K231955 · Carlsmed, Inc. · QIH · Nov 3, 2023 · Radiology
Device Facts
Record ID
K231955
Device Name
aprevo® Digital Segmentation
Applicant
Carlsmed, Inc.
Product Code
QIH · Radiology
Decision Date
Nov 3, 2023
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2050
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Spine segmentation
—
IOU > 0.80
IOU > 0.80
—
—
—
—
Vertebral body labeling
—
Accuracy > 0.90
Accuracy > 0.90
—
—
—
—
Indications for Use
aprevo® Digital Segmentation software is intended to be used by trained, medically knowledgeable design personnel to perform digital image segmentation of the spine, primarily lumbar anatomy. The device inputs DICOM images and outputs a 3-D model of the spine.
Device Story
Software device processes DICOM images to generate 3D models of spine anatomy; primarily lumbar. Workflow: pre-processing filters soft tissue; AI-based algorithm segments spine structure; renders 3D model. Operated by trained, medically knowledgeable design personnel. Output reviewed via third-party software. Facilitates digital segmentation for clinical design workflows; benefits include automated, accurate vertebral body identification and segmentation.
Clinical Evidence
No clinical data. Bench testing only. Performance evaluated using independent training and validation datasets. Segmentation accuracy measured by Intersection over Union (IOU) score (>80%). Vertebral body labeling accuracy >90%; sensitivity and specificity >80%.
Technological Characteristics
Software-based medical image processing system. Inputs: DICOM. Outputs: 3D models. Core technology: AI-based segmentation algorithm. Standalone software deployment. Developed per FDA software guidance.
Indications for Use
Indicated for trained, medically knowledgeable design personnel performing digital image segmentation of the spine, primarily lumbar anatomy, using DICOM input to generate 3D models.
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 & 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.
Carlsmed, Inc. Karen Liu VP Quality and Regulatory 1800 Aston Ave. Suite 100 SAN DIEGO, CALIFORNIA 92008
Re: K231955
November 3, 2023
Trade/Device Name: aprevo® Digital Segmentation Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: September 28, 2023 Received: October 5, 2023
Dear Karen Liu:
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/cdrb/cfdocs/cfpmn/pmn.cfm identifies.combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
U.S. Food & Drug Administration 10903 New Hampshire Avenue Silver Spring, MD 20993 www.fda.gov
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ements, the Quality System (QS) regulation (21 CFR Part
Page 2
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about 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).
Sincerelv.
Jessica Lamb
Jessica Lamb Assistant Director DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
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#### DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
## Indications for Use
Submission Number (if known)
K231955
Device Name
#### aprevo® Digital Segmentation
#### Indications for Use (Describe)
aprevo® Digital Segmentation software is intended to be used by trained, medically knowledgeable design personnel to perform digital image segmentation of the spine, primarily lumbar anatomy. The device inputs DICOM images and outputs a 3-D model of the spine.
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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#### K231955 510(K) SUMMARY
| Submitter's Name: | Carlsmed, Inc. |
|-----------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|
| Submitter's Address: | 1800 Aston Ave, Ste 100<br>Carlsbad, CA 92008 |
| Submitter's Telephone: | 760-766-1926 |
| Contact Person: | Karen Liu, VP Quality and Regulatory<br>Carlsmed, Inc.<br>1800 Aston Avenue Suite 100<br>Carlsbad, CA 92008<br>760-766-1926<br>regulatory@carlsmed.com |
| Date Summary was Prepared: | October 31, 2023 |
| Trade or Proprietary Name: | aprevo® Digital Segmentation |
| Predicate Clearance Numbers<br>and Name | K183105, Mimics Medical |
| Reference Device Number<br>and Name | K202034 aprevo™ Intervertebral Body Fusion Device<br>K201232 Limbus Contour |
| Common or Usual Name: | Medical Image Management and Processing System |
| Classification: | Class II per 21 CFR §892.2050 |
| Product Code: | QIH |
| Classification Panel: | Radiology |
## DESCRIPTION OF THE DEVICE SUBJECT TO PREMARKET NOTIFICATION:
The device is a software medical device that will use DICOM images as input and provide 3D model of the spine structure. Pre-processing will be performed on the uploaded DICOM files to filter soft tissue and identifying spine. Upon removal of soft tissue and identification of spine structure, the software will utilize an AI-based algorithm to segment the structure and render a 3D model as an output.
## INDICATIONS FOR USE
aprevo® Digital Segmentation software is intended to be used by trained, medically knowledgeable design personnel to perform digital image segmentation of the spine, primarily lumbar anatomy. The device inputs DICOM images and outputs a 3-D model of the spine.
