AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing, and evaluating MR brain images. AI-Rad Companion Brain MR provides the following functionalities: - Automated segmentation and quantitative analysis of includual brain structures and white matter hyperintensities - Quantitative comparison of brain structure with normative data from a healthy population - Presentation of results of reporting that includes all numerical values as well as visualization of these results
Device Story
AI-Rad Companion Brain MR is post-processing software for MR brain images; inputs include T1 MPRAGE and T2 FLAIR datasets. Device performs automated segmentation and volumetric quantification of brain structures and white matter hyperintensities (WMH). It compares patient-specific volumetric data against age-matched normative reference data. Output includes numerical values, 3D overlay maps, and DICOM structured reports archived in PACS. Used in clinical settings by radiologists/healthcare professionals to assist in evaluating brain morphometry and WMH. Enhancements in VA40 version include WMH analysis and updated deployment structure. Device aids clinical decision-making by providing objective, quantitative metrics for brain structure assessment, potentially benefiting patients through improved diagnostic support for neurological diseases.
Clinical Evidence
No clinical trials performed. Performance validated via bench testing using 89 subjects (MS, AD, cognitive impairment, healthy controls). Accuracy assessed against manual ground truth from three radiologists. Results: Volumetric PCC 0.98 (95% CI 0.97-0.99), ICC 0.97 (95% CI 0.96-0.98); Voxel-wise Dice 0.60 (95% CI 0.53-0.63); WMH Lesion-wise F1-score 0.60 (95% CI 0.57-0.64); Reproducibility Dice 0.79 (95% CI 0.77-0.81).
Technological Characteristics
Software-based post-processing system. Inputs: T1 MPRAGE and T2 FLAIR MR images. Architecture: Cloud solution with edge components on customer premises. Connectivity: PACS integration via DICOM SR. Software class: Moderate level of concern. Complies with ISO 14971:2019 for risk management.
Indications for Use
Indicated for clinicians to view, analyze, and evaluate MR brain images. Patient population includes individuals with neurological conditions such as Multiple Sclerosis, Alzheimer's, and cognitive impairment, as well as healthy controls, across a wide age range (19-83).
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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April 15, 2022
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Siemens Healthcare GmBh % Kira Kuzmenchuk Regulatory Affairs Specialist Siemens Medical Solutions USA, Inc. 40 Liberty Blvd., Mail Code 65-1 MALVERN PA 19355
Re: K213706
Trade/Device Name: AI-Rad Companion Brain MR Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: March 11, 2022 Received: March 15, 2022
Dear Kira Kuzmenchuk:
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); medical device reporting of medical device-related adverse events) (21 CFR 803) for
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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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#### Indications for Use
510(k) Number (if known)
Device Name
AI-RAD Companion Brain MR
Indications for Use (Describe)
AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing, and evaluating MR brain images.
Al-Rad Companion Brain MR provides the following functionalities:
- Automated segmentation and quantitative analysis of includual brain structures and white matter hyperintensities
- Quantitative comparison of brain structure with normative data from a healthy population
- Presentation of results of reporting that includes all numerical values as well as visualization of these results
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 FOR AI-Rad Companion Brain MR
Submitted by: Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355 Date Prepared: November 22, 2021
This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of Safe Medical Devices Act of 1990 and 21 CFR §807.92.
## 1. Submitter
| Importer/Distributor | Siemens Medical Solutions USA, Inc.<br>40 Liberty Boulevard<br>Malvern, PA 19355<br>Mail Code: 65-1A<br>Registration Number: 2240869 |
|----------------------|--------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturing Site | Siemens Healthcare GmbH<br>Henkestrasse 127<br>Erlangen, Germany 91052<br>Registration Number: 3002808157 |
#### 2. Contact Person
Kira Kuzmenchuk Regulatory Affairs Specialist Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Mail Code: 65-1A Malvern, PA 19335 Phone: +1 (484) 901 - 9471 Email: kira.kuzmenchuk@siemens-healthineers.com
## 3. Device Name and Classification
| Product Name: | AI-Rad Companion Brain MR |
|----------------------|------------------------------------------------|
| Trade Name: | AI-Rad Companion Brain MR |
| Classification Name: | Medical Image Management and Processing System |
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| Classification Panel: | |
|-----------------------|--|
| CFR Section: | |
| Device Class: | |
| Product Code: | |
Radiology 21 CFR §892.2050 Class II OIH
## 4. Predicate Device
| Product Name: | AI-Rad Companion Brain MR |
|-------------------------|--------------------------------------------|
| Propriety Trade Name: | AI-Rad Companion Brain MR |
| 510(k) Number: | K193290 |
| Clearance Date: | June 17, 2020 |
| Classification Name: | Picture Archiving and Communication System |
| Classification Panel: | Radiology |
| CFR Section: | 21 CFR §892.2050 |
| Secondary CFR Section: | 21 CFR §892.1000 |
| Device Class: | Class II |
| Primary Product Code: | LLZ |
| Secondary Product Code: | LNH |
| Recall Information: | N/A |
## 5. Indications for Use
AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing and evaluating MR brain images.
