(148 days)
Not Found
Yes
The device description explicitly states that the image analysis uses Artificial Intelligence (AI) technology and specifically utilizes a machine learning (ML) algorithm to compute segmentations of nodules.
No
The device is a diagnostic tool that aids in the analysis and review of images by providing measurements; it does not treat or cure any condition.
Yes
The "Device Description" explicitly states, "NinesMeasure is a semi-automatic, diagnostic patient imaging tool used to measure the size of selected pulmonary nodules in a radiological image."
Yes
The device description explicitly states "The software system is comprised of a set of software modules" and describes its function as operating over a network interface to receive and process DICOM images and return measurements. There is no mention of accompanying hardware components that are part of the device itself.
No, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens taken from the human body, such as blood, urine, or tissue, to provide information for diagnosis, monitoring, or screening.
- NinesMeasure's Function: NinesMeasure analyzes medical images (CT scans), not biological specimens. It provides quantitative measurements of pulmonary nodules based on image data.
- Intended Use: The intended use clearly states it's a "semi-automatic tool indicated for use by trained radiologists to aid in the analysis and review of adult thoracic CT images." This focuses on image analysis and measurement, not the analysis of biological samples.
Therefore, based on the provided information, NinesMeasure falls under the category of a medical imaging analysis software or a diagnostic imaging tool, not an In Vitro Diagnostic device.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The section "Control Plan Authorized (PCCP) and relevant text" explicitly states "Not Found".
Intended Use / Indications for Use
NinesMeasure is a semi-automatic tool indicated for use by trained radiologists to aid in the analysis and review of adult thoracic CT images. NinesMeasure provides quantitative information about pulmonary nodule size on a single study or over the time course of several thoracic studies by providing long and short axis diameter measurements in the axial plane.
Based on analysis of DICOM images and provided input from a radiologist, indicating the location of the pulmonary nodule, the device uses artificial intelligence algorithms to automatically perform the measurements, and allows the axial measurements to be displayed and reviewed. NinesMeasure is limited for use on solid pulmonary nodules.
The device is intended to be used as a measurement tool by a trained radiologist and is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis. The device does not alter the original medical image.
Product codes
LLZ
Device Description
NinesMeasure is a semi-automatic, diagnostic patient imaging tool used to measure the size of selected pulmonary nodules in a radiological image. The software system is comprised of a set of software modules for performing image analysis at a specified image location to calculate measurements of pulmonary nodules on adult thoracic CT images. The system operates over a standard network interface and receives the DICOM images and coordinates of the pulmonary nodule to measure. The system then returns the measurements for the long and short axis diameters for review by a trained radiologist.
NinesMeasure is designed to be used with a standard PACS, where the user can indicate a location of the pulmonary nodule to measure, and then review and edit the measurements on the DICOM image.
The image analysis uses Artificial Intelligence (Al) technology to analyze chest CT images for computing the measurements. Specifically, the device utilizes a machine learning (ML) algorithm to compute segmentations of nodules, from which the long and short axis measurements are then calculated.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
CT
Anatomical Site
Thoracic, Chest (lung)
Indicated Patient Age Range
Adult
Intended User / Care Setting
Trained radiologists
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
The test dataset was diverse, and included 3 different major scanner manufacturers, 7 different scanner models, 11 different clinical sites.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
The algorithm performance was validated with a retrospective, multi-center image comparison study. The study was performed to evaluate the NinesMeasure device and demonstrate the product's performance as a workflow tool for pulmonary nodule measurement consistent with the proposed indications for use.
Primary Endpoint - All Nodules:
- Normalized error on long axis diameter [95% CI]: 0.113 [Upper Bound 0.124]
- Normalized error on short axis diameter [95% CI]: 0.131 [Upper Bound 0.143]
Primary Endpoint stratified by nodule size:
- Nodule size 3-6 mm (100 nodules): Normalized error on long axis diameter [95% CI] 0.104 [Upper Bound: 0.119], Normalized error on short axis diameter [95% CI] 0.123 [Upper Bound: 0.139]
- Nodule size 6-8 mm (63 nodules): Normalized error on long axis diameter [95% CI] 0.119 [Upper Bound: 0.138], Normalized error on short axis diameter [95% CI] 0.143 [Upper Bound: 0.161]
- Nodule size 8-10 mm (46 nodules): Normalized error on long axis diameter [95% CI] 0.13 [Upper Bound: 0.156], Normalized error on short axis diameter [95% CI] 0.133 [Upper Bound: 0.166]
The performance goals for the primary endpoints were met.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Normalized error on long axis diameter, Normalized error on short axis diameter.
