N/A (Standalone study only) | N/A (Standalone study only) |
| Clinical Validation (Reader Improvement - MRMC): | |||
| Statistically significant improvement in reader AUC with AI assistance vs. without AI assistance | N/A (MRMC study only) | Statistically significant improvement of 5.7% from 68.6% (unaided) to 74.3% (aided) (p-value |
§ 892.2060 Radiological computer-assisted diagnostic software for lesions suspicious of cancer.
(a)
Identification. A radiological computer-assisted diagnostic software for lesions suspicious of cancer is an image processing prescription device intended to aid in the characterization of lesions as suspicious for cancer identified on acquired medical images such as magnetic resonance, mammography, radiography, or computed tomography. The device characterizes lesions based on features or information extracted from the images and provides information about the lesion(s) to the user. Diagnostic and patient management decisions are made by the clinical user.(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 image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended.
(iii) Results from performance testing protocols that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Standalone 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; and description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity).(2) Labeling must include:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the intended reading protocol.
(iii) A detailed description of the intended user and recommended user training.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations, including situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) Detailed instructions for use.
(viii) A detailed summary of the performance testing, including: Test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders (
e.g., lesion and organ characteristics, disease stages, and imaging equipment).