K232412 · Thirona BV · JAK · Jan 8, 2024 · Radiology
Device Facts
Record ID
K232412
Device Name
LungQ v3.0.0
Applicant
Thirona BV
Product Code
JAK · Radiology
Decision Date
Jan 8, 2024
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.1750
Device Class
Class 2
Attributes
Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Lung and lobar volume measurement
—
Difference <= 10%
Difference < 10%
—
—
Head-to-head performance testing comparing LungQ v3.0.0 to LungQ v1.1.0 (K173821) and human expert segmentations.
>1 (human experts)
Pulmonary density measurement
—
LAA-950HU: -1% to 1%; LAA-910HU: -10% to 10%; PD15: -10 HU to 10 HU
Difference < threshold value
—
—
Head-to-head performance testing comparing LungQ v3.0.0 to LungQ v1.1.0 (K173821) and human expert segmentations.
>1 (human experts)
Fissure completeness classification
—
Az value >= 0.95
Az value = 0.97
—
—
Head-to-head performance testing comparing LungQ v3.0.0 to LungQ v1.1.0 (K173821).
—
Indications for Use
The Thirona LungQ software provides reproducible CT values for pulmonary tissue which is essential for providing quantitative support for diagnosis and follow up examination. The LungQ software can be used to support physician in the diagnosis and documentation of pulmonary tissues images (e.g. abnormalities) from CT thoracic datasets. Three-D segmentation and isolation of sub-compartments, volumetric analysis, density evaluation, and reporting tools are provided.
Device Story
LungQ v3.0.0 is stand-alone command-line software for analyzing thoracic CT scans. It processes DICOM-formatted CT thoracic datasets to perform 3D segmentation of lungs, lobes, and pulmonary sub-segments; calculates volumetric and density metrics (including LAA-950HU, LAA-910HU, and 15th percentile density); and evaluates fissure completeness. The software operates on Linux systems without a graphical user interface. Physicians use the generated reports to assist in clinical diagnosis and follow-up of pulmonary abnormalities. By providing reproducible quantitative measurements of lung structure and density, the device aids in objective assessment of pulmonary tissue, potentially improving diagnostic consistency and monitoring of disease progression.
Clinical Evidence
No clinical testing was performed. Substantial equivalence was demonstrated via non-clinical bench testing, including software verification, validation, and a head-to-head performance study against the predicate. The study compared lung/lobar volumes, density scores (LAA-950HU, LAA-910HU, PD15), and fissure completeness across diverse CT scanner brands. Results showed differences within specified thresholds (e.g., volume difference ≤10%, fissure completeness Az value 0.97). (Sub)segmental measurements were validated against human expert-corrected segmentations.
Technological Characteristics
Stand-alone command-line software running on Linux. Utilizes DICOM input. Performs 3D segmentation of lungs, lobes, and sub-segments; volumetric analysis; density evaluation; and fissure analysis. Complies with IEC 62304 (software lifecycle), ISO 14971 (risk management), IEC 62366-1 (usability), and AAMI TIR57/NEMA HN 1 (cybersecurity).
Indications for Use
Indicated for use by physicians to support the diagnosis and documentation of pulmonary tissue abnormalities from CT thoracic datasets, providing quantitative support for diagnosis and follow-up examination.
Regulatory Classification
Identification
A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.
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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services seal on the left and the FDA acronym and name on the right. The FDA acronym is in a blue square, and the full name "U.S. Food & Drug Administration" is in blue text.
January 8, 2024
Thirona BV % Eva Rikxoort Official Correspondent Toernooiveld 300 Toernooiveld 300, 6525 EC NETHERLANDS
Re: K232412
Trade/Device Name: LungO v3.0.0 Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-Ray System Regulatory Class: Class II Product Code: JAK Dated: August 10, 2023 Received: December 6, 2023
Dear Eva Rikxoort:
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.
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).
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2
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about 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,
Lu Jiang
Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
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# Indications for Use
510(k) Number (if known) K232412
Device Name LungQ V3.0.0
#### Indications for Use (Describe)
The Thirona LungQ software provides reproducible CT values for pulmonary tissue which is essential for providing quantitative support for diagnosis and follow up examination. The LungQ software can be used to support physician in the diagnosis and documentation of pulmonary tissues images (e.g. abnormalities) from CT thoracic datasets. Three-D segmentation and isolation of sub-compartments, volumetric analysis, density evaluation, and reporting tools are provided.
