(113 days)
No reference devices were used in this submission.
Yes
The document explicitly states that the device provides "machine learning-enabled tools for image analysis and segmentation" and mentions "machine learning algorithms" and "machine learning (ML)-based tools". It also describes the training and testing of a "machine learning algorithm".
No
The device is intended for surgical planning and display, providing visual, non-diagnostic output, and does not directly treat or diagnose a disease or condition.
No
The document explicitly states that "The output file is meant for visual, non-diagnostic use and shall be reviewed by clinicians who are responsible for all final patient management decisions." This indicates it's for planning and display, not to make a diagnosis.
Yes
The device description explicitly states that IRISeg is a "standalone software application." While it processes medical images (CT scans), it does not mention or describe any accompanying hardware components that are part of the device itself. The output is a file for rendering a 3D model, which is also a software-based output.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use explicitly states that the output file is "meant for visual, non-diagnostic use" and that clinicians are "responsible for all final patient management decisions." This clearly indicates that the device is not intended to provide a diagnosis or be used in the diagnostic process.
- Device Description: The description focuses on segmentation and generating output files for rendering 3D models, which aligns with surgical planning and visualization rather than diagnostic testing.
- Lack of Diagnostic Claims: There are no claims or indications that the device is used to detect, diagnose, monitor, or predict any disease or condition based on in vitro analysis of biological specimens.
IVD devices are specifically designed to perform tests on samples taken from the human body (like blood, urine, tissue) to provide information for diagnosis, monitoring, or treatment decisions. This device operates on medical images (CT scans) and is intended for visualization and planning purposes, not for analyzing biological samples or making diagnostic determinations.
No
The provided text does not contain any explicit statement that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
IRISeg is intended for use as a software application that receives DICOM compliant contrastenhanced CT images, provides manual and machine learning-enabled tools for image analysis and segmentation, and creates an output file that can be used to render a 3D model for preoperative surgical planning and intraoperative display. The use of IRISeq may include the generation of preliminary segmentations using machine learning algorithms. IRISeg is intended for use by qualified professionals. The output file is meant for visual, non-diagnostic use and shall be reviewed by clinicians who are responsible for all final patient management decisions.
The machine learning enabled kidney CT auto-seamentation tool is intended for use for adult patients with contrast-enhanced, axial kidney CT images with slice thickness 3mm or less.
Product codes (comma separated list FDA assigned to the subject device)
QIH, LLZ
Device Description
IRISeg Version 3.1 ("IRISeg") is a standalone software application created by Intuitive Surgical for segmentation of contrast-enhanced kidney CT images and generation of output files that can be rendered as virtual 3D models of kidney organs. IRISeg is designed to professionals ("users") with manual and machine learning (ML)-based tools for segmentation of kidney anatomy based on CT scans.
Input File
IRISeg can open and load CT imaging files in DICOM (Digital Imaging and Communications in Medicine) format, and label map files (2D segmented labels) in NIFTI (Neuroimaging Informatics Technology Initiative) format from an accessible storage location.
Output File
Following the use of IRISeg to segment kidney CT imaging files, the software can be used to generate an output file that can be used to render virtual segmented kidney 3D models.
IRISeg Manual Tools
IRISeg includes a variety of tools for users to manually edit segmentation labels such as Paintbrush tools, Eraser tools, Connected Component Selection, Free Curve Selection, Morphological operations, Mathematical Operations.
IRISeg manual tools can be used to modify the output of the ML-based auto-segmentation algorithm or to label structures from scratch without the use of ML-based auto-segmentation. The ML-based autosegmentation does not generate mass labels. Users must segment and label renal masses with the manual tools.
IRISeg ML-Based Auto-Segmentation Tools
IRISeg includes an ML-based auto-segmentation algorithm for automatic segmentation of four kidney structures from CT imaging. The auto-segmentation algorithm is a neural network based ML algorithm. It is trained on segmented kidney CT models that were sourced from clinical data processed during commercial operation of the cleared IRIS 1.0 system (K182643). Each 3D model was reviewed by one U.S board certified radiologist. The input is a 3D CT image (series of 2D slices). The output of the model is four probability maps for kidney parenchyma, kidney artery, kidney vein, and collecting system. The probability maps are thresholded to generate binary masks for kidney parenchyma, kidney artery, kidney vein and collecting system. The ML-based auto-segmentation does not generate binary masks for kidney masses.