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# TECHNOLOGICAL CHARACTERISTICS
The aprevo® Digital Segmentation will allow the user to import, visualize and segment medical images, check and correct the segmentations, and create digital 3D models. The software functionality is equivalent to the predicate device (Mimics Medical, K183105) from intended use and technological characteristics, and does not raise any new question of safety and effectiveness.
## SUBSTANTIAL EQUIVALENCE
The subject device, aprevo® Digital Segmentation is software intended to segment spine bony structure in an automated manner. The device has similar intended use and technological characteristics to its predicate device Mimics Medical. The table below includes detail on comparison between the subject device to its predicate device, Mimics Medical (K183105)
| Characteristic | Subject Device | Predicate Device | Differences |
|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
| Name | aprevo® Digital<br>Segmentation | Mimics Medical | |
| Clearance<br>Number | K231955 | K183105 | |
| Regulation<br>Number | 892.2050 | 892.2050 | Identical |
| Product Code | QIH | LLZ | Similar: both are Image<br>Processing Systems |
| Indications For<br>Use | aprevo® Digital<br>Segmentation software<br>is intended to be used<br>by trained, medically<br>knowledgeable design<br>personnel to perform<br>digital image<br>segmentation of the<br>spine, primarily<br>lumbar anatomy. The<br>device inputs DICOM<br>images and outputs a<br>3-D model of the<br>spine. | Mimics Medical is<br>intended for use as a<br>software interface and<br>image segmentation system<br>for the transfer of medical<br>imaging information to an<br>output file. Mimics<br>Medical is also intended for<br>measuring and treatment<br>planning.<br>The Mimics Medical<br>output can be used for the<br>fabrication of physical<br>replicas of the output file<br>using traditional or additive<br>manufacturing methods.<br>The physical replica can be<br>used for diagnostic<br>purposes in the field of<br>orthopaedic, maxillofacial<br>and cardiovascular<br>applications.<br>Mimics Medical should be<br>used in conjunction with<br>expert clinical judgment. | Similar. The subject device<br>has a narrower indication for<br>use compared to the<br>predicate device. |
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| Characteristic | Subject Device | Predicate Device | Differences | | | |
|-----------------------------------|--------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--|--|--|
| Technical Characteristics | | | | | | |
| Compatible<br>Input File<br>Types | DICOM | DICOM and standard<br>imaging formats (such as<br>RAW, TIFF, BMP and<br>jpeg format) | Similar. The subject device<br>has narrower input file types. | | | |
| Segmentation<br>Functionality | Automatic Spinal<br>Algorithm | Manual tools, Semi-<br>automatic tools, and<br>automatic algorithms | Similar. Both devices<br>include automated spine<br>segmentation tool. | | | |
| User<br>Interaction | User cannot review<br>and edit segmentation<br>and 3D models | Contains tools to review<br>and edit segmentation and<br>3D models | Similar: The output of both<br>devices can be reviewed by<br>the user. While the predicate<br>device has a viewer to<br>review and edit<br>segmentation outputs the<br>subject device output does<br>not include any viewer but<br>its outputs can be reviewed<br>as part of the entire<br>workflow utilizing 3rd party<br>software. | | | |
| 3D Model<br>Generation | The subject device<br>generates a 3D model | The predicate device<br>generates a 3D model | Identical | | | |
| Export Outputs | The subject device<br>generates an output<br>file. | The predicate device<br>generates an output file. | Identical | | | |
| Intended User<br>Population | Trained Personnel,<br>Knowledgeable in<br>Medicine | Trained personnel,<br>knowledgeable in medicine | Identical | | | |
## NON-CLINICAL TESTING
The aprevo® Digital Segmentation has been evaluated in accordance with internal software specifications and applicable performance standards through the software development and verification and validation procedures to ensure performance according to specifications, user requirements, and the FDA guidance document Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices.
Independent training and validation datasets were selected to ensure model performance would reflect real clinical performance. Validation datasets represented diversity in populations and equipment.
The software performance was evaluated using an IOU (intersection over union) score for segmentation which exceeded the acceptance criteria of 80%. It was also evaluated for accuracy of vertebral body labeling which exceeded the acceptance criteria of 90% overall, with sensitivity and specificity exceeding 80% each. Additionally, the performance was evaluated across key cohorts.
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# CLINICAL TESTING
Not applicable.
# CONCLUSION
The overall indications for use and technology characteristics of the aprevo® Digital Segmentation are similar to the primary predicate device. This leads to the conclusion that the proposed aprevo® Digital Segmentation is substantially equivalent to its primary predicate from the safety and effectiveness perspective.
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.