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
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
February 25, 2021
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: a symbol on the left and the FDA acronym with the agency's name on the right. The symbol on the left is a stylized representation of a human figure, while the right side features the FDA acronym in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
Nines, Inc. % John J. Smith, M.D., J.D. Regulatory Counsel Hogan Lovells US LLP 555 13th Street. NW WASHINGTON DC 20004
Re: K202990
Trade/Device Name: NinesMeasure Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: January 22, 2021 Received: January 22, 2021
Dear Dr. 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 (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 and Part 809); medical device reporting of medical device-related adverse events) (21 CFR
1
- for 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
2
510(k) Number (if known)
K202990
Device Name
NinesMeasure
Indications for Use (Describe)
NinesMeasure is a semi-automatic tool indicated for use by trained radiologists to aid in the analysis and review of adult thoracic CT images. NinesMeasure provides quantitative information about pulmonary nodule size on a single study or over the time course of several thoracic studies by providing long and short axis diameter measurements in the axial plane.
Based on analysis of DICOM images and provided input from a radiologist, indicating the location of the pulmonary nodule, the device uses artificial intelligence algorithms to automatically perform the measurements, and allows the axial measurements to be displayed and reviewed. NinesMeasure is limited for use on solid pulmonary nodules.
The device is intended to be used as a measurement tool by a trained radiologist and is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis. The device does not alter the original medical image.
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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3
510(k) SUMMARY Nines, Inc.'s NinesMeasure K202990
Submitter:
Nines, Inc 329 Alma Street Palo Alto, CA 94301
Contact Person:
Dr. Russell Stewart Phone: 650 924 6159 russell@nines.com
Date Prepared: January 22, 2021
Name of Device: NinesMeasure
Classification Name: System, Image processing, Radiological
Regulatory Class: Class II
Product Code: LLZ
Predicate Device: Philips Medical Systems Nederland B.V.'s Lung Nodule Assessment and Comparison Option (LNA) (K162484)
Device Description
NinesMeasure is a semi-automatic, diagnostic patient imaging tool used to measure the size of selected pulmonary nodules in a radiological image. The software system is comprised of a set of software modules for performing image analysis at a specified image location to calculate measurements of pulmonary nodules on adult thoracic CT images. The system operates over a standard network interface and receives the DICOM images and coordinates of the pulmonary nodule to measure. The system then returns the measurements for the long and short axis diameters for review by a trained radiologist.
NinesMeasure is designed to be used with a standard PACS, where the user can indicate a location of the pulmonary nodule to measure, and then review and edit the measurements on the DICOM image.
The image analysis uses Artificial Intelligence (Al) technology to analyze chest CT images for computing the measurements. Specifically, the device utilizes a machine learning (ML) algorithm to compute segmentations of nodules, from which the long and short axis measurements are then calculated.
4
Intended Use / Indications for Use
NinesMeasure is a semi-automatic tool indicated for use by trained radiologists to aid in the analysis and review of adult thoracic CT images. NinesMeasure provides quantitative information about pulmonary nodule size on a single study or over the time course of several thoracic studies by providing long and short axis diameter measurements in the axial plane.
Based on analysis of DICOM images and provided input from a radiologist, indicating the location of the pulmonary nodule, the device uses artificial intelligence algorithms to automatically perform the measurements, and allows the axial measurements to be displayed and reviewed. NinesMeasure is limited for use on solid pulmonary nodules.
The device is intended to be used as a measurement tool by a trained radiologist and is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis. The device does not alter the original medical image.
Summary of Technological Characteristics
The NinesMeasure has similar technological characteristics as the predicate. Both devices utilize image processing algorithms that calculate pulmonary nodule measurements and return the measurements to the workstation. Although the predicate is cleared for multiple features in addition to pulmonary nodule measurements, these minor differences do not impact the safety of the subject device.
| | NinesMeasure
K202990 | Philips Medical Systems'
Lung Nodule Assessment and
Comparison Option
(LNA)(K162484) |
|-------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Classification Name | System, Image
processing,
Radiological | System, Image
processing,
Radiological |
| Device Class | Class II | Class II |
| Classification Panel | Radiology | Radiology |
| Product Code | LLZ | LLZ, JAK |
| Regulation
Description | Radiological
Image Processing
Software | Radiological
Image Processing
Software |
| Regulation
Number | 21 CFR 892.2050 | 21 CFR 892.2050
21 CFR 892.1750 |
| Indications for Use | NinesMeasure is a semi-
automatic tool indicated for use
by trained radiologists to aid in
the analysis and review of adult
thoracic CT images.
