Arterys Oncology DL is a medical diagnostic application for viewing, manipulation and comparison of medical images from multiple imaging modalities and/or multiple time-points. The application supports anatomical datasets, such as CT or MR. The images can be viewed in a number of output formats including MIP and volume rendering.

Arterys Oncology DL enables visualization of information that would otherwise have to be visually compared disjointedly. Arterys Oncology DL provides analytical tools to help the user assess and document changes in morphological activity at diagnostic and therapy follow-up examinations.

Arterys Oncology DL is designed to support the oncological workflow by helping the user confirm the absence or presence of lesions, including evaluation, quantification, follow-up and documentation of any such lesions.

Note: The clinician retains the ultimate responsibility for making the pertinent diagnosis based on their standard practices and visual comparison of the separate unregistered images. Arterys Oncology DL is a complement to these standard procedures.

Device Description

This traditional 510(k) is being submitted for Arterys Oncology DL which is intended for viewing, manipulation, 3D-visualization and comparison of medical images from multiple imaging modalities and/or multiple time-points. The application supports anatomical datasets, such as CT or MR. The images can be viewed in a number of output formats including MIP and volume rendering. The software supports the oncological workflow by helping the user to confirm the absence or presence of lesions, including evaluation, follow-up and documentation of any such lesions.

Key features of the software are:

2D and 3D visualization and comparative review
Manual volumetric segmentation
Semi-automatic volumetric segmentation of lung nodules and liver lesions
Co-registration
Longitudinal tracking
Nodule/lesion size quantifications
Data reporting based on Lung-RADS and LI-RADS guidelines

AI/ML Overview

The provided text describes the Arterys Oncology DL device and its 510(k) submission for FDA clearance. While it outlines general performance data and verification activities, it lacks the specific details required to fully address all aspects of the acceptance criteria and the study that proves the device meets them.

Here's an analysis based on the available information:

1. A table of acceptance criteria and the reported device performance

The document mentions "software specifications" and "design requirements" but does not explicitly state quantitative acceptance criteria for the device's performance (e.g., specific accuracy, sensitivity, or specificity thresholds). Instead, it broadly states that "the device meets its design requirements and intended use, that it is as safe and as effective as the predicate devices, and that no new issues of safety and effectiveness were raised."

Therefore, a precise table of acceptance criteria and reported device performance cannot be generated from the given text.

2. Sample size used for the test set and the data provenance

The document mentions "Testing for Liver MR Deep Learning Model, Lung MR Deep Learning Model, Lung Longitudinal Tracking and usability." However, it does not specify the sample sizes used for these test sets.

It also does not provide information on the data provenance (e.g., country of origin, retrospective or prospective nature) for the test sets.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The document does not provide any information regarding the number of experts used to establish the ground truth for the test set or their qualifications.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not provide any information about the adjudication method used for the test set.

5. 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

The document does not explicitly state that a multi-reader multi-case (MRMC) comparative effectiveness study was done, nor does it provide any effect size for human reader improvement with AI assistance. It mentions that "the clinician retains the ultimate responsibility" and that the device "is a complement to these standard procedures," suggesting an AI-assisted workflow, but no specific study details are provided.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The document states, "Arterys non-clinical V&V testing included Testing for Liver MR Deep Learning Model, Lung MR Deep Learning Model, Lung Longitudinal Tracking and usability." This suggests that performance of the deep learning models themselves was evaluated, which would align with standalone algorithm performance, particularly for the "segmentation of lung nodules and liver lesions" feature. However, the document does not explicitly present or detail standalone (algorithm only) performance metrics or studies.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The document does not specify the type of ground truth used for any of the testing mentioned (Liver MR Deep Learning Model Testing, Lung MR Deep Learning Model Testing, Lung Longitudinal Tracking).

8. The sample size for the training set

The document does not provide the sample size for the training set.

9. How the ground truth for the training set was established

The document does not provide information on how the ground truth for the training set was established.

Summary of Missing Information:

The provided text focuses on the regulatory submission process, the device's intended use, and its general adherence to safety and performance standards. It lacks the detailed technical and scientific study information typically found in a clinical study report or technical performance evaluation, specifically:

Quantitative acceptance criteria.
Specific sample sizes for test and training sets.
Data provenance for test and training sets.
Details on expert involvement (number, qualifications, adjudication) for ground truth establishment.
Specific performance metrics for the deep learning models (standalone or assisted).
Details of any comparative effectiveness studies with human readers.
Methods for establishing ground truth for both training and testing.

Summary

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January 25, 2018

Image /page/0/Picture/1 description: The image contains the logos of the Department of Health and Human Services and the Food and Drug Administration (FDA). The Department of Health and Human Services logo is on the left, and the FDA logo is on the right. The FDA logo includes the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.

Arterys Inc. % Mr. John Axerio-Cilies Chief Operating Officer and Founder 51 Federal Street, Suite 305 SAN FRANCISCO CA 94107

Re: K173542

Trade/Device Name: Arterys Oncology DL Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: II Product Code: LLZ Dated: November 15, 2017 Received: November 16, 2017

Dear Mr. Axerio-Cilies:

We have reviewed vour 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. 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); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820); and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

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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 http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm for the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/) and CDRH Learn

(http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/DICE) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

Michael D. O'Hara For

Robert Ochs. Ph.D Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known)

K173542

Device Name Arterys Oncology DL

Indications for Use (Describe)

Arterys Oncology DL enables visualization of information that would otherwise have to be visually compared disjointedly. Artervs Oncology DL provides analytical tools to help the user assess and document changes in morphological activity at diagnostic and therapy follow-up examinations.

