(25 days)
ClariCT.AI is a software device intended for networking, communication, processing and enhancement of CT images in DICOM format regardless of the manufacturer of CT scanner or model.
ClariCT.AI software is intended for denoise processing and enhancement of CT DICOM images when higher image quality and/or lower dose acquisitions are desired. ClariCT.Al software can be used to reduce noises in CT images of the head, chest, and abdomen, in particular in CT images with a lower radiation dose. ClariCT.Al may also improve the image quality of low-dose nondiagnostic Filtered Back Projection images as well as Iterative Reconstruction images. The subject device, ClariCT.Al, added a new module (named Al Marketplace Integration module) to the original cleared device (K183460) to enable installation on the Al Marketplace system. The module integrates the Denoising Processor of the original device into the Al Marketplace system. So ClariCT.Al can be hosted through a third-party Al marketplace that integrates centrally with PACS and seamlessly integrates into the existing IT and modality infrastructure.
The ClariCT.AI device, as described in the 510(k) summary, is a software intended for denoise processing and enhancement of CT images. The submission K212074 focuses on the addition of a new module ("AI Marketplace Integration module") to the previously cleared device (K183460), enabling installation on an AI Marketplace system. This new module allows the device to be hosted through a third-party AI marketplace, integrating with PACS and existing IT infrastructure. The submission asserts that this change has no effect on the safety or efficacy of the device and does not raise any potential safety risks, and that the subject device is identical in performance to the legally marketed predicate device.
Since the submission states that "ClariCT.AI does not require clinical studies to demonstrate substantial equivalence to the predicate devices" and that the subject device is "identical in performance to the legally marketed device (K183460)", it implies that the performance data for K212074 relies on the performance data of the original K183460 submission. However, the provided document does not contain the detailed acceptance criteria or a study proving the device (K212074, or even K183460) meets such criteria, nor does it provide information regarding sample size, data provenance, ground truth establishment, or any comparative effectiveness studies.
Therefore, based solely on the provided text, I cannot provide the requested information in detail. The document primarily focuses on demonstrating substantial equivalence based on the functionality of the new integration module and adherence to general medical device standards.
Here's a breakdown of what can be extracted and what is missing:
1. Table of acceptance criteria and reported device performance:
- Acceptance Criteria (Missing): The document states, "Meets the acceptance criteria and is adequate for its intended use," but does not explicitly list these criteria.
- Reported Device Performance (Missing): No specific performance metrics (e.g., PSNR, SSIM, radiologists' scores for noise reduction, image quality, diagnostic accuracy improvements) are reported for either the subject or predicate device.
2. Sample size used for the test set and data provenance:
- Sample Size (Missing): Not mentioned in the provided text.
- Data Provenance (Missing): Not mentioned in the provided text (e.g., country of origin, retrospective/prospective). The document mentions "substantial datasets" were used for testing, but no specifics.
3. Number of experts used to establish the ground truth for the test set and their qualifications:
- Missing: This information is not provided in the document.
4. Adjudication method for the test set:
- Missing: Not mentioned in the provided text.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size:
- No: The document explicitly states: "ClariCT.AI does not require clinical studies to demonstrate substantial equivalence to the predicate devices." This implies that no MRMC comparative effectiveness study was conducted for this submission (K212074). Given the assertion of identical performance to the predicate, it also suggests such a study wasn't deemed necessary for K183460's clearance either, at least not for the purpose of demonstrating substantial equivalence.
6. If a standalone (algorithm only without human-in-the-loop performance) was done:
- Implied Yes, but details missing: The document mentions "performance tests" for the device, which is a software algorithm. However, the specific results of these standalone tests (e.g., quantitative metrics of noise reduction) are not provided. The function is described as "denoise processing and enhancement of CT images."
7. The type of ground truth used:
- Missing: Not specified. For a denoising algorithm, ground truth might involve noiseless or extremely low-noise reference images, or expert consensus on image quality. This is not detailed.
8. The sample size for the training set:
- Missing: Not mentioned. The device uses "pre-trained deep learning models," but the training set size is not provided.
9. How the ground truth for the training set was established:
- Missing: Not mentioned.
Conclusion based on provided text:
The 510(k) summary for ClariCT.AI (K212074) indicates that the device has undergone non-clinical performance testing to comply with international standards and FDA guidance. It asserts that "The test results in this 510(k), demonstrate that ClariCT.AI ... Meets the acceptance criteria and is adequate for its intended use." However, the document does not detail the specific acceptance criteria, the specific performance results against those criteria, or the methodology of any studies (e.g., sample sizes, ground truth establishment, expert involvement) that would prove these claims. The submission primarily focuses on the substantial equivalence of K212074 to its predicate (K183460) by asserting that the new AI Marketplace integration module does not alter its safety or efficacy.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).