The algorithm output is intended as an initial estimate of the segmentation. The user must use the manual tools to update the initial algorithm output to generate the kidney CT 3D model.
The development of IRISeg kidney CT ML-based auto-segmentation algorithm follows FDA's Good Machine Learning Practices for Medical Device Development: Guiding Principles, October 2021.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
contrast-enhanced CT images
Anatomical Site
kidney
Indicated Patient Age Range
adult patients
Intended User / Care Setting
qualified professionals
Description of the training set, sample size, data source, and annotation protocol
The auto-segmentation algorithm is a neural network based ML algorithm. It is trained on segmented kidney CT models that were sourced from clinical data processed during commercial operation of the cleared IRIS 1.0 system (K182643). Each 3D model was reviewed by one U.S board certified radiologist.
Description of the test set, sample size, data source, and annotation protocol
A total of 81 kidney CT scans were used to evaluate the algorithm. The test dataset did not include more imaging study from any particular patient. No imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used in testing ensured diversity in patient population, scan parameters and scanner manufacturers. Subgroup analysis was performed to ensure model performance was generalizable.
Performance was evaluated by comparing segmentations generated by the kidney CT machine learning algorithm against segmentations generated by a consensus of three U.S Board Certified Radiologists for the same imaging study.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Machine Learning Auto-Segmentation Testing
IRISeg includes a ML-enabled kidney CT auto-segmentation algorithm. Intuitive conducted the bench performance testing using clinical kidney CT images to help demonstrate that the proposed device achieved the expected segmentation accuracy performance testing of the algorithm was done using clinical kidney CT scans. A total of 81 kidney CT scans were used to evaluate the algorithm. The test dataset did not include more imaging study from any particular patient. No imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used in testing ensured diversity in patient population, scan parameters and scanner manufacturers. Subgroup analysis was performed to ensure model performance was generalizable.
Performance was evaluated by comparing segmentations generated by the kidney CT machine learning algorithm against segmentations generated by a consensus of three U.S Board Certified Radiologists for the same imaging study. The performance of the machine learning model is characterized by the Sørensen–Dice coefficient (DSC) or the Mean Distance to Agreement (MDA) metric. All testing results are presented on a 95% two-sided t-statistic confidence interval basis. Overall testing results across the entire test dataset are summarized below.
Overall Testing Results
| Test Case | Artery DSC | Parenchyma DSC | Vein DSC | Collecting System MDA |
|---|---|---|---|---|
| Overall Testing (N=81) | [0.87, 0.90] | [0.95, 0.97] | [0.87, 0.89] | [1.3, 1.9] |
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sørensen–Dice coefficient (DSC) or the Mean Distance to Agreement (MDA)
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
No reference devices were used in this submission.
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 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).
0
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December 10, 2024
Intuitive Surgical Inc. % Jyh-Shyan (Jesse) Lin Senior Regulatory Affairs Specialist 1266 Kifer Road SUNNYVALE, CA 94086
Re: K242461
Trade/Device Name: IRISeg Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH, LLZ Dated: August 19, 2024 Received: August 19, 2024
Dear Jyh-Shyan (Jesse) Lin:
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.
1
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).
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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
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 medical devices and radiation-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-regulatory
2
assistance/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,
Lu Jiang
Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems 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
Enclosure
3
Indications for Use
Submission Number (if known)
Device Name
IRISeg
Indications for Use (Describe)
IRISeg is intended for use as a software application that receives DICOM compliant contrastenhanced CT images, provides manual and machine learning-enabled tools for image analysis and segmentation, and creates an output file that can be used to render a 3D model for preoperative surgical planning and intraoperative display. The use of IRISeq may include the generation of preliminary segmentations using machine learning algorithms. IRISeg is intended for use by qualified professionals. The output file is meant for visual, non-diagnostic use and shall be reviewed by clinicians who are responsible for all final patient management decisions.
