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510(k) Data Aggregation
(248 days)
CuraRad-ICH is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage. CuraRad-ICH analyzes cases using deep learning algorithms to identify suspected ICH findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
CuraRad-ICH is not intended to direct attention to specific portions of an image or to anomalies other than acute ICH. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT studies.
CuraRad-ICH is software as a medical device (SaMD) that detects intracranial hemorrhage (ICH) condition by analyzing non-contrast CT images. The software needs to be integrated with a third-party worklist application to receive analysis requests and the corresponding DICOM images, and return the ICH findings (whether suspected ICH is found) to the worklist to alert the radiologists.
To help radiologists triage and prioritize reading of images for patients with ICH, CuraRad-ICH uses deep learning methods to automatically detect acute ICH in non-contrast head CT scans. The software analyzes the input image and returns a binary prediction as to whether the exam suggests the presence of acute ICH.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria & Reported Device Performance
| Metric | Acceptance Criteria (Implied) | Reported Device Performance (95% CI) |
|---|---|---|
| Sensitivity | Clinically Meaningful | 90.6% (85.9%-94.2%) |
| Specificity | Clinically Meaningful | 93.1% (88.3%-96.4%) |
| Positive Predictive Value (PPV) | Reasonable given prevalence | Varies by prevalence: |
| 1% prevalence: 11.8% (7.2%-18.8%) | ||
| 15% prevalence: 70% (57.4%-80.1%) | ||
| 54.9% prevalence (actual): 89.1% (84.3%-92.5%) | ||
| Negative Predictive Value (NPV) | Very High | Varies by prevalence: |
| 1% prevalence: 99.9% (99.8%-100%) | ||
| 15% prevalence: 98.3% (97.4%-98.8%) | ||
| 54.9% prevalence (actual): 91.8% (88.6%-94.3%) | ||
| System Processing Time | Not explicitly stated but implies quick processing | Average: 43 seconds (39-46) |
| Median: 33 seconds | ||
| Min: 16 seconds | ||
| Max: 301 seconds |
Note: The document states that the performance "met the pre-specified criteria for study success," implying that the reported values for sensitivity and specificity were the acceptance criteria.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 388 CT studies (213 positives and 175 negatives)
- Data Provenance: Retrospective, collected from 296 imaging facilities across 48 states in the US.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document states ground truth was "provided by experienced radiologists" but does not specify the number of experts or their specific qualifications (e.g., years of experience).
4. Adjudication Method for the Test Set
The document does not explicitly state the adjudication method used for establishing ground truth for the test set. It only mentions "ground truth provided by experienced radiologists."
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No, an MRMC comparative effectiveness study was not reported. The study focused on the standalone performance of the AI algorithm.
6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, a standalone performance study was done. The reported sensitivity, specificity, PPV, and NPV are for the CuraRad-ICH algorithm's performance in identifying ICH.
7. The Type of Ground Truth Used
The ground truth was established by "experienced radiologists," implying an expert consensus or expert-determined ground truth.
8. The Sample Size for the Training Set
The document does not specify the sample size used for the training set. It only states that the deep learning algorithm was "trained on non-contrast head CT scans with ICH ground truth."
9. How the Ground Truth for the Training Set Was Established
The ground truth for the training set was "provided by experienced radiologists." This implies expert labeling or consensus, similar to the test set, though no further details are given.
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