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.
Device Story
NinesMeasure is a semi-automatic diagnostic tool for radiologists; operates via standard network interface within PACS environments. Input: DICOM thoracic CT images and radiologist-provided nodule location coordinates. Processing: Machine learning (ML) algorithms perform nodule segmentation and compute long/short axis diameters. Output: Quantitative measurements displayed on the original DICOM image for radiologist review/edit. Clinical utility: Assists in pulmonary nodule size assessment; does not replace full patient evaluation or diagnostic confirmation. Benefits: Standardizes measurement workflow; provides quantitative data for longitudinal monitoring of solid nodules.
Clinical Evidence
Retrospective, multi-center study using 209 nodules across 11 sites and 3 scanner manufacturers. Primary endpoints: normalized error on long and short axis diameters. Results: Normalized error for long axis was 0.113 (95% CI upper bound 0.124) and for short axis was 0.131 (95% CI upper bound 0.143). Performance goals met across all nodule size strata (3-6mm, 6-8mm, 8-10mm).
Technological Characteristics
Software-based image processing tool; operates on standard network interface; DICOM-compatible. Uses machine learning algorithms for nodule segmentation and measurement. Standalone software module for integration with PACS. No hardware components.
Indications for Use
Indicated for trained radiologists to aid in analysis/review of adult thoracic CT images; provides quantitative long/short axis diameter measurements of solid pulmonary nodules in the axial plane on single or longitudinal studies.
Regulatory Classification
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.
Special Controls
*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).
Predicate Devices
Philips Medical Systems Nederland B.V.'s Lung Nodule Assessment and Comparison Option (LNA) (K162484)
Submission Summary (Full Text)
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February 25, 2021
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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
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803) 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
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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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# 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.
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# 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<br>K202990 | Philips Medical Systems'<br>Lung Nodule Assessment and<br>Comparison Option<br>(LNA)(K162484) |
|-------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Classification Name | System, Image<br>processing,<br>Radiological | System, Image<br>processing,<br>Radiological |
| Device Class | Class II | Class II |
| Classification Panel | Radiology | Radiology |
| Product Code | LLZ | LLZ, JAK |
| Regulation<br>Description | Radiological<br>Image Processing<br>Software | Radiological<br>Image Processing<br>Software |
| Regulation<br>Number | 21 CFR 892.2050 | 21 CFR 892.2050<br>21 CFR 892.1750 |
| Indications for Use | NinesMeasure is a semi-<br>automatic tool indicated for use<br>by trained radiologists to aid in<br>the analysis and review of adult<br>thoracic CT images.<br>NinesMeasure provides<br>quantitative information about<br>pulmonary nodule size on a | The Lung Nodule Assessment<br>and Comparison Option is<br>intended for use as a diagnostic<br>patient-imaging tool. It is<br>intended for the review and<br>analysis of thoracic CT images,<br>providing quantitative and<br>characterizing information about |
| | | |
| | single study or over the time<br>course of several thoracic<br>studies by providing long and<br>short axis diameter<br>measurements in the axial<br>plane.<br>Based on analysis of DICOM<br>images and provided input from<br>a radiologist, indicating the<br>location of the pulmonary<br>nodule, the device uses artificial<br>intelligence algorithms to<br>automatically perform the<br>measurements, and allows the<br>axial measurements to be<br>displayed and reviewed.<br>NinesMeasure is limited for use<br>on solid pulmonary nodules.<br>The device is intended to be<br>used as a measurement tool by<br>a trained radiologist and is<br>limited to analysis of imaging<br>data and should not be used in-<br>lieu of full patient evaluation or<br>relied upon to make or confirm a<br>diagnosis. The device does not<br>alter the original medical image. | nodules in the lung in a single<br>study, or over the time course of<br>several thoracic studies.<br>Characterizations include<br>diameter, volume and volume<br>over time. The system<br>automatically performs the<br>measurements, allowing lung<br>nodules and measurements to<br>be displayed. |
| User Population | Radiologists | Radiologists and Technologist |
| Technological Characteristics | Image processing algorithms<br>computing pulmonary nodule<br>measurements and returning<br>computed measurements to the<br>workstation. | Image processing algorithms<br>computing pulmonary nodule<br>measurements and returning<br>computed measurements to the<br>workstation. |
| Components | Image processing algorithms for<br>nodule measurement | -Image processing algorithms<br>-Display, comparison, and risk<br>calculations |
| Anatomical region of interest | Chest | Chest |
| Features | -long axis measurement<br>-short axis measurement<br>(perpendicular to long axis) | -long axis measurement<br>-short axis measurement<br>(perpendicular to long axis)<br>-Average/Max 3D/Effective<br>diameter (mm)<br>-Volume (mm³)<br>-Mean Densities (HU)<br>-Segmentation of lung airway,<br>lungs and lung lobes<br>-Single click lung nodule<br>segmentation<br>-Nodule Characteristics<br>-Comparison and matching<br>-Automatic calculation of |
| | doubling time, percent and<br>absolute change of all numerical<br>parameters<br>-Reporting results functions<br>including dictation table, patient<br>related information, LungRads,<br>and Risk Calculator<br>-Printing option | |
A table comparing the key features of the subject and predicate device is provided below.
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# 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<br>[95% CI] | 0.113<br>[Upper Bound 0.124] |
| Normalized error on short axis diameter<br>[95% CI] | 0.131<br>[Upper Bound 0.143] |
The primary endpoint stratified by nodule size is listed below:
| Nodule size | Number of nodules | Normalized error on<br>long axis diameter<br>[95% CI] | Normalized error on<br>short axis diameter<br>[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.
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# 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.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.