(182 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.Al 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, heart, 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 system enables the receipt of DICOM images from CT imaging devices (modalities), enables their denoise processing and enhancement, and transmission to a PACS workstation.
The medical device, ClariCT.AI, is a software device intended for networking, communication, processing, and enhancement of CT images in DICOM format. It aims to reduce noise in CT images, particularly those with lower radiation doses, and improve image quality in low-dose non-diagnostic Filtered Back Projection and Iterative Reconstruction images.
Acceptance Criteria and Device Performance:
The document primarily focuses on demonstrating the substantial equivalence of ClariCT.AI to a predicate device (Zia, K160852) and compliance with regulatory standards. While specific quantitative acceptance criteria for image quality metrics (e.g., noise reduction percentage, CNR improvement) are not explicitly detailed in a table, the document states:
- Acceptance Criteria: The device "Meets the acceptance criteria" and "is adequate for its intended use." This implies that the internal verification and validation processes of ClariPI Inc. established specific performance benchmarks, which the device successfully met.
- Reported Device Performance: The document generally indicates that ClariCT.AI:
- Complies with international and FDA-recognized consensus standards (ISO 14971, NEMA-PS 3.1-3.20 DICOM).
- Complies with FDA guidance documents for software in medical devices and interoperable medical devices.
- Demonstrates compliance through phantom data (ACR CT Accreditation Phantom) and clinical processed data. These tests evaluate the device's ability to maintain image quality while reducing noise and enhancing images.
- The "Performance Data" section asserts that the test results "demonstrate that ClariCT.Al...Meets the acceptance criteria and is adequate for its intended use."
A Table of Acceptance Criteria & Reported Performance is not explicitly provided in the document in a quantitative format. The document describes meeting unspecified acceptance criteria through various tests.
Study Information:
-
Sample Size used for the test set and the data provenance:
- Test Set Description: The test set included "A variety of clinical processed data" which comprised:
- "Paired datasets of low and high doses for the same patients"
- "IR & FBP datasets" (Iterative Reconstruction & Filtered Back Projection)
- "Datasets for subgroup analysis of datasets with various genders, ages, body weights, races, and ethnicities"
- "Datasets with varying scan conditions using scanners from different vendors for different organs"
- Sample Size: The exact number of patients or images in the test set is not specified in the provided text.
- Data Provenance: The document does not specify the country of origin. The data is described as "clinical processed data," implying it's derived from real patient scans, but whether it's retrospective or prospective is not explicitly stated. However, given the nature of "paired datasets of low and high doses for the same patients" and "IR & FBP datasets," it strongly suggests these are retrospective analyses of existing clinical data.
- Test Set Description: The test set included "A variety of clinical processed data" which comprised:
-
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- This information is not provided in the document. The document mentions "clinical processed data" but does not detail how ground truth for image quality improvements or noise reduction effectiveness was established by experts.
-
Adjudication method for the test set:
- The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for the test set.
-
If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- No, an MRMC comparative effectiveness study was not done. The document explicitly states: "ClariCT.AI does not require clinical studies to demonstrate substantial equivalence to the predicate device." This indicates that the regulatory pathway relied on demonstrating technical equivalence and performance through non-clinical means and potentially expert consensus on image quality, rather than a reader study.
-
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance assessment was done. The entire "PERFORMANCE DATA" section describes the technical testing of the ClariCT.AI algorithm on phantom and clinical data to demonstrate its ability to reduce noise and enhance images, independent of human interaction during the measurement process. The compliance with standards and internal V&V processes are all focused on the algorithm's output.
-
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- The document implies that the ground truth for the "clinical processed data" and "phantom data" likely relied on objective measurements of image quality parameters (such as noise levels, signal-to-noise ratio, contrast-to-noise ratio) and/or expert visual assessment of image quality improvement, although the latter is not explicitly detailed as "ground truth." For the phantom, the known geometric and contrast properties serve as a form of ground truth for evaluating image fidelity after processing. For clinical data, "paired datasets of low and high doses for the same patients" suggests that the high-dose images might serve as a reference for expected image quality without significant noise. However, explicit details on how ground truth was established for image quality improvement are not provided.
-
The sample size for the training set:
- The document states that the "Noise reduction is performed with the use of pre-trained deep learning models." However, the sample size for the training set used to develop these deep learning models is not specified in the provided text.
-
How the ground truth for the training set was established:
- The document does not provide details on how the ground truth for the training set, used to develop the deep learning models, was established.
§ 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).