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.

Device Description

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.

AI/ML Overview

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.
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.

Summary

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Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA text logo on the right. The FDA text logo is in blue and includes the acronym "FDA" in a blue square, followed by "U.S. FOOD & DRUG ADMINISTRATION" in a stacked format.

June 13, 2019.

ClariPI Inc % Mr. Carl Alletto Consultant OTech Inc. 8317 Belew Drive MCKINNEY TX 75071

Re: K183460

Trade/Device Name: ClariCT.AI Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: May 3, 2019 Received: May 7, 2019

Dear Mr. Alletto:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for

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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely.

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K183460

Device Name ClariCT.AI

Indications for Use (Describe)

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.

Type of Use (Select one or both, as applicable)
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X | Prescription Use (Part 21 CFR 801 Subpart D)

| | Over-The-Counter Use (21 CFR 801 Subpart C)

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Image /page/3/Picture/1 description: The image shows the logo for "Clariπ MEDICAL IMAGING SOLUTIONS". The word "Clari" is in bold black font, while the pi symbol is in a light blue color. Below the word "Clariπ" is the text "MEDICAL IMAGING SOLUTIONS" in a smaller, thinner font.

This 510(k) Summary is being submitted in accordance with the requirements of as required by K183460 section 807.92(c).

. SUBMITTER

ClariPl Inc. 3F, 70-15, Ihwajang-gil, Jongno-qu Seoul, Korea, Republic of [03088] Tel: +82-2-741-3014 Fax: +82-2-743-3014 Email: claripi@claripi.com

Contact person: Ms. Hyun-Sook Park, CEO Date Prepared: May 3, 2019

II. DEVICE

Name of Device: ClariCT.Al Common or Usual Name: Picture, archive and communications system Classification Name: System, Image Processing, Radiological (21 CFR 892.2050) Regulatory Class: II Product Code: LLZ

III. PREDICATE DEVICE

This predicate has not been subject to a design-related recall.

The ClariCT.Al software device is substantially equivalent to K160852:

Device Classification Name	system, image processing, radiological
510(k) Number	K160852
Device Name	Zia
Applicant	Zetta Medical Technologies, LLC.1313 Ensell RoadLake Zurich, IL 60047
Regulation Number	892.2050
Classification Product Code	LLZ
Date Received	03/28/2016
Decision Date	12/15/2016
510k Review Panel	Radiology

IV. DEVICE DESCRIPTION

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.

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Image /page/4/Picture/1 description: The image shows the logo for "ClariPi MEDICAL IMAGING SOLUTIONS". The word "Clari" is in bold black font, and the "Pi" is a blue stylized version of the mathematical symbol pi. Below the logo is the text "MEDICAL IMAGING SOLUTIONS" in a smaller font size.

V. INDICATIONS FOR USE

ClariCT.Al, 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.

VI. SUBSTANTIAL EQUIVALENCE TABLE

The subject device (ClariCT.Al) is substantially equivalent to the predicate device (K160852, ZIA) which is also used for noise reduction and enhancement of CT images.

The following information compares the subject device to the predicate. The difference lies in noise reduction method where ClariCT.AI, the subject device uses pre-trained deep learning models whereas the predicate device uses reqularization process at flat regions with data fidelity constraints at edges. It has no effect on the safety or efficacy of the subject device and does not raise any potential safety risks, and the subject device is identical in performance to the legally marketed device.

Item	Subject Device – ClariCT.AI	Predicate- ZIA(K160852)
Intended Use	ClariCT.AI is intended fornetworking, communication,processing and enhancementof CT images in DICOMformat.	ZIA image enhancement system isan image processing software thatcan be used for reducing noise inCT images. Enhanced images willbe uploaded back to host/PACSsystems and exist in conjunction tothe original images. ZIA, is notintended for mammographyapplications. The device processingis not effective for lesion, mass orabnormalities of sizes less than 2.0mm.
Intended User	Radiologists and Specialists	Radiologists and Specialists
Modality Support	CT	CT
Noise ReductionMethod	Noise reduction is performedwith the use of pre-traineddeep learning models.	Regularization process at flatregions with data fidelity constraintsat edges.
Image Format andcommunications	DICOM	DICOM
Components andHardwarerequirement	Window Operating System,PC Hardware, CUDAsupported graphics card orequivalent.	Window Operating System,PC Hardware, CUDA supportedgraphics card or equivalent.

VII. PERFORMANCE DATA

Non-clinical performance testing has been performed on ClariCT.Al. (the subject device) and demonstrates compliance with the following International and FDA-recognized consensus standards and FDA guidance document:

. ISO 14971Medical devices - Application of risk management to medical devices
. NEMA-PS 3.1- PS 3.20 Digital Imaging and Communications in Medicine (DICOM)

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510(k) Summary

Image /page/5/Picture/1 description: The image shows the logo for ClariPi Medical Imaging Solutions. The word "Clari" is in bold black font, followed by a blue pi symbol. Below the word "ClariPi" is the text "MEDICAL IMAGING SOLUTIONS" in a smaller, lighter font.

. Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices issued May 11, 2005.
Design Considerations and Pre-market Submission Recommendations for Interoperable . Medical Devices issued September 6, 2017.
. The subject device, was tested in accordance with the internal Verification and Validation processes of ClariPI Inc.. Verification and Validation tests have been performed to address intended use, the technological characteristics claims, requirement specifications, and the risk management results. ClariCT.Al has been validated using:
- The use of ACR CT Accreditation Phantom o
- A variety of clinical processed data: o
  - " Paired datasets of low and high doses for the same patients
  - IR & FBP datasets
  - . 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

The test results in this 510(k), demonstrate that ClariCT.Al:

complies with the aforementioned international and FDA-recognized consensus ● standards and
. FDA guidance document, and
. Meets the acceptance criteria and is adequate for its intended use.

Therefore, ClariCT.Al, is substantially equivalent to the currently marketed predicate device, in terms of safety and effectiveness.

Clinical Testing:

ClariCT.Al does not require clinical studies to demonstrate substantial equivalence to the predicate device.

VIII CONCLUSIONS

Verification and Validation activities required to establish the safety and effectiveness of ClarCT.Al, were performed. Testing involved system level tests, performance tests, and safety testing from risk analysis. Testing performed, demonstrated the subject device meets pre-defined functionality requirements.

The subject device and predicate device are substantially equivalent in the areas of technical characteristics, general function, application, and intended use. Test results with the phantom data and clinical processed dataset demonstrate that the subject device is as safe and effective and therefore substantially equivalent to the predicate device.

Regulation Number and Section

§ 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).