Spine CAMP™ is a fully-automated software that analyzes X-ray images of the spine to produce reports that contain static and/or motion metrics. Spine CAMP™ can be used to obtain metrics from sagittal plane radiographs of the lumbar and/or cervical spine and it can be used to visualize intervertebral motion via an image registration method referred to as "stabilization." The radiographic metrics can be used to characterize and assess spinal health in accordance with established guidance. For example, common clinical uses include assessing spinal stability, alignment, degeneration, fusion, motion preservation, and implant performance. The metrics produced by Spine CAMP are intended to be used to support qualified and licensed professional healthcare practitioners in clinical decision making for skeletally mature patients of age 18 and above.
Device Story
Spine CAMP™ is fully-automated image processing software for clinical use by healthcare practitioners. It accepts DICOM X-ray images of the spine as input. The device uses an AI engine to perform vertebral body detection, landmark specification, and image registration. It calculates distances, angles, linear/angular displacements, and spinopelvic measurements. It produces reports, annotated images, and visualizations of intervertebral motion (stabilization). Used in clinical settings to support decision-making regarding spinal health, alignment, and implant performance. The output assists clinicians in assessing spinal stability and degeneration. The device is server-based and requires human intervention for final interpretation.
Clinical Evidence
No clinical data. Bench testing only. Performance validated on a dataset of 215 lateral cervical and 232 lateral lumbar radiographs. Statistical correlations and equivalence tests compared subject device outputs against predicate (Spine CAMP v1.0) and reference (QMA) devices, demonstrating statistical equivalence for all evaluated variables.
Technological Characteristics
Server-based software; DICOM input; AI-driven vertebral body detection, landmark specification, and registration. Performs 2D motion analysis and linear/angular measurements. Features include automated reporting and image stabilization. Software utilizes retrained AI models (AI Engine v3.2).
Indications for Use
Indicated for skeletally mature patients age 18+ requiring analysis of sagittal plane lumbar and/or cervical spine X-rays to assess spinal health, including stability, alignment, degeneration, fusion, motion preservation, and implant performance.
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).
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Image /page/0/Picture/0 description: The image shows 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.
Medical Metrics, Inc. Kirk Johnson Director of Regulatory and Quality Affairs 2121 Sage Road Suite 300 HOUSTON, TEXAS 77056
Re: K231668
July 7, 2023
Trade/Device Name: Spine CAMPTM (1.1) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OIH Dated: June 7, 2023 Received: June 7, 2023
Dear Kirk Johnson:
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
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statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) 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 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-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,
Jessica Lamb
Jessica Lamb. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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## Indications for Use
Submission Number (if known)
K231668
Device Name
Spine CAMP™ (1.1)
## Indications for Use (Describe)
Spine CAMP™ is a fully-automated software that analyzes X-ray images of the spine to produce reports that contain static and/or motion metrics. Spine CAMP™ can be used to obtain metrics from sagittal plane radiographs of the lumbar and/or cervical spine and it can be used to visualize intervertebral motion via an image registration method referred to as "stabilization." The radiographic metrics can be used to characterize and assess spinal health in accordance with established quidance. For example, common clinical uses include assessing spinal stability, alignment, degeneration, fusion, motion preservation, and implant performance. The metrics produced by Spine CAMP are intended to be used to support qualified and licensed professional healthcare practitioners in clinical decision making for skeletally mature patients of age 18 and above.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/3/Picture/0 description: The image contains the logo for Medical Metrics INC. The logo features a stylized graphic to the left of the company name. Below the company name is the tagline "Insight from Imaging."
Medical Metrics, Inc. 2121 Sage Road, Suite 300 Houston, Texas 77056
P 713-850-7500 F 713-850-7527 www.medicalmetrics.com
## 510(K) SUMMARY
# 510(K) #K231668
| Submitter Information [21 CFR 807.929(a)(1)] | |
|-------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Name | Medical Metrics, Inc. |
| Address | 2121 Sage Road, Suite 300 |
| | Houston, Texas 77056 |
| Phone number | +1 713 850-7500 |
| Fax number | +1 713 850-7527 |
| Email address | kjohnson@medicalmetrics.com |
| Establishment Registration<br>Number | Pending 510(k) clearance and marketing of device |
| Name of contact person | Kirk Johnson |
| Date prepared | 07/05/2023 |
| Name of the device [21 CFR 807.92(a)(2)] | |
| Trade name | Spine CAMP™ (1.1) |
| Regulation name | Medical Image Management and Processing System |
| Review panel | Radiology |
| Regulation Number | 892.2050 |
| Product Code | QIH |
| Legally marketed device to which<br>equivalence is claimed<br>[21 CFR 807.92(a)(3)] | Spine CAMP™ v1.0 (K221632) |