| Type of Use (Select one or both, as applicable) | |
|------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
| <span style="text-decoration: underline;"></span> Prescription Use (Part 21 CFR 801 Subpart D) | <span style="text-decoration: underline;"></span> Over-The-Counter Use (21 CFR 801 Subpart C) |
| <input checked="" type="checkbox"/> | <input type="checkbox"/> |
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### Section 05
#### Section 5 510(k) Summary
## 5.1 SUBMITTER
Submitted by:
Thirona BV
Toernooiveld 300
6525 EC Nijmegen the Netherlands
Contact Person:
Eva van Rikxoort
Telephone Number: +31 6 47 14 28 38
Email: evavanrikxoort@thirona.eu
Date Prepared: 8th August 2023
### 5.2 DEVICE
| Trade Name | LungQ v3.0.0 |
|-----------------------|----------------------------------|
| Common Use/Usual Name | Computer Tomography X-ray system |
| Product Code | JAK |
| Classification | Class II, 21 CFR 892.1750 |
| Device Panel | Radiology |
#### 5.3 PREDICATE DEVICE
| Predicate Device | LungQ V1.1.0 |
|--------------------------|---------------------------|
| Predicate Classification | Class II, 21 CFR 892.1750 |
#### 5.4 REFERENCE DEVICE
| Predicate Device | VIDA Vision |
|--------------------------|---------------------------|
| Predicate Classification | Class II, 21 CFR 892.1750 |
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## 5.5 DEVICE DESCRIPTION
The LungQ software is designed to aid in the interpretation of Computed Tomography (CT) scans of the thorax that may contain pulmonary abnormalities. LungQ is stand-alone command-line software which must be run from a command-line interpreter and does not have a graphical user interface.
## 5.6 INDICATION FOR USE
The Thirona LungQ software provides reproducible CT values for pulmonary tissue which is essential for providing quantitative support for diagnosis and follow up examination. The LungQ software can be used to support physician in the diagnosis and documentation of pulmonary tissues images (e.g., abnormalities) from CT thoracic datasets. Three-D segmentation and isolation of sub-compartments, volumetric analysis, density evaluations, fissure evaluation, and reporting tools are provided.
## 5.7 COMPARISON OF TECHNOLOGICAL CHARACTERISTICS
Table 5-1 below compares the Thirona LungQ software to the predicate device.
| Item | LungQ v3.0.0<br>Thirona<br>(Subject Device) | LungQ v1.1.0<br>Thirona<br>(Predicate Device) | VIDA vision<br>VIDA Diagnostics, Inc<br>(Reference Device) |
|---------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------|------------------------------------------------------------|
| <b>510(k) Number</b> | NA | K173821 | K200990 |
| Product Code | JAK | Same | Same |
| Regulation Number | 21 CFR 892.1750 | Same | Same |
| Device<br>Classification | Class II | Same | Same |
| Common Name | Software Accessory to a<br>Computed tomography x-ray<br>system | Same | Same |
| Intended Use | The Thirona LungQ software<br>provides reproducible CT<br>values for pulmonary tissue<br>which is essential for<br>providing quantitative<br>support for diagnosis and<br>follow up examination. The<br>LungQ software can be used<br>to support physician in the<br>diagnosis and<br>documentation of<br>pulmonary tissues images<br>(e.g., abnormalities) from CT<br>thoracic datasets. Three-D | Same | Equivalent |
| | segmentation and isolation<br>of sub-compartments,<br>volumetric analysis, density<br>evaluations, fissure<br>evaluation, and reporting<br>tools are provided. | | |
| Modality | CT | Same | Same |
| Data Loading | DICOM | Same | Same |
| Application | Command-line interface | Same | Equivalent |
| OS | Linux | Equivalent | Equivalent |
| Segmentation | Provides 3D segmentation | Same | Same |
| | Provides Segmentation of<br>the:<br>• Left Lung<br>• Right Lung<br>• Left Upper Lobe<br>• Left Lower Lobe<br>• Right Upper Lobe<br>• Right Middle Lobe<br>• Right Lower Lobe<br>• Pulmonary<br>(sub)segments | Equivalent | Equivalent |
| | Provides Airways<br>Segmentation | Same | Same |
| | User cannot manually edit<br>segmentation | Same | Equivalent |
| Lung Volume<br>Analysis Support | Ability to measure volume<br>for:<br>• Both Lungs<br>• Left Lung<br>• Right Lung<br>• Left Upper Lobe<br>• Left Lower Lobe<br>• Right Upper Lobe<br>• Right Middle Lob<br>• Right Lower Lobe<br>• Pulmonary<br>(sub)segments | Equivalent | Equivalent |
| Volume Density<br>Analysis | Ability to measure volume at<br>multiple density ranges for:<br>• Both Lungs | Equivalent | Equivalent |
| | Left Lung Right Lung Left Upper Lobe Left Lower Lobe Right Upper Lobe Right Middle Lob Right Lower Lobe Pulmonary (sub)segments Ability to measure the 15th percentile density analysis | Same | Same |
| Fissure Analysis | Ability to perform fissure evaluations | Same | Same |
| Analyzed Data Output | Provides a report | Same | Same |
#### Table 5-1: Substantial Equivalence Comparison between Subject, Predicate and Reference Device
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### 5.8 PERFORMANCE DATA
The performance testing for LungQ consists of norm-compliance testing, design verification and validation testing. For norm-compliance and design verification, the testing was performed per subject device, while the validation, such as non-clinical validation and usability validation, were performed at system level representing a logical clinical workflow following the intended use.