AI-Rad Companion Brain MR provides the following functionalities:
- Automatic segmentation and quantitative analysis of individual brain structures and white . matter hyperintensities
- Quantitative comparison of each brain structure with normative data from a healthy population
- Presentation of results for reporting that includes all numerical values as well as visualization of these results.
## 6. Device Description
AI-Rad Companion Brain MR VA40 is an enhancement to the predicate. AI-Rad Companion Brain MR VA20 (K193290). Just as in the predicate, AI-Rad Companion Brain MR addresses the automatic quantification and visual assessment of the volumetric properties of various brain structures based on T1 MPRAGE datasets. In AI-Rad Companion Brain MR VA40, the quantification and visual assessment extends to white matter hyperintensities on the basis of T1 MPRAGE and T2 weighted FLAIR datasets. These datasets are acquired as part of a typical head MR acquisition. The results are directly archived in PACS as this is the standard location for reading by radiologist. From a predefined list of 30 structures (e.g. Hippocampus, Left Frontal
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# Healthineer
Grey Matter, etc.), volumetric properties are calculated as absolute and normalized volumes with respect to the total intercranial volume. The normalized values for a given patient are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects.
The white matter hyperintensities can be visualized as a 3D overlay map and the quantification in count and volume as per 4 brain regions in the report.
As an update to the previously cleared device, the following modifications have been made:
- 1. Modified Intended Use Statement
- 2. Addition of white matter hyperintensities overlay map, count and volume as per 4 brain regions
- 3. Enhanced DICOM Structured Report (DICOM SR)
- 4. Updated deployment structure
## 7. Substantially Equivalent (SE) and Technological Characteristics
The intended use of the predicate device and the subject device are equivalent. The main difference is that AI-Rad Companion Brain MR VA40 adds the additional analysis of white matter hyperintensities compared to the predicate, AI-Rad Companion Brain MR VA20.
The subject device, AI-Rad Companion Brain MR VA40 is substantially equivalent with regard to the intended use and technical characteristics compared to the predicate device, AI-Rad Companion Brain MR VA20 (K193290), with respect to the software features, functionalities, and core algorithms. The additional features, enhancements and improvements provided in AI-Rad Companion Brain MR VA40 increase the usability and reduce the complexity of the imaging workflow for the clinical user. The white matter hyperintensity algorithm within AI-Rad Companion Brain MR VA40 is equivalent to the algorithm in icobrain (K192130). Icobrain serves as a reference device within this submission and a dedicated comparison of technological characteristics is provided.
| | Subject Device:<br>AI-Rad Companion<br>Brain MR VA40 | Predicate Device:<br>AI-Rad Companion<br>Brain MR VA20<br>(K193290) | Reference Device:<br>icobrain (K192130) |
|------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|
| Indications for<br>Use | AI-Rad Companion<br>Brain MR is a post-<br>processing image<br>analysis software that<br>assists clinicians in<br>viewing, analyzing, and<br>evaluating MR brain<br>images. | AI-Rad Companion<br>Brain MR is a post-<br>processing image<br>analysis software that<br>assists clinicians in<br>viewing, analyzing, and<br>evaluating MR brain<br>images. | icobrain is intended<br>for automatic<br>labeling,<br>visualization and<br>volumetric<br>quantification of<br>segmentable brain<br>structures |
The risk analysis and non-clinical data support that both devices perform equivalently and do not raise different questions of the safety and effectiveness.
