The iCAS -LV is intended to receive multi-phase volume datasets of reconstructed studies from PACS devices, to process them and to transfer the processing output to the PACS in DICOM format. It is a PC-based, self-contained, noninvasive image analysis software application. The device provides tools for visualization, measurements, segmentation, annotation, images registration, processing, and reporting.

The device is intended for use by trained physicians. Further, the iCAS-LV is indicated to support the physicians in visualization of CT reconstructed images and evaluation of physician-identified liver lesions. The combination of the visualization, interactive segmentation, measurements, automatic registration, and volumetric analysis, supports the physician in evaluation of the lesions in terms of size, shape, position and changes over time. The iCAS should not be used in isolation for diagnosis and making patient management decisions.

Device Description

The iCAS-LV (iCAS hereafter) provides tools for interactive segmentation of radiologist-identified liver lesions, automatic lesion length (diameter) and lesion volume computation, supervised automatic liver registration of the prior and current contrast-enhanced CT (ceCT scans, henceforth CT scans), semiautomatic lesions matching between two scans, and automatic lesion length (diameter) and volume change over time computation. These software tools will enable the user to easily assess the individual lesions volume and length (diameter), the total lesions burden volume and their evolution over time. This may help save radiologists and clinicians significant time and effort and improve the comprehensiveness and reliability of their reporting. The processing of the CT scans by iCAS does not rely on nor use any CT scanner-specific data. The device is compatible with CT scanners of vendors and models that conform to DICOM requirements as specified in the device Labeling.

The output is a quantitative analysis of the liver lesions volumes and their locations in the prior and current CT scans and a quantitative analysis of the volumetric changes of these lesions over time. The pipeline process consists then of two validation/manual steps and four automatic steps as follows:

1. Generate liver ROI of both Prior and Current scans.
1. Registration of liver ROls using deformable registration.
1. Lesion segmentation using 3D U-Net models. Segmentations are not displayed to the user until lesions are identified in Step 4
1. Designation of the liver lesions by the radiologist in both prior and current scans, with the selection and validation of their computed segmentations, including, when needed, manual correction of these computed lesion segmentations.
1. Semi-automatic lesion matching of the identified lesions in the Prior and Current CT scans, including Labeling the lesions as existing, new or disappeared.
1. Lesions and lesions change quantification (volume and diameter), which can be extended to a complete longitudinal CT study analysis. The technique computes various quantitative lesion change measures and identifies key slices in each scan.

AI/ML Overview

The provided text describes the acceptance criteria and the study that proves the device meets the acceptance criteria for the iCAS-LV device.

Acceptance Criteria and Device Performance Study for iCAS-LV

The iCAS-LV device is an image analysis software application intended to support physicians in the visualization and evaluation of physician-identified liver lesions based on CT reconstructed images. The studies presented demonstrate the device's performance in segmenting and quantifying liver lesions.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in a pass/fail format with specific thresholds. However, it describes performance testing conclusions, which can be interpreted as demonstrating meeting implicit criteria for agreement with expert assessments for clinical utility.

Metric/Testing Type	Acceptance Criteria (Implicit)	Reported Device Performance
Phantom Testing	Algorithm estimates for sphere volume and diameter should perform well against ground truth and expert readers.	- Volume (cc) Estimation: The algorithm performed well against the ground truth for volume estimation and in relation to expert readers. - Sphere Diameter (mm) Estimation: Algorithm estimates were slightly better than reader estimates in most cases, with readers similarly overestimating diameter. - RECIST (mm) Assessments: Also collected as an additional assessment. - Changes Across Locations: Analysis on pairs of phantoms indicated the scanner was in line with reader performance for changes in location, volumes, and invariance due to positioning. - Multi-Scanner Performance: Performance confirmed across four scanner manufacturers (GE, Philips, Siemens, Canon) with "small differences," supporting effectiveness for both volume and diameter estimation on multiple platforms.
Standalone Performance Testing (Clinical Data)	Physician-assisted iCAS-LV assessments of lesion 3D volumetric data should agree with radiologist manual assessments.	- The analysis supports the utility of the iCAS algorithm based on DICE, ASSD, SHD, volume, and RECIST measurements. - The comparison results demonstrated that when used by a trained radiologist, the iCAS-LV assessment of lesions 3D volumetric data agrees with the radiologist's manual assessment.
Software V&V	Software should be validated for its intended use per design documentation and fulfill relevant requirements.	Demonstrated passing results on all applicable unit, integration, and requirements testing.