Type of Use (Select one or both, as applicable)
☑ Prescription Use (Part 21 CFR 801 Subpart D)	☐ Over-The-Counter Use (21 CFR 801 Subpart C)

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Section 5. 510(k) Summary

General Information 1.

510(k) Sponsor	Arterys Inc.
Address	51 Federal St. Suite 305San Francisco, CA 94107
Correspondence Person	John Axerio-CiliesCOO and Founder
Contact Information	Email: quality@arterys.comPhone: 650-391-7111
Date Prepared	Nov. 15, 2017

2. Proposed Device

Proprietary Name	Arterys Oncology DL
Common Name	Oncology
Classification Name	System, Image Processing, Radiological
Regulation Number	21 CFR 892.2050
Product Code	LLZ
Regulatory Class	II

3. Predicate Device

Proprietary Name	syngo™ TrueD
Premarket Notification	K101749
Classification Name	System, Image Processing, Radiological
Regulation Number	21 CFR 892.2050
Product Code	LLZ
Regulatory Class	II

Reference Device 4.

Proprietary Name	Arterys Cardio DL
Premarket Notification	K163253
Classification Name	System, Image Processing, Radiological
Regulation Number	21 CFR 892.2050
Product Code	LLZ
Regulatory Class	II

Device Description 5.

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confirm the absence or presence of lesions, including evaluation, follow-up and documentation of any such lesions.

Key features of the software are:

2D and 3D visualization and comparative review
. Manual volumetric segmentation
Semi-automatic volumetric segmentation of lung nodules and liver lesions
Co-registration
Longitudinal tracking
Nodule/lesion size quantifications
Data reporting based on Lung-RADS and LI-RADS guidelines

6. Indications for Use

Arterys Oncology DL is a medical diagnostic application for viewing, manipulation, 3Dvisualization and comparison of medical images from multiple imaging modalities and/or multiple time-points. The application supports anatomical datasets, such as CT or MR. The images can be viewed in a number of output formats including MIP and volume rendering.

7. Comparison of Technological Characteristics with the Predicate Device

Feature/Function	Proposed Device:Arterys Oncology DL	Predicate Device(Primary):syngoTM TrueD(K101749)	ReferenceDevice:Arterys CardioDL(K163253)
Image input	Complies withDICOM Standard	Complies withDICOM & DICOMRT Standards
Type of scans	MR, CT	MR, CT, PET,SPECT
Feature/Function	Proposed Device:Arterys Oncology DL	Predicate Device(Primary):syngo™ TrueD(K101749)	ReferenceDevice:Arterys CardioDL(K163253)
2D and 3D ImageReview	Yes	Yes
2D and 3DComparativeReview	Yes	Yes
Manual-VolumetricSegmentation	Yes	Yes
Co-registration	Yes	Yes
LongitudinalTracking	Yes, linked VOIsbetween timepoints	Yes, linked VOIsbetween timepoints
Linear DimensionCalculation forVolumetricSegmentation	Yes,longest lineardimension (mm) inaxial plane, andorthogonal to longestlinear dimension	Yes, maximumdiameter (mm)measurement
VolumeCalculation	Yes	Yes
Reporting	Yes, basic reportingandLI-RADS and Lung-RADS observationreporting based onstandard guidelines	Yes, basic reporting
Semi-automated,VolumetricSegmentation	Yes, available onlyfor lung CT and liverMR studies		Yes, available forventricularsegmentation

Table 5.1: Comparison Table

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8. Performance Data

The safety and performance of Arterys Oncology DL has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Non-clinical verification and validation test results established that the device meets its design requirements and intended use, that it is as safe and

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as effective as the predicate devices, and that no new issues of safety and effectiveness were raised. Arterys non-clinical V&V testing included Testing for Liver MR Deep Learning Model, Lung MR Deep Learning Model, Lung Longitudinal Tracking and usability. The Verification, validation and usability testing data demonstrate that the device meets all its specifications.

Further, during the development, potential hazards were controlled by a risk management plan including risk analysis, risk mitigation, verification and risk-benefit analysis. Additionally, the software verification and validation activities were performed in accordance with the following standards, in addition to the FDA Guidance Document, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices":

Digital Imaging and Communications in Medicine (DICOM); PS 3.1 3.20 (2011) .
. Medical device software - Software life cycle process, IEC 62304: First edition 2006-05. 2015
. Medical devices - Application of risk management to medical devices, ISO 14971; Second edition 2007-03-01, 2012
. Medical Devices - Application of usability engineering to medical devices, ISO 62366: 2007/(R) 2013

9. Conclusion

Based on the information submitted in this premarket notification and on the indications for use, technological characteristics, and performance testing, Arterys Oncology DL raises no new questions of safety or effectiveness and is substantially equivalent to the predicate device in terms of safety, efficacy, and performance.

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).