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| | and volumetry of<br>MPRAGE data. | and volumetry of<br>MPRAGE data. | volumetry of<br>MPRAGE data. |
|-------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| Brain<br>Morphometry<br>Quantification | Calculation of label<br>maps (display of brain<br>segmentation) and<br>partially combined label<br>maps (fused with the<br>processed MPRAGE<br>data). | Calculation of label<br>maps (display of brain<br>segmentation) and<br>partially combined<br>label maps (fused with<br>the processed<br>MPRAGE data). | Normalized and<br>unnormalized<br>volume and volume<br>changes of different<br>brain structures. |
| Brain<br>Morphometry:<br>Deviation Map | Calculation of deviation<br>map (representation of<br>brain status in relation to<br>reference data) and<br>partially combined<br>deviation maps (fused<br>with the processed<br>MPRAGE data) User<br>customizable color labels<br>for the overlay map. | Calculation of<br>deviation map<br>(representation of brain<br>status in relation to<br>reference data) and<br>partially combined<br>deviation maps (fused<br>with the processed<br>MPRAGE data) User<br>customizable color<br>labels for the overlay<br>map. | Not available |
| Brain White<br>Matter<br>Hyperintensities<br>Segmentation | Pre-processing<br>functionality for<br>automatic segmentation<br>and volumetry of<br>MPRAGE and FLAIR<br>data. | Not available | Image processing for<br>automatic<br>segmentation and<br>volumetry of FLAIR<br>data. |
| Brain White<br>Matter<br>Hyperintensities<br>Quantification | Calculation of white<br>matter hyperintensities<br>count and volume as per<br>4 brain regions. | Not available | Unnormalized<br>volume and volume<br>changes of FLAIR<br>white matter<br>hyperintensities as<br>per 4 brain regions |
| Brain White<br>Matter<br>Hyperintensities<br>Map | Calculation of white<br>matter hyperintensities<br>map fused with the<br>processed FLAIR data<br>User customizable color<br>labels for the overlay<br>map. | Not available | Calculation of white<br>matter<br>hyperintensities map<br>overlaid with the<br>FLAIR data |
| Distribution &<br>Archiving | Creation of an image<br>series for a morphometry | Creation of an image<br>series for a | Automatic transfer<br>of generated image |
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| | report. Automatic<br>transfer of generated<br>maps and morphometry<br>report to a PACS system. | morphometry report.<br>Automatic transfer of<br>generated maps and<br>morphometry report to<br>a PACS system. | series and report to a<br>PACS system. |
|---------------------------------|------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------|
| User Interface<br>Confirmation | Confirmation UI with<br>basic visualization<br>functionality | Confirmation UI with<br>basic visualization<br>functionality | Not available. |
| User Interface<br>Configuration | Configuration UI | Configuration UI | Not available |
| Architecture | Cloud solution and Edge<br>components deployed on<br>customer premise. | Cloud only solution<br>with no components<br>deployed on customer<br>premise. | Cloud only solution<br>with no components<br>deployed on<br>customer premise. |
| DICOM SR | DICOM structured<br>report representation of a<br>natural language report | Basic morphometry<br>report | DICOM structured<br>report |
Table 1: Comparison table for Al-Rad Companion Brain MR VA40, predicate device Al-Rad Companion Brain MR VA20 (K193290) and reference device icobrain (K192130)
Siemens Healthineers has determined that icobrain (K192130) has similar technological and performance characteristics with respect to the segmentation and quantification of white matter hyperintense lesions. Icobrain produces reports that identify unnormalized volumes and volume changes of FLAIR white matter hyperintensities of four different regions (juxtacortical, periventricular, infratentorial, deep white matter) using equivalent methodology used in the subject device, AI-Rad Companion Brain MR VA40.
The conclusions from all verification and validation data suggest that these enhancements are equivalent with respect to safety and effectiveness of the predicate device. These modifications do not change the intended use of the product. Siemens is of the opinion that AI-Rad Companion Brain MR VA40 is substantially equivalent to the currently marketed device, AI-Rad Companion Brain MR VA20 (K193290).
## 8. Nonclinical Tests
Non-clinical tests were conducted to test the functionality of AI-Rad Companion Brain MR. Software validation and bench testing have been conducted to assess the performance claims as well as the claim of substantial equivalence to the predicate device.
AI-Rad Companion has been tested to meet the requirements of conformity to multiple industry standards. Non-clinical performance testing demonstrates that AI-Rad Companion Brain MR complies with the FDA guidance document, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005) as well as with the following voluntary FDA recognized Consensus Standards listed in Section 9.
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#### Verification and Validation
Software documentation for a Moderate Level of Concern software, per FDA's Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" issued on May 11, 2005, is also included as part of this submission. The performance data demonstrates continued conformance with special controls for medical devices containing software. Non-clinical tests were conducted on the subject device during product development.
Software "bench" testing in the form of Unit, System and Integration tests were performed to evaluate the performance and functionality of the new features and software updates. All testable requirements in the Requirement Specifications and the Risk Analysis have been successfully verified and traced in accordance with the Siemens Healthineers DH product development (lifecycle) process. Human factor usability validation is addressed in system testing and usability validation test records. Software verification and regression testing have been performed successfully to meet their previously determined acceptance criteria as stated in the test plans.
Siemens Healthineers adheres to the cybersecurity requirements as defined the FDA Guidance "Content of Premarket Submissions for Management for Cybersecurity in Medical Devices," issued October 2, 2014 by implementing a process of preventing unauthorized access, modifications, misuse or denial of use, or the unauthorized use of information that is stored, accessed, or transferred from a medical device to an external recipient.
