LungMaps is a post-processing software application for supporting the evaluation of lung perfusion and ventilation based on Magnetic Resonance Imaging (MRI). The software can be used for the creation of maps and statistical evaluations providing information on lung perfusion and ventilation. Results when interpreted by a radiologist yield information that may assist in diagnosis. The results shall not be used as only source for diagnosis. LungMaps is intended for patients aged 6 years and older who require evaluation of lung perfusion and/or ventilation.
Device Story
LungMaps is a post-processing software application (OpenApp) for MRI lung analysis; operates on the syngo.via platform. Inputs: 2D time series of lung MR images. Processing: deep learning-based U-Net algorithm performs automated lung segmentation; generates perfusion-weighted and ventilation-weighted maps; calculates statistics. Output: visual maps and statistical data for radiologist review. Used in clinical settings by healthcare professionals to assist in diagnosis; not for use as the sole diagnostic source. Benefits: provides quantitative/visual assessment of lung function to support clinical decision-making.
Clinical Evidence
No clinical tests performed. Bench testing only. Algorithm validated using 342 MR lung images (0.55T, 1.5T, 3T; ages 6-85). Primary endpoint: Dice coefficient (average 0.91). Secondary metrics: Hausdorff distance (4.1 pixels), average surface distance (0.8 pixels), volumetric differences. Training set: 1912 images from 193 subjects. Results correlate with standard-of-care techniques (DCE MRI, SPECT, 129Xe imaging).
Technological Characteristics
Post-processing software; operates on syngo.via platform. Deep learning segmentation (U-Net architecture). Conforms to IEC 62304 (software life cycle), ISO 14971 (risk management), and IEC 62366-1 (usability). Version VA10A.
Indications for Use
Indicated for patients aged 6 years and older requiring evaluation of lung perfusion and/or ventilation via MRI.
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
syngo.MR Neuro fMRI – a part of syngo.MR Neurology (K182904)
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**FDA** **U.S. FOOD & DRUG**
ADMINISTRATION
June 15, 2026
Siemens Healthineers AG
Milind Dhamankar
Clinical Affairs Professional
Siemens Medical Solutions USA, Inc.
40 Liberty Blvd.
Malvern, Pennsylvania 19355
Re: K253690
Trade/Device Name: LungMaps
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH, LNH
Dated: May 20, 2026
Received: May 20, 2026
Dear Milind Dhamankar:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13485 clause 8.3 (Nonconforming product), ISO 13485 clause 8.5.2 (Corrective action), and ISO 13485 clause 8.5.3 (Preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and ISO 13485 clause 7.5) and document changes and approvals in the Medical Device File (ISO 13485 clause 4.2.3).
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 (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-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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-devices/device-advice-comprehensive-regulatory-
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assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Digitally signed by Michael D. O'hara -S
Date: 2026.06.15 15:50:18 -04'00'
Michael O'Hara, Ph.D.
Deputy Director
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
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
| Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. | K253690 | ? |
| --- | --- | --- |
| Please provide the device trade name(s). | | ? |
| LungMaps | | |
| Please provide your Indications for Use below. | | ? |
| LungMaps is a post-processing software application for supporting the evaluation of lung perfusion and ventilation based on Magnetic Resonance Imaging (MRI). The software can be used for the creation of maps and statistical evaluations providing information on lung perfusion and ventilation. Results when interpreted by a radiologist yield information that may assist in diagnosis. The results shall not be used as only source for diagnosis. LungMaps is intended for patients aged 6 years and older who require evaluation of lung perfusion and/or ventilation. | | |
| Please select the types of uses (select one or both, as applicable). | ☑ Prescription Use (21 CFR 801 Subpart D) ☐ Over-The-Counter Use (21 CFR 801 Subpart C) | ? |
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SIEMENS
Healthineers
K253690
# 510(k) Summary
This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of the Safe Medical Devices Act 1990 and 21 CFR § 807.92.
1. General Information
Establishment:
Siemens Medical Solutions USA, Inc.
40 Liberty Boulevard
Malvern, PA 19355, USA
Registration Number: 2240869
Date Prepared:
May 18, 2026
Manufacturer:
Siemens Healthineers AG
Magnetic Resonance (MR)
Allee am Röthelheimpark 2
91052 Erlangen
Germany
Registration Number: 3002808157
2. Contact Information
Milind Dhamankar
Clinical Affairs Professional
Siemens Medical Solutions USA, Inc.