NinesMeasure provides
quantitative information about
pulmonary nodule size on a | The Lung Nodule Assessment
and Comparison Option is
intended for use as a diagnostic
patient-imaging tool. It is
intended for the review and
analysis of thoracic CT images,
providing quantitative and
characterizing information about |
| | | |
| | single study or over the time
course of several thoracic
studies by providing long and
short axis diameter
measurements in the axial
plane.
Based on analysis of DICOM
images and provided input from
a radiologist, indicating the
location of the pulmonary
nodule, the device uses artificial
intelligence algorithms to
automatically perform the
measurements, and allows the
axial measurements to be
displayed and reviewed.
NinesMeasure is limited for use
on solid pulmonary nodules.
The device is intended to be
used as a measurement tool by
a trained radiologist and is
limited to analysis of imaging
data and should not be used in-
lieu of full patient evaluation or
relied upon to make or confirm a
diagnosis. The device does not
alter the original medical image. | nodules in the lung in a single
study, or over the time course of
several thoracic studies.
Characterizations include
diameter, volume and volume
over time. The system
automatically performs the
measurements, allowing lung
nodules and measurements to
be displayed. |
| User Population | Radiologists | Radiologists and Technologist |
| Technological Characteristics | Image processing algorithms
computing pulmonary nodule
measurements and returning
computed measurements to the
workstation. | Image processing algorithms
computing pulmonary nodule
measurements and returning
computed measurements to the
workstation. |
| Components | Image processing algorithms for
nodule measurement | -Image processing algorithms
-Display, comparison, and risk
calculations |
| Anatomical region of interest | Chest | Chest |
| Features | -long axis measurement
-short axis measurement
(perpendicular to long axis) | -long axis measurement
-short axis measurement
(perpendicular to long axis)
-Average/Max 3D/Effective
diameter (mm)
-Volume (mm³)
-Mean Densities (HU)
-Segmentation of lung airway,
lungs and lung lobes
-Single click lung nodule
segmentation
-Nodule Characteristics
-Comparison and matching
-Automatic calculation of |
| | doubling time, percent and
absolute change of all numerical
parameters
-Reporting results functions
including dictation table, patient
related information, LungRads,
and Risk Calculator
-Printing option | |
A table comparing the key features of the subject and predicate device is provided below.
5
6
Performance Testing
Nines performed software verification and validation testing that covers the performance of the algorithms, as well as the performance of the software and its components. In all instances, NinesMeasure functioned as intended and expected.
The algorithm performance was validated with a retrospective, multi-center image comparison study. The study was performed to evaluate the NinesMeasure device and demonstrate the product's performance as a workflow tool for pulmonary nodule measurement consistent with the proposed indications for use. The test dataset was diverse, and included 3 different major scanner manufacturers, 7 different scanner models, 11 different clinical sites.
The primary endpoints of the algorithm are listed below:
| Primary Endpoint - All Nodules | Result |
|---|---|
| Normalized error on long axis diameter | |
| [95% CI] | 0.113 |
| [Upper Bound 0.124] | |
| Normalized error on short axis diameter | |
| [95% CI] | 0.131 |
| [Upper Bound 0.143] |
The primary endpoint stratified by nodule size is listed below:
| Nodule size | Number of nodules | Normalized error on
long axis diameter
[95% CI] | Normalized error on
short axis diameter
[95% CI] |
|-------------|-------------------|-------------------------------------------------------|--------------------------------------------------------|
| 3-6 mm | 100 | 0.104 [Upper Bound: 0.119] | 0.123 [Upper Bound: 0.139] |
| 6-8 mm | 63 | 0.119 [Upper Bound: 0.138] | 0.143 [Upper Bound: 0.161] |
| 8-10 mm | 46 | 0.13 [Upper Bound: 0.156] | 0.133 [Upper Bound: 0.166] |
The performance goals for the primary endpoints were met.
Based on the clinical performance as documented in the clinical study, the subject software has a safety and effectiveness profile that is similar to the predicate device.
7
Conclusions
NinesMeasure has the same intended uses 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 technological differences between NinesMeasure and its predicate device raise no new issues of safety or effectiveness. Performance data demonstrate that NinesMeasure performs as intended. Thus, NinesMeasure is substantially equivalent.