The machine learning enabled kidney CT auto-seamentation tool is intended for use for adult patients with contrast-enhanced, axial kidney CT images with slice thickness 3mm or less.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
ver-The-Counter Use (21 CFR 801 Subpart C)
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K242461
510(k) SUMMARY
This summary of 510(k) safety and effectiveness information is submitted in accordance with the requirements of Safe Medical Device Act (SMDA) 1990 and 21 CFR 807.92.
1. SUBMITTER
| 510(k) Owner: | Intuitive Surgical, Inc.
1266 Kifer Road
Sunnyvale, CA 94086 |
|----------------|----------------------------------------------------------------------------------------------------------------------|
| Contact: | Jyh-Shyan Lin
Senior Regulatory Affairs Specialist
Phone Number: 408-523-4952
Email: jesse.lin@intusurg.com |
| Date Prepared: | November 15, 2024 |
2. SUBJECT DEVICE INFORMATION
| Manufacturer Name: | Intuitive Surgical, Inc. |
|---|---|
| 510(k) Number: | K242461 |
| Trade Name: | IRISeg |
| Common Name: | Medical image processing software |
| Classification: | Class II |
| 21 CFR 892.2050 | |
| Medical image management and processing system | |
| Primary Product Codes: | QIH |
| Associate Product Code: | LLZ |
| Review Panel: | System, Image Processing, Radiological |
3. PREDICATE DEVICE INFORMATION
| Manufacturer Name: | Intuitive Surgical, Inc. |
|---|---|
| 510(k) Number: | K182643, last cleared on February 22, 2019 |
| Trade Name: | IRIS 1.0 System |
| Common Name: | Medical image processing software |
| Classification: | Class II |
| 21 CFR 892.2050 | |
| Medical image management and processing system | |
| Product Code: | LLZ |
| Review Panel: | System, Image Processing, Radiological |
No reference devices were used in this submission.
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4. DEVICE DESCRIPTION
IRISeg Version 3.1 ("IRISeg") is a standalone software application created by Intuitive Surgical for segmentation of contrast-enhanced kidney CT images and generation of output files that can be rendered as virtual 3D models of kidney organs. IRISeg is designed to professionals ("users") with manual and machine learning (ML)-based tools for segmentation of kidney anatomy based on CT scans.
Input File
IRISeg can open and load CT imaging files in DICOM (Digital Imaging and Communications in Medicine) format, and label map files (2D segmented labels) in NIFTI (Neuroimaging Informatics Technology Initiative) format from an accessible storage location.
Output File
Following the use of IRISeg to segment kidney CT imaging files, the software can be used to generate an output file that can be used to render virtual segmented kidney 3D models.
IRISeg Manual Tools
IRISeg includes a variety of tools for users to manually edit segmentation labels such as Paintbrush tools, Eraser tools, Connected Component Selection, Free Curve Selection, Morphological operations, Mathematical Operations.
IRISeg manual tools can be used to modify the output of the ML-based auto-segmentation algorithm or to label structures from scratch without the use of ML-based auto-segmentation. The ML-based autosegmentation does not generate mass labels. Users must segment and label renal masses with the manual tools.
IRISeg ML-Based Auto-Segmentation Tools
IRISeg includes an ML-based auto-segmentation algorithm for automatic segmentation of four kidney structures from CT imaging. The auto-segmentation algorithm is a neural network based ML algorithm. It is trained on segmented kidney CT models that were sourced from clinical data processed during commercial operation of the cleared IRIS 1.0 system (K182643). Each 3D model was reviewed by one U.S board certified radiologist. The input is a 3D CT image (series of 2D slices). The output of the model is four probability maps for kidney parenchyma, kidney artery, kidney vein, and collecting system. The probability maps are thresholded to generate binary masks for kidney parenchyma, kidney artery, kidney vein and collecting system. The ML-based auto-segmentation does not generate binary masks for kidney masses.
The algorithm output is intended as an initial estimate of the segmentation. The user must use the manual tools to update the initial algorithm output to generate the kidney CT 3D model.
The development of IRISeg kidney CT ML-based auto-segmentation algorithm follows FDA's Good Machine Learning Practices for Medical Device Development: Guiding Principles, October 2021.