40 Liberty Boulevard
Malvern, PA 19355, USA
Phone: +1(610) 517-9484
E-mail: milind.dhamankar@siemens-healthineers.com
3. Device Name and Classification
Device/ Trade name:
LungMaps
Classification Name:
Automated Radiological Image Processing Software
Radiology
Classification Panel:
CFR Code:
21 CFR § 892.2050
Classification:
II
Product Code:
Primary: QIH
Secondary: LNH
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**SIEMENS**
**Healthineers**
#### 4. Legally Marketed
##### Predicate Device$^{1}$
| **Trade name:** | syngo.MR Neuro fMRI – a part of syngo.MR Neurology |
| --- | --- |
| **510(k) Number:** | K182904 |
| **Classification Name:** | Picture Archiving and Communication System (PACS) |
| **Classification Panel:** | Radiology |
| **CFR Code:** | 21 CFR § 892.2050 |
| **Classification:** | II |
| **Product Code:** | Primary: LLZ Secondary: LNH |
##### Reference Device$^{1}$:
| **Trade name:** | Chondral Quant |
| --- | --- |
| **510(k) Number:** | K231351 |
| **Classification Name:** | Picture Archiving and Communication System (PACS) |
| **Classification Panel:** | Radiology |
| **CFR Code:** | 21 CFR § 892.2050 |
| **Classification:** | II |
| **Product Code:** | Primary: LLZ Secondary: LNH |
#### 5. Indications for Use / Intended Use
LungMaps is a post-processing software application for supporting the evaluation of lung perfusion and ventilation based on Magnetic Resonance Imaging (MRI). The software can be used for the creation of maps and statistical evaluations providing information on lung perfusion and ventilation.
Results when interpreted by a radiologist yield information that may assist in diagnosis. The results shall not be used as only source for diagnosis.
LungMaps is intended for patients aged 6 years and older who require evaluation of lung perfusion and/or ventilation.
#### 6. Device Description
The medical device LungMaps is a post-processing software application for supporting the evaluation of lung perfusion and ventilation based on Magnetic Resonance Imaging (MRI). The software can be used for the creation of maps and statistical evaluations providing information on lung perfusion and ventilation.
Version VA10A is the initial version of this medical device.
$^{1}$ The predicate and the reference device have not been subject to a design-related recall.
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SIEMENS Healthineers
LungMaps processes one or multiple 2D time series of the lung MR images and provides perfusion-weighted and ventilation weighted maps of the lung.
The MR LungMaps application offers a range of possibilities to users, including series selection of input data, algorithm processing, data export of results, image, and result visualization, editing tools, and statistics calculation. This application is deployed as an OpenApp.
LungMaps consists of new and modified features that are similar to what is currently offered on the predicate device. The subject device includes the following modifications in comparison to the predicate device:
- New body region compared to predicate device
- Perfusion-weighted and ventilation weighted maps of the lung
- One or multiple 2D time series
# 7. Substantial Equivalence
LungMaps with software version VA10A is substantially equivalent to the following predicate and reference device:
| Predicate Device | FDA Clearance Number and Date | Product Code | Manufacturer |
| --- | --- | --- | --- |
| syngo.MR Neuro fMRI – a part of syngo.MR Neurology | K182904 - July 5, 2019. | LLZ, LNH | Siemens Healthcare GmbH |
| Reference Device | FDA Clearance Number and Date | Product Code | Manufacturer |
| Chondral Quant | K231351– July 13, 2023 | LLZ, LNH | Siemens Healthcare GmbH |
# 8. Technological Characteristics
The subject device LungMaps with software version VA10A is substantially equivalent to the predicate device with regard to the operational environment, programming language, operating system and performance.
The subject device conforms to the standard for medical device software (IEC 62304).
# 9. Nonclinical Tests
The following performance testing was conducted on the subject devices.
| Performance Test | Tested Software | Source/Rationale for test |
| --- | --- | --- |
| Solution Validation Report | LungMaps with software version VA10A | Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices |
| SW Sub-System Verification Test Reports | | |
An automatic lung segmentation algorithm based on deep learning is integrated into the LungMaps analysis. The architecture used for the segmentation task is based on the U-Net
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SIEMENS Healthineers
architecture, which is the established state-of-the art method for medical image segmentation tasks.