2. Sample Size Used for the Test Set and Data Provenance

Sample Size for Test Set:
- Patients: 108 patients
- CT Scans: 219 contrast-enhanced CT (ceCT) scans
- Liver Lesions: 2,127 liver lesions in total (1,942 with diameter >5mm, 1,130 with diameter >10mm).
- Pairs of Lesions/Scans (for longitudinal changes): Mean number of 4.4±5.0 pairs of lesions per pairs of scans.
Data Provenance: Retrospective, multi-site data.
- 54 patients from Israel and Italy.
- 54 patients from the US.
Scanner Manufacturers Represented: GE Medical Systems, Philips, Siemens, Toshiba, Hitachi, and some classified as "unknown."

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

Number of Experts: Three experienced radiologists.
Qualifications:
- Two experienced radiologists: Independently identified and delineated liver metastases.
- One of these two is US board-certified.
- A third senior radiologist: Reviewed and compared the findings of the first two.

4. Adjudication Method for the Test Set

The ground truthing process involved a form of adjudication:

Two experienced radiologists independently identified and delineated lesions.
A third senior radiologist reviewed and compared their findings.
The final lesion delineations were validated or modified by the third radiologist, establishing the Ground Truth. This can be interpreted as a form of expert consensus with a senior reviewer for final decision.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

The provided text does not explicitly describe a conventional MRMC comparative effectiveness study designed to measure the effect size of how much human readers improve with AI vs. without AI assistance.
The clinical data analysis primarily focused on the agreement of physician-assisted iCAS software estimates with ground truth established by radiologists, rather than a direct comparison of human readers' performance with and without the device. The study "compared lesion volume and length (diameter), as well as changes in lesion volume and length over time, using estimates generated by physician-assisted iCAS software, in comparison to ground truth determined by three radiologists." This is a performance study of the device-assisted workflow, not a human-reader comparative study.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

Yes, a "Standalone Performance Testing" section is explicitly mentioned.
The description of this testing states: "HighRAD conducted a clinical data analysis comparing lesion volume and length (diameter), as well as changes in lesion volume and length over time, using estimates generated by physician-assisted iCAS software, in comparison to ground truth determined by three radiologists."
While the term "physician-assisted" is used in its description, the context of "Standalone Performance Testing" and the metrics (DICE, ASSD, SHD) typically associated with segmentation accuracy of an algorithm against a ground truth, suggest an evaluation of the algorithm's output which the physician would then validate/correct (as described in the workflow, step 4: "selection and validation of their computed segmentations, including, when needed, manual correction"). The "agreement" with manual assessment implies the automated part of the system is the focus of this standalone evaluation. The initial segmentation (step 3 of workflow, "Lesion segmentation using 3D U-Net models") happens before physician designation and validation (step 4). The "Standalone" section seems to refer to the performance of these automated steps which form the basis for the physician's validation/correction.

7. The Type of Ground Truth Used

Expert Consensus / Expert Delineation: The ground truth for the clinical data analysis was established by the consensus of three experienced radiologists, with a senior radiologist validating or modifying the final lesion delineations.
Phantom Testing: For phantom studies, a "ground truth" was used against which the algorithm's and readers' estimates were compared; for physical phantoms, this typically refers to the known physical dimensions and volumes of the spheres within the phantom.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It only describes the dataset used for "testing" or "validation" of the deep learning algorithm.

9. How the Ground Truth for the Training Set was Established

The document does not provide information on how the ground truth was established for the training set, as it does not describe the training set itself.

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). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

HighRAD Ltd. % John Smith Partner Hogan Lovells US LLP 555 Thirteenth St. Washington, District of Columbia 20004

March 1, 2024

Re: K231690 Trade/Device Name: iCAS-LV Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: January 29, 2024 Received: January 29, 2024

Dear John Smith,

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 (the 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 available 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.

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

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Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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).

Sincerelv

Jessica Lamb

Jessica Lamb, Ph.D. Assistant Director, Imaging Software Team DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

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510(k) Number (if known)

K231690

Device Name

iCAS-LV

Indications for Use (Describe)

The iCAS -LV is intended to receive multi-phase volume datasets of reconstructed studies from PACS devices, to process them and to transfer the processing output to the PACS in DICOM format. It is a PC-based, self-contained, noninvasive image analysis software application. The device provides tools for visualization, measurements, seqmentation, annotation, imaqes registration, processing, and reporting.

