AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K202404 · Dec 22, 2021
BoneMRI
Mriguidance B.V.
Retrospective clinical imaging data (MRI and CT scans)
Retrospective clinical data from 61 patients were used to perform a quantitative voxel-by-voxel validation of the BoneMRI software, comparing reconstructed MRI-based radiodensity against co-registered CT scans.
Quantitative voxel-by-voxel validation; Retrospective validation study
61 patients (pelvic region); Sample Size: 61
Co-registered CT scans
3D bone morphology accuracy, radiodensity (HU) accuracy, and radiodensity contrast correlation
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Bone morphology, radiodensity, and radiodensity contrast
CNN with cascaded filter banks
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Mean absolute cortical delineation error < 1.0 mm; mean radiodensity deviation < 10 HU (bone < 55 HU); mean HU correlation coefficient > 0.80 (bone > 0.75)
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Retrospective clinical investigation: 61 patients
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Indications for Use
BoneMRI is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the bone structures in MRI images with enhanced contrast with respect to the surrounding soft tissue. It is to be used in the pelvic region, which includes the boney anatomy of the sacrum, hip bones and femoral heads. Warning: BoneMRI images are not intended to replace CT images and are not to be used for diagnosis or monitoring of (primary or metastatic) tumors.
Device Story
BoneMRI is a standalone image processing software for clinical/hospital networks; analyzes 3D gradient echo MRI scans; uses convolutional neural network (CNN) to transform MRI inputs into 3D tomographic radiodensity contrast images (BoneMRI images); assigns Hounsfield Unit (HU) values to volume elements based on intensity and contextual information; outputs DICOM images for viewing on existing PACS workstations; used by radiologists or orthopedic surgeons; assists in visualizing bone structures with enhanced contrast vs. soft tissue; benefits include improved bone morphology and radiodensity assessment in the pelvic region without additional radiation.
Clinical Evidence
Retrospective study of 61 patients; compared BoneMRI output to co-registered CT scans. Primary endpoints: 3D bone morphology accuracy, radiodensity (HU), and radiodensity contrast. Results: mean absolute cortical delineation error < 1.0 mm; mean radiodensity deviation < 10 HU (overall) and < 55 HU (bone); mean HU correlation coefficient > 0.80 (overall) and > 0.75 (bone).
Technological Characteristics
Standalone software; Linux-based; DICOM input/output; CNN-based image enhancement algorithm; utilizes cascade of filter banks; assigns Hounsfield Unit (HU) values to volume elements; operates on standard PACS workstations.
Indications for Use
Indicated for adult patients requiring visualization of pelvic bone structures (sacrum, hip bones, femoral heads) in MRI images. Contraindicated for use in diagnosing or monitoring primary or metastatic tumors; not intended to replace CT imaging.
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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December 22, 2021
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MRIguidance B.V % Suji Shetty Executive Vice President Maxis Medical 7052 Hollow Lake Way San Jose, California 95120
Re: K202404
Trade/Device Name: BoneMRI Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: November 29, 2021 Received: November 30, 2021
Dear Suji Shetty:
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
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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 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,
Jessica Lamb, Ph.D. Assistant 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) K202404
Device Name BoneMRI
### Indications for Use (Describe)
BoneMRI is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the bone structures in MRI images with enhanced contrast with respect to the surrounding soft tissue. It is to be used in the pelvic region, which includes the boney anatomy of the sacrum, hip bones and femoral heads. Warning: BoneMRI images are not intended to replace CT images and are not to be used for diagnosis or monitoring of (primary or metastatic) tumors.
| 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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#### 5.0 510(K) STATEMENT/SUMMARY
This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of SMDA 1990 and 21 CRF 807.92.