To train the network, manual annotations were used that were all reviewed by a radiologist before being used for training, tuning, or testing. For training and testing, the Dice coefficient was selected to optimize the model. The Dice coefficient represents the primary evaluation end point. The Hausdorff distance, the average surface distance, and the volumetric difference are secondary evaluation metrics.
To train the algorithm, 1912 MR lung images were used. Training was performed on 193 subjects from 6 different data sources. Tuning was performed on 46 subjects and 382 images.
To test the algorithm, 342 images were used. On this test set, an average Dice score of 0.91 was achieved. The average and standard deviation (SD) for the Hausdorff distance is 4.1 (SD = 3.9) pixels and the average surface distance is 0.8 (SD = 0.8) pixels. The average absolute volumetric difference is 2.5 (SD = 159.5) and the relative volumetric difference is 3.2 (SD = 21.4).
The data set is a representative data set that included images acquired on 0.55T, 1.5T, and 3T MRI systems. The data set contains subjects with an age distribution from 6 to 85 years.
Any update on the network will only be implemented as a part of a new software version.
The datasets used for training were not used for validating or testing the algorithm.
Non-clinical tests such as unit test, integration testing, and system test are passed.
The system test results indicate that open defects were identified which had no impact on safety and effectiveness of LungMaps with software version VA10A.
## 10. Clinical Tests / Publications
No clinical tests were conducted for the subject device.
Clinical publications and other support documents were referenced to provide information on the use, testing and validation of the Subject Device. LungMaps produces results that are consistent with the implementation that has been evaluated in various scientific publications. The implementation in the scientific publications has been shown to correlate with standard of care techniques like DCE MRI, SPECT and 129Xe imaging and the same holds true for LungMaps.
No animal testing has been performed.
## 11. Safety and Effectiveness
The device labeling contains instructions for use and any necessary cautions and warnings to ensure safe and effective use of the device.
Risk Management is ensured via a risk analysis in compliance with ISO 14971, to identify and provide mitigation of potential hazards early in the design cycle and continuously throughout the development of the product. Siemens Healthineers AG adheres to recognized and established industry standards, to minimize hazards. Furthermore, the device is intended for healthcare professionals.
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**SIEMENS**
**Healthineers**
LungMaps with software version VA10A conforms to the following standards:
| Recognition Number | Product Area | Title of Standard | Reference Number and date | Standards Development Organization |
| --- | --- | --- | --- | --- |
| 5-125 | General I (QS/RM) | Medical devices - Application of risk management to medical devices | 14971 Third Edition 2019-12 | ISO |
| 5-129 | General I (QS/RM) | Medical devices Part 1: Application of usability engineering to medical devices including Amendment 1 | 62366-1:2015+AMD1:2020 (Consolidated Text) | ANSI AAMI IEC |
| 13-79 | Software/Informatics | Medical device software - Software life cycle processes | 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION | IEC |
## 12. Conclusion as to Substantial Equivalence
The extensive testing of LungMaps with software version VA10A has been successfully completed. All risk mitigations, as identified in the Risk Analysis and all relevant requirements for LungMaps with software version VA10A have been tested and verified successfully.
Verification and validation of the product within the meaning of the Quality System Regulation (21 CFR § 820.30) have been performed by trained personnel. LungMaps with software version VA10A has been found to be validated for its intended use.
Indications for Use for subject device is different compared to the predicate device; however, it uses the same hosting platform syngo.via for both the subject device and the predicate device. Both the devices are integrated into the already cleared and marketed general *syngo*.via workflow concept. LungMaps processes perfusion-weighted and ventilation weighted maps of the lung, and predicate device *syngo*.MR Neuro fMRI processes fMRI statistical maps.
The difference between the predicate device and the subject device does not impact the safety and effectiveness of the subject device.
Therefore, it is Siemens’ opinion that the safety and effectiveness of the subject device have been fully verified by objective evidence, and that the subject device performs as safely and effectively as the predicate device (K182904) and the subject device is substantially equivalent to the predicate device.
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.