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5. INTENDED USE/INDICATIONS FOR USE
IRISeg is intended for use as a software application that receives DICOM compliant contrast-enhanced CT images, provides manual and machine learning-enabled tools for image analysis and segmentation, and creates an output file that can be used to render a 3D model for preoperative surgical planning and intraoperative display. The use of IRISeg may include the generation of preliminary segmentations using machine learning algorithms. IRISeg is intended for use by qualified professionals. The output file is meant for visual, non-diagnostic use and shall be reviewed by clinicians who are responsible for all final patient management decisions.
The machine learning enabled kidney CT auto-segmentation tool is intended for use for adult patients with contrast-enhanced, axial kidney CT images with slice thickness 3mm or less.
6. SUMMARY OF SUBSTANTIAL EQUIVALENCE
The subject device has been developed as a standalone software application by modifying the segmentation software application of the predicate device, the IRIS 1.0 System (K182643). The comparison with the predicate device is based on the intended use, indications for use, general design, technological characteristics and operational principle/workflow. A summary of the subject device compared to the segmentation software application (part of the predicate device) is provided below.
| Predicate Device (K182643) -
Segmentation Software Application as
part of IRIS 1.0 System | Subject Device
IRISeg | |
|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|
| Intended use for providing tools for image analysis
and segmentation of kidney CT images.
The IRIS 1.0 System is intended as a medical
imaging system that allows the processing, review,
analysis, communication, and media interchange
of multi-dimensional digital images acquired from
CT imaging devices. It is also intended as software
for preoperative surgical planning, and as
software for the intraoperative display of the
aforementioned multi-dimensional digital images.
The IRIS 1.0 System Is designed for use by health
care professionals and is intended to assist the
clinician who is responsible for making all final
patient management decisions. | SAME intended use.
DIFFERENCE in Indications for use:
The machine learning enabled kidney CT
auto-segmentation tool is intended for use
for adult patients with contrast-enhanced,
axial kidney CT images with slice thickness
3mm or less.
IRISeg is intended for use by qualified
professionals. | |
| Description | Predicate Device (K182643) -
IRISeg as part of IRIS 1.0 System | Subject Device
IRISeg |
| Regulation Number | 21 CFR §892.2050 | 21 CFR §892.2050 |
| Classification | Class II | Class II |
| Product Code | Primary: LLZ | Primary: QIH; Associate: LLZ |
| Prescription use | Rx only | Rx only |
| Host Hardware
Compatibility | General-purpose computer
hardware | General-purpose computer
hardware |
| Intended population | Adult patients | Adult patients |
| Intended Users | Healthcare Professionals | Qualified Professionals |
| Intended Clinical
Decision Support | The output file can be used to
render a 3D model for
preoperative surgical planning
and intraoperative display.
The output file is meant for
visual, non-diagnostic use and
shall be reviewed by clinicians
who are responsible for all final
patient management decisions. | Equivalent to the predicate
device |
| Principles of Operations
/ Workflow | Manual segmentation alone
Auto-segmentation followed by
manual segmentation. | Equivalent to the predicate
device |
| User interface /
Environment | Graphical user interface design.
Office setting (Segmentation
Software Application running on
a general-purpose computer) | Equivalent to the predicate
device |
| Supported Input | DICOM-Compliant CT scans
Axial views, contrast enhanced | Equivalent to the predicate
device |
| Supported Output | Segmentation files that can be
used to render a 3D model for
preoperative surgical planning
and intraoperative display | Equivalent to the predicate
device |
| Supported Segmentation
Structures | 5 Kidney CT structures:
Parenchyma, Artery, Vein,
Collecting System and Mass | Equivalent to the predicate
device |
| ML Auto-Segmentation
Structures | 5 Kidney CT structures:
Parenchyma, Artery, Vein,
Collecting System and Mass | 4 Kidney CT structures:
Parenchyma, Artery, Vein and
Collecting System |
| Description | Predicate Device (K182643) -
IRISeg as part of IRIS 1.0 System | Subject Device
IRISeg |
| Manual Tools | Various segmentation and
selection tools for manual
segmentation. | Equivalent to the predicate
device |
| 2D and 3D Visualization
Features | Volume rendering, 3D model
visualization, 2D slice
visualization. | Equivalent to the predicate
device |
| Segmentation Support
Features | Metadata viewing | Equivalent to the predicate
device |
Comparison of Indications for Use and intended Use
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Comparison of Device Characteristics
Image /page/7/Picture/2 description: The image shows the word "INTUITIVE" in blue font. There is a small symbol above the word. The font is sans-serif and the word is centered in the image. The letters are evenly spaced and the word is easy to read.