# 510 (k) number: K202404
#### I. Applicant Information
MRIguidance B.V. Gildstraat 91a 3572 EL, Utrecht The Netherlands info@mriguidance.com www.mriguidance.com +31 854000810
## Contact Person
Marijn van Stralen Chief Technology Officer MRIguidance B.V. Email: marijn@mriguidance.com Tel .: +31 610 505 649 Date Prepared: December 22, 2021
## Official Correspondant
Dr. Sujith Shetty Executive Vice President MAXIS LLC Email: sjshetty(@maxismedical.com
#### II. Device Information
| Trade Name: | BoneMRI |
|----------------------|------------------------------------------------------------|
| Common Name: | MRI image enhancement software |
| Classification name: | Picture archiving and Communication system (21CRF892.2050) |
| Regulatory Class: | Class II |
| Product Code: | QIH |
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#### III. Predicate Device
| Name | Manufacturer | 510(k)# |
|----------|----------------------|---------|
| SubtleMR | Subtle Medical, Inc. | K191688 |
This predicate has not been subject to a design-related recall. No reference devices were used in this submission.
#### IV. Device Description
The BoneMRI application is a standalone image processing software application that analyses 3D gradient echo MRI scans acquired with a dedicated MRI scan protocol. From the analysis, 3D tomographic radiodensity contrast images, called BoneMRI images, are constructed.
The BoneMRI images can be used to visualize the bone structures in MR images with enhanced contrast with respect to the surrounding soft tissue. The application is designed to be used by imaging experts, such as radiologists or orthopaedic surgeons, typically in a physician's office.
The BoneMRI application is a server application running in the clinic or hospital networks. It returns the reconstructed BoneMRI images as DICOM images.
#### V. Indications for Use
BoneMRI is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the bone structures in MRI images with enhanced contrast with respect to the surrounding soft tissue. It is to be used in the pelvic region, which includes the boney anatomy of the sacrum, hip bones and femoral heads.
Warning: BoneMRI images are not intended to replace CT images and are not to be used for diagnosis or monitoring of (primary or metastatic) tumors.
#### VI. Comparison of Technological Characteristics with the Predicate Device:
A comparison of the intended use, indication for use, and technological characteristics of the BoneMRI application to the predicate device SubtleMR are presented below. We have included the attributes suggested in FDA's website guidance for this comparison.
| Name | Manufacturer | 510(k)# |
|----------|----------------------|---------|
| SubtleMR | Subtle Medical, Inc. | K191688 |
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# A. Intended Use
| | Predicate Device<br>SubtleMR | Subject Device<br>BoneMRI | Comment |
|-------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended Use | SubtleMR is an image<br>processing software<br>that can be used for<br>image enhancement in<br>MRI images. It can be<br>used to reduce image<br>noise for head, spine,<br>neck, and knee MRI,<br>or increase image<br>sharpness for non-<br>contrast-enhanced<br>head MRI. | BoneMRI is an image<br>processing software<br>that can be used for<br>image enhancement in<br>MR images. It can be<br>used to visualize the<br>bone structures in MR<br>images with enhanced<br>contrast with respect<br>to the surrounding soft<br>tissue | Similar -<br>Intended uses are<br>the same for Image<br>enhancements for<br>MRI. But the<br>intended use<br>differences does<br>not affect the<br>safety and<br>effectiveness of the<br>device when used<br>as labeled and is<br>similar to the<br>predicate use. |
| 21CFR Section | 892.2050 | 892.2050 | The same |
| Product Code | LLZ | QIH | Similar |
| Target Population | Adults | Adults | The same |
# B. Technological Characteristics
| | Predicate Device<br>SubtleMR | Subject Device<br>BoneMRI | Comment |
|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Nature | Software package | Software package | The same |
| Operating System | Linux | Linux | The same |
| Data input | MRI images in<br>DICOM format | MRI images in<br>DICOM format | The same |
| Data output | MRI images in<br>DICOM format | MRI images in<br>DICOM format | The same |
| Processing<br>Algorithms | SubtleMR software<br>implements an image<br>enhancement<br>algorithm using<br>convolutional neural<br>network based<br>filtering. Original<br>images are enhanced<br>by running through a<br>cascade of filter | MRIguidance<br>software implements<br>an image<br>enhancement<br>algorithm using<br>convolutional neural<br>network. Original<br>images are enhanced<br>by running them<br>through a cascade of | Different –<br><br>The algorithm while<br>using similar<br>methodology, uses<br>different filters and<br>outputs to enhance<br>the image. The<br>difference does not<br>affect the safety and |