Performance testing data of the proposed devices demonstrate that the subject device is substantially equivalent to the predicate device, and that the design output meets the design input requirements.
#### Compliance Testing
Norm-compliance performance tests were performed on the proposed LungQ software according to the following FDA recognized consensus standards and FDA guidance documents (see Table 5-2 and Table 5-3), and were all passed.
| Identification<br>Number | Edition /<br>Year | Title | Rec<br>Numbe<br>r |
|----------------------------------|-------------------|----------------------------------------------------------------------------------------------------------|-------------------|
| AAMI TIR57 | 2016 | Principles for medical device security—Risk management | 13-83 |
| ANSI AAMI<br>IEC TIR 80002-<br>1 | 2009 | Medical device software - Part 1: Guidance on the<br>application of ISO 14971 to medical device software | 13-34 |
| ANSI NEMA<br>HN 1 | 2019 | Manufacturer Disclosure Statement for Medical Device<br>Security | 13-123 |
Table 5-2: Standards and Guidance documents
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| IEC 62304 | 1.1/2015 | Medical device software - Software life-cycle processes | 13-79 |
|-----------------------|----------|-------------------------------------------------------------------------------------------------------------------------------|--------|
| IEC 62366-1 | 1.1/2020 | Medical devices - Application of usability engineering to<br>medical devices | 5-129 |
| IEC 82304-1 | 1.0/2016 | Health software - Part 1: General requirements for<br>product safety | 13-97 |
| ISO 14971 | 3/2019 | Medical devices - Application of risk management to<br>medical devices | 5-125 |
| ISO 15223-1 | 4/2021 | Medical devices — Symbols to be used with information<br>to be supplied by the manufacturer — Part 1: General<br>requirements | 5-117 |
| ISO 20417 | 1/2021 | Medical devices — Information to be supplied by the<br>manufacturer | 5-135 |
| ISO/IEC 21778 | 1/2017 | Information technology — The JSON data interchange<br>syntax | - |
| ISO/IEC 27001 | 2/2013 | Information technology - Security techniques -<br>Information security management systems -<br>Requirements | - |
| NEMA PS 3.1 -<br>3.20 | 2022d | Digital Imaging and Communications in Medicine<br>(DICOM) Set | 12-349 |
#### Table 5-3: Guidance documents
| Identification<br>Number | Year | Title |
|--------------------------|------|---------------------------------------------------------------------------------------------------------------------------------------|
| FDA-1997-D-0029 | 2002 | General Principles of Software Validation |
| FDA-2011-D-0469 | 2016 | Applying Human Factors and Usability Engineering to Medical<br>Devices |
| FDA-2011-D-0652 | 2014 | The 510(k) Program: Evaluating Substantial Equivalence in<br>Premarket Notifications [510(k)] |
| FDA-2014-D-0456 | 2018 | Appropriate Use of Voluntary Consensus Standards in Premarket<br>Submissions for Medical Devices |
| FDA-2015-D-4852 | 2017 | Design Considerations and Pre-market Submission<br>Recommendations for Interoperable Medical Devices |
| FDA-2016-D-1853 | 2021 | Unique Device Identification System: Form and Content of the<br>Unique Device Identifier (UDI) |
| FDA-2018-D-1329 | 2019 | Recommended Content and Format of Non-Clinical Bench<br>Performance Testing Information in Premarket Submissions |
| FDA-2019-D-3598 | 2019 | Off-The-Shelf Software Use in Medical Devices |
| FDA-2021-D-0775 | 2023 | Content of Premarket Submissions for Device Software Functions |
| FDA-2021-D-1158 | 2022 | Cybersecurity in Medical Devices: Quality System Considerations and<br>Content of Premarket Submissions |
| FDA-2023-D-1030 | 2023 | Cybersecurity in Medical Devices: Refuse to Accept Policy for Cyber<br>Devices and Related Systems Under Section 524B of the FD&C Act |
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#### Performance Testing
Software Verification testing was conducted to ensure that the Lung Q software met its requirements. The verification testing included white box testing to verify implementation and system integration testing. The LungQ software successfully passed the verification testing.