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7. RISK MANAGEMENT (SAFETY)
Risk is managed in compliance with ISO 14971, to identify and provide mitigation of potential hazards throughout the software development life cycle (SDLC). Risks related to IRISeg ML-based autosegmentation algorithm is managed by following the AAMI CR34971 Guidance on the Application of ISO 14971 to Artificial Intelligence and Machine Learning. Clinical risk analysis, comparative task analysis, use-related risk analysis, Cybersecurity Risk Analysis, and Risk Assessment of off-The-Shelf Software are conducted, and the risks are mitigated throughout the SDLC.
8. PERFORMANCE DATA (EFFECTIVENESS)
Performance testing data demonstrate that the subject device is substantially equivalent to the segmentation software application in the predicate device IRIS 1.0 System (cleared under K182643), and the design output meets the design input requirements. The performance testing included software verification, machine-learning algorithm performance testing, cybersecurity testing, and design validation testing.
Software Verification and Validation
Software verification and validation have been performed in accordance with IEC 62304 Edition 1.1 2015-06 Consolidated Version. Software documentation has been provided according to FDA's Guidance, Content of Premarket Submissions for Device Software Functions" (June 14, 2023). IRISeg underwent software verification and validation testing. The software testing, integration testing, functional testing, usability testing, segmentation performance testing, as well as the testing for clinical and usability risks. Acceptance criteria were based on the requirements and intended use of IRISeg. Test results showed that all tests met the acceptance criteria. The software testing results demonstrate the IRISeg meets design specifications and user needs.
Machine Learning Auto-Segmentation Testing
IRISeg includes a ML-enabled kidney CT auto-segmentation algorithm. Intuitive conducted the bench performance testing using clinical kidney CT images to help demonstrate that the proposed device achieved the expected segmentation accuracy performance testing of the algorithm was
9
done using clinical kidney CT scans. A total of 81 kidney CT scans were used to evaluate the algorithm. The test dataset did not include more imaging study from any particular patient. No imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used in testing ensured diversity in patient population, scan parameters and scanner manufacturers. Subgroup analysis was performed to ensure model performance was generalizable.
Performance was evaluated by comparing segmentations generated by the kidney CT machine learning algorithm against segmentations generated by a consensus of three U.S Board Certified Radiologists for the same imaging study. The performance of the machine learning model is characterized by the Sørensen–Dice coefficient (DSC) or the Mean Distance to Agreement (MDA) metric. All testing results are presented on a 95% two-sided t-statistic confidence interval basis. Overall testing results across the entire test dataset are summarized below.
| Test Case | Artery DSC | Parenchyma DSC | Vein DSC | Collecting System
MDA |
|---------------------------|--------------|----------------|--------------|--------------------------|
| Overall Testing
(N=81) | [0.87, 0.90] | [0.95, 0.97] | [0.87, 0.89] | [1.3, 1.9] |
Overall Testing Results
Cybersecurity Testing
The cybersecurity verification and validation testing were conducted, and cybersecurity was evaluated per FDA's Guidance "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions" (September, 2023). Specifically, addressing the following cybersecurity testing areas: security requirement testing, threat mitigation testing, and penetration testing. The cybersecurity verification and validation test results demonstrate the adequacy of the implemented cybersecurity controls.
Labeling
The device labeling contains instructions for use and any necessary cautions to ensure safe and effective use of IRISeg.
Labeling information including the prescription use ("Rx only"), the name and place of business of the manufacturer, device description, indications for use, cybersecurity labeling and transparency labeling (per Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles, June 2024) is provided in the subject device's user manual. The UDI (per 21 CFR 801.50 Labeling requirements for standalone software) is provided on the software About screen.
9. CONCLUSION
The subject device IRISeg and the segmentation software application (part of the predicate device, IRIS 1.0 System (K182643)) are deemed to be substantially equivalent based on indications for use, technological characteristics, and performance testing. IRISeg raises no new questions related to safety or effectiveness.