(94 days)
Rapid 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 segments and analyzes ASPECTS Regions of Interest (ROIs). Rapid ASPECTS extracts image data for the ROI(s) to provide analysis and computer analytics based on morphological characteristics. The imaging features are then synthesized by an artificial intelligence algorithm into a single ASPECT (Alberta Stroke Program Early CT) Score. Rapid 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. This device provides information that may be useful in the characterization of early ischemic brain tissue injury during image interpretation (within 6 hours). Rapid ASPECTS provides a comparative analysis to the ASPECTS standard of care radiologist assessment using the ASPECTS atlas definitions and atlas display including highlighted ROIs and numerical scoring.
Rapid ASPECTS provides an automatic ASPECT score based on the case input file for the physician. The score includes which ASPECT 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 other clinical factors the clinician may integrate though the Rapid Platform User Interface.
The ASPECTS software module processing pipeline performs four major tasks:
- Orientation and spatial normalization of the input imaging data (rigid registration/alignment with anatomical template);
- Delineation of pre-defined regions of interest on the normalized input data and computing numerical values characterizing underlying voxel values within those regions;
- Identification and highlighting previous/old stroke areas along with areas of early ischemic change; and
- Labeling of these delineated regions and providing a summary score reflecting the number of regions with early ischemic change as per ASPECTS guidelines.
Subsequently, the system notifies the physician of the ASPECT score which then requires the confirmation by the physician that a Large Vessel Occlusion (LVO) is detected. The ASPECTS information is then available for the physician to review and edit prior to pushing the data to a PACS or Workstation. The final summary score together with the regions selected and underlying voxel values are then sent to the Picture Archiving and Communication System (PACS) to become a part of the permanent patient medical record.
Here's a summary of the acceptance criteria and the study details for the Rapid ASPECTS device, based on the provided document:
1. Table of Acceptance Criteria and Reported Device Performance
The FDA clearance document does not explicitly state pre-defined acceptance criteria in terms of specific performance metrics (e.g., minimum accuracy, sensitivity, specificity thresholds). Instead, the performance is demonstrated through a comparative effectiveness study showing improvement in human reader agreement.
| Acceptance Criteria Category | Specific Criteria (Implicitly from study goals) | Reported Device Performance (as stated in document) |
|---|---|---|
| Clinical Efficacy | Improvement in agreement with expert consensus read for ASPECTS scoring. | Readers (neurologists, radiologists, emergency medicine, neurocritical care specialists) significantly increased their agreement with an expert consensus read when using Rapid ASPECTS (P |
§ 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).