Software Validation was conducted to ensure the software met the user needs (i.e. input requirements). This validation was based on user scenarios. The LungQ software successfully passed the software validation.
Human factors (HF) engineering process was followed in accordance with the usability standard and FDA guidance. The restricted interface of LungQ with the third party end-user-interface is well controlled by the use of standard input and output formats. The usability is determined by the input requirements, which have been identified during risk management. The human factors validation test (summative usability evaluation) of LungQ was performed.
#### Substantial equivalence Study
A head-to-head performance testing was conducted between the subject and the predicate device.
The aim of this study was to assess and compare the measurement of lung structure parameters, such as lung and lobar volumes, density scores (LAA-950HU and LAA-910HU), 15th percentile lung density (PD15), and fissure completeness between LungQ v3.0.0 (subject device) and LungQ v1.1.0 (primary predicate, K1738210). Firstly, both devices analyzed the lung and lobar volumes, density scores, PD15 and fissure completeness. Subsequently, Bland-Altman plots were used for the pairwise comparison of lung and lobar volumes, density scores, PD15 measurements between the two devices. Moreover, for comparison of the fissure completeness measurements between both devices, receiver operating characteristic (ROC) analysis was performed and the area under de ROC curve (Az value) was calculated. The tolerable variability for absolute and relative threshold values was defined as:
- . Lung and lobar volume: Difference ≤ 10%
- . Lung and lobar density measurements:
- LAA-950HU: Agreements limits -1% to 1%
- LAA-910HU: Agreement limits -10% and 10% o
- 15™ Percentile: Agreement limits -10 HU to 10 HU O
- Fissure completeness classification: Az value ≥ 0.95
The (sub)segmental volumes and density scores were not compared with the primary predicate but with segmentation which were corrected by human experts. The pairwise comparison between LungQ v3.0.0 and the experts was performed using Blant-Altman analysis of (sub)segmental volumes and density scores. The tolerable variability for absolute and relative values are 150mL and 5%, respectively.
The results showed that for the lung and lobar volumes, density scores (LAA-950HU and LAA-910HU), and 15th percentile lung density (PD15), the difference between LungQ v3.0.0 (subject device) and
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the primary predicate were less than the threshold value. The area under the AUC curve (Az value) for the comparison of fissure completeness measurements between LungQ v3.0.0 (subject device) and the primary predicate was found to be 0.97, above the minimal threshold value. The mean difference (SD) between LungQ v3.0.0 and the reference of the human experts for the (sub)segmental volumes and density scores were less than the threshold value.
The scans were taken with a wide variety of scanner brands and models used to obtain scans in the datasets used for the equivalence study testing are shown in Table 5-4.
| Table 5-4: Imaging parameters equivalence study. | | |
|--------------------------------------------------|--|--|
|--------------------------------------------------|--|--|
| Imaging parameters Equivalence study | |
|--------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Scanner manufacture | GE MEDICAL SYSTEMS; SIEMENS; Philips |
| Scanner types | LightSpeed16; LightSpeed VCT; Sensation 64;<br>Definition; Sensation 16; Definition AS+;<br>SOMATOM Definition Flash; Brilliance 64;<br>LightSpeed Pro 16; Discovery CT750 HD;<br>SOMATOM Definition; LightSpeed Ultra<br>SOMATOM Definition AS; LightSpeed16 |
The results showed that outputs from Thirona LungQ v3.0.0 are equivalent to the predicate device, LungQ v1.1.0.
#### Conclusion on performance testing
All compliance, verification and validation tests have been used to support substantial equivalence of the subject device and to demonstrate that the LungQ v3.0.0 device:
- comply with the aforementioned international and FDA recognized consensus standards and FDA guidance documents; and
- meet the acceptance criteria and are adequate for their intended use.
Based on the information provided above, the LungQ v3.0.0 device is considered substantially equivalent to the predicate device in terms of safety and effectiveness.
No clinical testing was required as substantial equivalence was demonstrated by the attributes of intended use, technological characteristics, and non-clinical testing.
### 5.9 CONCLUSIONS
The proposed device, LungQ v3.0.0, is substantially equivalent to the above-mentioned predicate device, in terms of intended use, technological characteristics and, safety and effectiveness.
Substantial equivalence was demonstrated by non-clinical performance tests provided in this 510(k) premarket notification. These tests demonstrate that the proposed device comply with the user needs specifications and product requirements, as well as the requirements specified in the international and
Thirona
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FDA-recognized consensus standards, and are as safe and effective as the predicate device, and do not raise any new safety and/or effectiveness concerns.
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.