| Device description<br>[21 CFR 807.92(a)(4)] | Spine CAMP™ is a fully-automated image processing software<br>device. It is designed to be used with X-ray images and is<br>intended to aid medical professionals in the measurement and<br>assessment of spinal parameters. Spine CAMP™ is capable of<br>calculating distances, angles, linear displacements, angular<br>displacements, and mathematical combinations of these metrics<br>to characterize the morphology, alignment, and motion of the<br>spine. These analysis results are presented in the form of<br>reports, annotated images, and visualizations of intervertebral<br>motion to support their interpretation. |
| Indications for use<br>[21 CFR 807.92(a)(5)] | Spine CAMP™ is a fully-automated software that analyzes X-<br>ray images of the spine to produce reports that contain static<br>and/or motion metrics. Spine CAMP™ can be used to obtain<br>metrics from sagittal plane radiographs of the lumbar and/or<br>cervical spine and it can be used to visualize intervertebral<br>motion via an image registration method referred to as<br>"stabilization." The radiographic metrics can be used to<br>characterize and assess spinal health in accordance with<br>established guidance. For example, common clinical uses<br>include assessing spinal stability, alignment, degeneration,<br>fusion, motion preservation, and implant performance. The<br>metrics produced by Spine CAMP™ are intended to be used to<br>support qualified and licensed professional healthcare<br>practitioners in clinical decision-making for skeletally mature<br>patients of age 18 and above. |
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| Summary of the technological characteristics of the device compared to the predicate device<br>[21 CFR 807.92(a)(6)] | | |
|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
| Feature | Spine CAMP™ v1.1<br>(Subject Device) | Spine CAMP™ v1.0<br>(Predicate Device K221632) |
| Classification Name | Automated Radiological Image<br>Processing Software | Automated Radiological Image<br>Processing Software |
| Product Code | QIH | QIH |
| Runs on Server | Yes | Yes |
| Image Input | DICOM | DICOM |
| Anatomical Area | Spine | Spine |
| Image Processing | Vertebral body detection;<br>Vertebral body landmark<br>specification; Vertebral body<br>registration | Vertebral body detection; Vertebral<br>body landmark specification;<br>Vertebral body registration |
| Linear Measurements | Yes | Yes |
| Angular Measurements | Yes | Yes |
| 2D Motion Analysis | Yes | Yes |
| Image Registration | Yes | Yes |
| Display of Image Alignment /<br>Stabilization | Yes | Yes |
| Clinical Reporting | Yes | Yes |
| Human Intervention for<br>Interpretation | Required | Required |
| Intended User | Trained professionals | Trained professionals |
| Comparison Summary | | |
Spine CAMP™ v1.1 is designed to utilize the same analysis methodology as the predicate device, Spine CAMP™ v1.0 (K221632). The types of inputs and outputs are identical between the two devices. The devices are nearly identical in all respects. The primary differences are:
- Spine CAMP's primary component, the Al Engine, was updated by retraining its Al models with . more imaging for improved generalization and performance. Improvements were also made to the Al Engine's logic to address potential failure modes.
- . Spine CAMP™ v1.1 is able to identify the femoral heads in lateral lumbar X-rays in order to produce spinopelvic measurements.
- . Configuration capabilities were expanded to derive outputs from the existing calculated results and to conditionally format report outputs according to the clinical user's preferences.
#### Performance Data [21 CFR 807.92(b)]
Summary of bench tests (non-clinical) conducted for determination of substantial equivalence [21 CFR 807.92(b)(1)]
Software verification and validation testing was completed to demonstrate functionality of the device across multiple datasets that had not been used to train any of the Al models. The same methodology was utilized for the performance qualification (PQ) tests for both Spine CAMP 1.1 and its primary component, the AI Engine v3.2, as had been previously utilized for the predicate device, Spine CAMP v1.0 and its primary component, the Al Enqine v3.1. The software functioned as intended and all results observed were as expected.
Additional bench testing was performed by evaluating Spine CAMP™ v1.1 performance on a large dataset that was previously analyzed by Spine CAMP™ v1.0. Additionally, this dataset was analyzed by five experienced operators using the reference device, QMA, for spinopelvic measurements that Spine CAMP™ v1.0 was not designed to produce. This dataset included 215 lateral cervical spine
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radiographs and 232 lateral lumbar spine radiographs. Statistical correlations and equivalence tests were performed by directly comparing vertebral landmark coordinates, image calibration, and intervertebral measurements between Spine CAMP™ v1.1 and the predicate device as well as spinopelvic measurements between Spine CAMP™ v1.1 and the reference device. This analysis demonstrated correlation and statistical equivalence for all variables evaluated.
Summary of clinical tests conducted for determination of substantial equivalence or of clinical information [21 CFR 807.92(b)(2)]
This section is not applicable to this submission. Clinical Data are not included.
## Conclusions drawn [21 CFR 807.92(b)(3)]
Spine CAMP™ is as safe and effective as the predicate device. The subject device has the same intended use and indications for use as its predicate device. Their technological characteristics and principles of operations are nearly identical. The minor differences between the subject and predicate devices (i.e., retrained Al models and enhanced reporting capabilities) do not raise new or different questions regarding safety and effectiveness when used as labeled. The same performance testing methodology that was used utilized to test the subject device as had been used to test the predicate device. Specifically, for a large dataset of images, the subject device and the predicate device produced outputs that were statistically 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.