| | Predicate Device<br>SubtleMR | Subject Device<br>BoneMRI | Comment |
| | banks, where<br>thresholding and<br>scaling operations are<br>applied. Separate<br>neural network based<br>filters are obtained<br>for noise reduction<br>and sharpness<br>increase. The<br>parameters of the<br>filters were obtained<br>through an image<br>guided optimization<br>process. | filter banks, where<br>thresholding and<br>scaling operations<br>are applied. Separate<br>neural network-<br>based filters are<br>obtained to assign a<br>Hounsfield Unit<br>(HU) value to a<br>single volume<br>element, based on<br>intensity and<br>contextual<br>information. The<br>parameters of the<br>model were obtained<br>through an algorithm<br>development<br>pipeline. | effectiveness of the<br>device when used as<br>labeled and is similar<br>to the predicate use. |
| User Interface | None - enhanced<br>images are viewed on<br>existing PACS<br>workstations | None - enhanced<br>images are viewed<br>on existing PACS<br>workstations | The same |
| Workflow | The software<br>operates on DICOM<br>files on the file<br>system, enhances the<br>images, and stores<br>the enhanced images<br>on the file system.<br>The receipt of<br>original DICOM<br>image files and<br>delivery of enhanced<br>images as DICOM<br>files depends on other<br>software systems.<br>Enhanced images co-<br>exist with the original<br>images. | The software<br>operates on DICOM<br>files on the file<br>system, enhances the<br>images, and stores<br>the enhanced images<br>on the file system.<br>The receipt of<br>original DICOM<br>image files and<br>delivery of enhanced<br>images as DICOM<br>files depends on<br>other software<br>systems. Enhanced<br>images co-exist with<br>the original images. | The same |
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#### VII. Performance Data:
BoneMRI conducted the following performance testing:
- Software verification and validation testing 1.
- 2. Studies that utilized retrospective clinical data to demonstrate the software enhanced imaging quality in MR images via an enhancement of bone.
# BoneMRI Pelvic region - Voxel-bv-Voxel analysis
Quantitative voxel-by-voxel validation of BoneMRI was performed on imaging data from 61 patients, consisting of the BoneMRI and CT of the same patient, acquired during the previously conducted clinical investigations. MRIguidance conducted the validations based on an in-house developed algorithm validation pipeline, the core validation framework. The objective was to validate the quantitative accuracy of BoneMRI for the pelvic region using rigorous, objective, and unbiased statistical tests. The endpoints were the metrics that described the accuracy of 3D bone morphology, radiodensity, and radiodensity contrast versus co-registered CT scans in terms of voxel-by-voxel HUs and standard deviations around these HU values. The results demonstrate clinically acceptable accuracy on all of the endpoints.
The data provided demonstrate that BoneMRI application v1.2 can accurately reconstruct the 3D bone morphology with a mean absolute cortical delineation error below 1.0 mm on average; accurately reconstruct the tissue radiodensity with a mean deviation below 10 HU on average and a mean deviation below 55 HU for bone specifically; accurately reconstruct the tissue radiodensity contrast with a mean HU correlation coefficient above 0.80 on average and a mean HU correlation coefficient above 0.75 for bone specifically.
CONCLUSION: BoneMRI demonstrates accurate bone morphology, radiodensity, and radiodensity contrast. Thus, BoneMRI is a useful tool to qualitatively and quantitatively assess the pelvic region.
## VIII. Conclusions:
BoneMRI, based on the indications for use, product performance, and clinical information provided in this notification, the subject device has been shown to be substantially equivalent to the currently marketed predicate device. The two devices have similar technological characteristics: both algorithms use image based reconstruction, and both methods have optimized parameters to ensure the robustness of the algorithm. This 510(k) submission includes information on the BoneMRI technological characteristics, as well as performance data and verification and validation activities demonstrating that BoneMRI is as safe and effective as the predicate, and does not raise different questions of safety and effectiveness.
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.