### 9. Performance Software Validation
To validate the AI-Rad Companion Brain MR software from clinical perspective, the white matter hyperintensities segmentation and analysis algorithm underwent a scientific evaluation. The results of clinical data-based software validation for the subject device AI-Rad Companion Brain demonstrated equivalent performance in comparison to the reference device. A complete scientific evaluation report is provided in support of the device modifications. The brain morphometry algorithm, unchanged from the predicate, did not undergo a new scientific evaluation.
Performance testing for AI-Rad Companion Brain MR WMH was performed on Siemens Healthineers test data from 89 subjects, which included Multiple Sclerosis patients (MS), Alzheimer's patients (AD), cognitive impaired (CI) and healthy controls (HC). Testing data has balanced distribution with respect to gender and age of the patient according to target patient population and field strength of the MR scanner used. Accuracy was validated by comparing the results of the subject device to manual annotated ground truth from three radiologists. Three sets of white matter hyper-intensity ground truth were annotated manually by a disjoint group of annotator, reviewer, and clinical expert, with each expert randomly assigned per case to minimize annotation bias. Reproducibility studies were conducted to demonstrate the robustness of the WMH segmented by our device with respect to instrumental and patient noise.
#### Acceptance Criteria:
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| Validation Type | Acceptance Criteria |
|---------------------------------------|----------------------------------------------------------------------------------------|
| Volumetric Segmentation Accuracy | PCC 95% Confidence Interval includes 0.91<br>ICC 95% Confidence Interval includes 0.95 |
| Voxel-wise Segmentation Accuracy | Mean Dice score >= 0.58 |
| WMH Lesion-wise Segmentation Accuracy | Mean F1-score >= 0.57 |
| Reproducibility | Lower Bound of the 95% Bootstrap CI Dice >=<br>0.63 |
#### Summary Performance data, Standard Deviations & CIs:
| | Volumetric Segmentation | | Voxel-wise<br>Segmentation | WMH Lesion-<br>wise<br>Segmentation | Reproducibility |
|--------|-------------------------|-------------|----------------------------|-------------------------------------|-----------------|
| | PCC | ICC | Dice | F1-score | Dice |
| AVG | 0.98 | 0.97 | 0.60 | 0.60 | 0.79 |
| STD | n.a. | n.a. | 0.18 | 0.14 | 0.11 |
| 95% CI | [0.97,0.99] | [0.96,0.98] | [0.53,0.63] | [0.57,0.64] | [0.77,0.81] |
#### Testing Data Information:
| | Reproducibility Cohort | Testing Cohort |
|--------------|------------------------|----------------|
| # Subjects | 25 | 64 |
| # Studies | 100 | 64 |
| # of Females | 12 | 35 |
| # of Males | 13 | 29 |
| Age Range | 23-55 | 19-83 |
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| Medical Indication | MS – all | MS – 36<br>Cognitive Impairment - 17<br>Other neurological disease - 5<br>Cognitive Normal – 5<br>Unknown – 1 |
|--------------------|-----------------------------------------|---------------------------------------------------------------------------------------------------------------|
| Scan Protocol | 3D T1w MPRAGE<br>3D T2w FLAIR | T1w MPRAGE<br>T2w FLAIR |
| Field Strength | 3T | 1.5T: 30<br>3.0T: 34 |
| Manufacturer | Siemens | Siemens |
| Data Origin | Cleveland (US): 16<br>Baltimore (US): 9 | New York (US): 25<br>ADNI (US): 12<br>Lausanne (CH): 8<br>CLEMENS (CH): 10<br>Montpellier (FR): 9 |
#### Standard Annotation Process:
For each dataset, three sets of white matter hyperintensity ground truth are annotated manually. Each set is annotated by a disjoin group of annotator, reviewer, and clinical expert with the expert randomly assigned per case to minimize annotation bias. For each test dataset, the three initial annotations are annotated by three different in-house annotators. Then, each initial annotation is reviewed by the in-house reviewer. Afterwards, each initial annotation is reviewed by the referred clinical expert. The clinical expert reviews and corrects the initial annotation of the WMH according to the annotation protocol.
#### Testing & Training Data Independence:
The training data used for the training of the White matter hyperintensity algorithm is independent of the data used to test the white matter hyperintensity algorithm.
#### 10. Clinical Tests
No clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion Brain MR. Verification and validation of the enhancements and improvements have been performed and these modifications have been validated for their intended use. The data from these activities were used to support the subject device and the substantial equivalence argument. No animal testing has been performed on the subject device.
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#### 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 ISO 14971:2019 compliance to identify and provide mitigation of potential hazards in a risk analysis early in the design phase and continuously throughout the development of the product. These risks are controlled via measures realized during software development, testing and product labeling.
Furthermore, the device is intended for healthcare professionals familiar with the post processing of magnetic resonance images.
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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.