The device is intended for use by trained physicians. Further, the iCAS-LV is indicated to support the physicians in visualization of CT reconstructed images and evaluation of physician-identified liver lesions. The combination of the visualization, interactive segments, automatic registration, and volumetric analysis, supports the physician in evaluation of the lesions in terms of size, shape, position and changes over time. The iCAS should not be used in isolation for diagnosis and making patient management decisions.

Type of Use (Select one or both, as applicable)

X Prescription Use (Part 21 CFR 801 Subpart D)

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

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K231690 510(k) SUMMARY HighRad's iCAS-LV

Submitter:

HighRAD, Ltd132 Menachem Begin St.Azrieli Center, Round Tower Fl.22P.O.B 67, Tel Aviv 6702101, Israel
Phone: +972-54-22-66660
Contact Person: Yossi Srour
Date Prepared:	February 29, 2024
Name of Device:	iCAS-LV
Classification Name:	Medical image management and processing system
Regulatory Class:	Class II
Product Code:	QIH
Regulation:	21 CFR 892.2050
Predicate Device:	IQQA-LIVER Software from EDDA Technology, Inc. (K061696)

Device Description

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1. Generate liver ROI of both Prior and Current scans.
1. Registration of liver ROls using deformable registration.
1. Lesion segmentation using 3D U-Net models. Segmentations are not displayed to the user until lesions are identified in Step 4
1. Designation of the liver lesions by the radiologist in both prior and current scans, with the selection and validation of their computed segmentations, including, when needed, manual correction of these computed lesion segmentations.
1. Semi-automatic lesion matching of the identified lesions in the Prior and Current CT scans, including Labeling the lesions as existing, new or disappeared.
1. Lesions and lesions change quantification (volume and diameter), which can be extended to a complete longitudinal CT study analysis. The technique computes various quantitative lesion change measures and identifies key slices in each scan.

Intended Use / Indications for Use

The iCAS -LV is intended to receive multi-phase volume datasets of reconstructed studies from PACS devices, to process them and to transfer the processing output to the PACS in DICOM format. It is a PC-based, self-contained, noninvasive imaqe analysis software application. The device provides tools for visualization, measurements, segmentation, annotation, images registration, processing, and reporting.

Performance Data

The following testing was conducted for the iCAS-LV system with data included in the 510(k) document.

Software Verification and Validation

Software verification and validation were conducted for the iCAS-LV software to validate it for its intended use per the design documentation in line with recommendations outlined in General Principles of Software Validation, Guidance for Industry and FDA Staff. The iCAS-LV software demonstrated passing results on all applicable unit, integration, and requirements testing.

Non-Clinical Performance Testing

Phantom Testing

HighRAD performed a phantom study evaluating sphere volume, sphere volume difference, and related measures with estimates generated by the iCAS algorithm and by two expert radiologists using a dedicated CT liver phantom. The results demonstrated the algorithm performed well against the ground truth for volume (cc) estimation and in relation to the expert readers. Sphere diameter (mm)

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estimates for the algorithm were slightly better than reader estimates in most cases, with readers similarly overestimating diameter. RECIST (mm) assessments were also collected as an additional assessment. The analysis of changes across locations on pairs of phantoms indicated the scanner was in line with reader performance for changes in location, volumes and invariance due to positioning.

Four scanners were evaluated; the performance was confirmed in all scanners with some small differences, as could be expected given the multiplicity of testing (GE, Philips, Siemens, and Canon). Hence, this phantom study supported the effectiveness of the algorithm for both volume and diameter estimation on multiple platforms.

Standalone Performance Testing

HighRAD conducted a clinical data analysis comparing lesion volume and length (diameter), as well as changes in lesion volume and length over time, using estimates generated by physician-assisted iCAS software, in comparison to ground truth determined by three radiologists. The analysis supports the utility of the iCAS algorithm based on DICE, ASSD, SHD, volume and RECIST measurements. The comparison results demonstrated that when used by a trained radiologist, the iCAS-LV assessment of lesions 3D volumetric agrees with the radiologist manual assessment.

The deep learning algorithm was tested using the following dataset:

. The retrospective multi-site data set consists of 108 patients (54 from Israel and Italy and 54 from the US) consists of 219 ceCT scans with 2,127 liver lesions. Of those, 1,942 lesions have a diameter >5mm (0.06cc), and 1,130 have a diameter >10mm (0.52cc). The mean number of lesions per scan is 9.7±13.1 with a mean lesion RECIST diameter of 13.97±12.41 mm and mean volume of 3.95±23.98cc.
. There were 51 females and 57 males with a mean age of 62 ±12 years (min 31 and max 92 years).
. For the volume changes over time calculation, the mean number of pairs of lesions per pairs of scans was 4.4±5.0.
. The clinical data used for the validation contained the following scanner manufacturers: GE Medical Systems, Philips, Siemens, Toshiba, Hitachi and some classified as "unknown".
. The ground truthing process involved two experienced radiologists, one of whom is US boardcertified, independently identifying and delineating liver metastases in abdominal ceCT scans. A third senior radiologist reviewed and compared their findings, with the final lesion delineations validated or modified by the third radiologist being considered as the Ground Truth for the study.

Substantial Equivalence

The predicate device, legally marketed IQQA-LIVER Software (K061696), and the iCAS-LV have the same intended use of communicating CT studies in DICOM format and of processing volumetric data.

Both the iCAS-LV and the predicate device are indicated for processing CT studies for visualization, evaluation and reporting of physician-identified liver lesions. Both devices support physicians in

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evaluating physician-identified liver lesions by combining image processing, viewing and reporting tools.

The technological characteristics of the two devices are similar but different because of each device's unique software design. The verification and validation (V&V) process includes phantom testing, bench testing and clinical experimental study to address any safety and effectiveness concerns. The device performance was verified and validated in software and bench tests with 4 commercially available CT scanners by known vendors. The experimental clinical study was performed in conformance with the clinical environment using clinical studies from Israel, Italy and the US. Thus, the V&V process aimed to meet all the requirements of international standards and internal design control procedures. A comparison table is presented below.

	Subject Device	Predicate Device
Parameter	iCAS -LV	IQQA-LIVER Software (K061696)
Intendeduse	The iCAS -LV is intended to receive multi-phase volume datasets of reconstructedstudies from PACS devices, to processthem and to transfer the processing outputto the PACS in DICOM format. It is a PC-based, self-contained, noninvasive imageanalysis software application. The deviceprovides tools for for visualization,measurements, segmentation, annotation,images registration, processing, andreporting. The device is intended for use bytrained physicians.	Indications for Use (intended use): The IQQA-Liver is a PC-based, self-contained,noninvasive image analysis software applicationfor reviewing serial multi-phase CT acquisitionsof the liver. Combining image viewing,processing and reporting tools, the software isdesigned to suport physicians in thevisualization, evaluation and reporting of liverand physician-identified liver lesions. Thesoftware supports a workflow based onautomated image registration for viewing andanalyzing multi-phase volume datasets. It alsoincludes tools for interactive segmentation and
Indicationsfor use	Further, the iCAS-LV is indicated to supportthe physicians in visualization of CTreconstructed images and evaluation ofphysician-identified liver lesions.Thecombination of the visualization, interactivesegmentation, measurements, automaticregistration, and volumetric analysis,supports the physician in evaluation of thelesions in terms of size, shape, position andchanges over time. The iCAS should not beused in isolation for diagnosis and makingpatient management decisions.	labeling of liver segments and vascularstructures. The software provides functionalitiesfor manual or interactive segmentation ofphysician-identified lesions, and allows forregional volumetric analysis of such lesions interms of size, shape, position and enhancementpattern, providing information for physician'sassessment of lesion characterization. Thesoftware is designed for use by trainedphysicians. Image source: DICOM
21CFRsection	892.2050	892.2050
ProductCode	QIH	LLZ
Devicenature	PC-based self-contained post processingSW package	PC-based self-contained post processing SWpackage

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Parameter	Subject DeviceiCAS -LV	Predicate DeviceIQQA-LIVER Software (K061696)
Data inputs	CT reconstructed images in DICOM format	CT reconstructed images in DICOM format
SWFunctions	The HighRAD uniquely designed SW,supports a workflow, which is based onautomated image registration, manual orinteractive segmentation of physician-identified liver lesions, for viewing andanalysing multi-phase volume datasets, Italso includes tools for Labeling andreporting.	Similar SW functions performed by EDDAunique design.
GraphicalUserInterface	A graphical user interface for users tointeract with the software, to visualize CTdata, to select tools and to drive theworkflow	A graphical user interface for users to interactwith the software, to visualize CT data, to selecttools and to drive the workflow
Dataoutputs	CT images in DICOM format	CT images in DICOM format

Conclusions

The iCAS-LV is as safe and effective as the predicate device has the same intended use and similar indications, technological characteristics, and principles of operation as its predicate device. The minor differences in indications do not alter the intended use of the device and do not affect its safety and effectiveness when used as labeled. In addition, the minor technological differences between iCAS-LV and its predicate devices raise no new issues of safety or effectiveness. Thus, the iCAS-LV is substantially equivalent.

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).