K242411 · Brainomix Limited · JAK · Feb 19, 2025 · Radiology
Device Facts
Record ID
K242411
Device Name
Brainomix 360 e-Lung
Applicant
Brainomix Limited
Product Code
JAK · Radiology
Decision Date
Feb 19, 2025
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.1750
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Lung segmentation
AI/ML image processing approach
—
Dice Similarity Coefficient (DSC) significantly higher than predicate (p<0.0001)
—
—
Head-to-head comparison study between proposed and predicate devices
3 (US board certified radiologists)
Indications for Use
The e-Lung software provides reproducible CT values for pulmonary tissue, which is essential for providing quantitative support in the examination of radiological findings. These radiological findings can then be evaluated by the physician in conjunction with a range of ancillary information to form a potential diagnosis or list of likely diagnoses. The e-Lung software package is intended to be a workflow enhancement and visualization tool for the assessment of CT thoracic datasets. e-Lung can be used to support the physician when examining the pulmonary and thoracic tissue (i.e. lung parenchyma) in CT thoracic datasets. 3D segmentation, volumetric measurements, density evaluations, and reporting tools are combined with a dedicated workflow.
Device Story
Brainomix 360 e-Lung is a standalone software module for CT thoracic datasets; operates on off-the-shelf or virtual servers. Inputs: DICOM-compliant CT thoracic scans. Processing: AI/ML-based lung segmentation; volumetric measurements; density evaluations based on Hounsfield units (HU); longitudinal assessment via automated grouping of patient scans. Outputs: Quantitative reports (CSV, Excel, PDF), visualization of analysis results in web UI, PACS, and cloud. Used in clinical settings by physicians to support diagnosis and longitudinal assessment of lung disease. Benefits: Workflow enhancement; automation of time-consuming manual tasks; provides objective, reproducible quantitative data to aid clinical decision-making.
Clinical Evidence
Bench testing and validation study. Lung segmentation performance validated via head-to-head comparison against predicate using a ground truth mask generated by three board-certified radiologists. Results showed the AI/ML algorithm achieved significantly higher Dice Similarity Coefficient (DSC) values than the predicate (p<0.0001). Generalizability confirmed across diverse patient demographics, clinical variables (BMI, smoking status), and scanner parameters. Software verification and validation confirmed longitudinal assessment features and PACS/cloud integration met all design requirements.
Technological Characteristics
Software-based CT processing module; runs on Ubuntu Linux via off-the-shelf or virtual servers. Compliant with DICOM (NEMA PS 3.1-3.20). Features AI/ML-based lung segmentation, volumetric measurements, and density evaluations (HU-based). Connectivity includes web UI, PACS, and cloud integration. Designed per ISO 14971:2019 risk management and IEC 81001-5-1 cybersecurity standards.
Indications for Use
Indicated for use by physicians to support the examination of pulmonary and thoracic tissue (lung parenchyma) in CT thoracic datasets for the documentation of radiological findings that may be indicative of chest diseases.
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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February 19, 2025
Brainomix Limited % Zsolt Szrnka Regulatory Affairs Manager First Floor, Seacourt Tower West Way OXFORD. OX2 0JJ UNITED KINGDOM
Re: K242411
Trade/Device Name: Brainomix 360 e-Lung Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-Ray System Regulatory Class: Class II Product Code: JAK Dated: August 14, 2024 Received: January 16, 2025
Dear Zsolt Szrnka:
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.
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality 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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatory
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assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
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
Enclosure
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#### Indications for Use
Submission Number (if known)
#### K242411
Device Name
Brainomix 360 e-Lung
#### Indications for Use (Describe)
The e-Lung software provides reproducible CT values for pulmonary tissue, which is essential for providing quantitative support in the examination of radiological findings. These radiological findings can then be evaluated by the physician in conjunction with a range of ancillary information to form a potential diagnosis or list of likely diagnoses. The e-Lung software package is intended to be a workflow enhancement and visualization tool for the assessment of CT thoracic datasets. e-Lung can be used to support the physician when examining the pulmonary and thoracic tissue (i.e. lung parenchyma) in CT thoracic datasets. 3D segmentation, volumetric measurements, density evaluations, and reporting tools are combined with a dedicated workflow.
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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#### K242411
Image /page/4/Picture/1 description: The image shows the logo for BRAINOMIX. The logo consists of a stylized brain icon on the left, followed by the word "BRAINOMIX" in a sans-serif font. The brain icon is a gradient of blue and teal, while the word "BRAINOMIX" is mostly gray, with the "AI" portion in a matching blue and teal gradient.
# 510(k) Summary Brainomix Limited – Brainomix 360 e-Lung
| Date Prepared: | 16Jan2025 |
|-----------------------------|---------------------------------------------------------------------------|
| Applicant's Name: | Brainomix Limited |
| Applicant's Address: | First Floor, Seacourt Tower, West Way<br>Oxford, OX 0JJ<br>United Kingdom |
| Official Contact: | Zsolt Szrnka<br>+44 (0) 1865 582730<br>regulatory@brainomix.com |
| Device Proprietary Name: | Brainomix 360 e-Lung |
| Device Classification Name: | System, X-Ray, Tomography, Computed |
| Regulatory Class: | Class II |
| Product Code: | JAK |
| Regulation Number: | 21 C.F.R. §892.1750 |
| Regulation Name: | Computed tomography x-ray system |
### 1. Predicate Device
Brainomix 360 e-Lung is Substantially Equivalent to the following Legally Marketed device:
Trade Name: Brainomix 360 e-Lung Manufacturer: Brainomix Ltd. Regulation Number: 21 C.F.R. §892.1750 Regulatory Class: Class II Regulation Name: Computed tomography x-ray system Product Code: JAK Submission Number: K233875
### 2. Device Description
Brainomix 360 e-Lung is a software package compliant with the DICOM standard and running on an off-the-shelf physical or virtual server. e-Lung is a CT processing module which operates within the integrated Brainomix 360 platform.
Brainomix 360 e-Lung is a stand-alone software device which uses a set of image processing algorithms to perform evaluation (3D segmentation and isolation of sub-compartments, volumetric measurements, and density evaluations), editing, and reporting tools which are combined with a dedicated workflow.
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Image /page/5/Picture/0 description: The image shows the logo for BRAINOMIX. The logo consists of a stylized brain icon on the left, followed by the word "BRAINOMIX" in bold, sans-serif font. The brain icon and the "AI" in the word "BRAINOMIX" are in a gradient of light blue to teal, while the rest of the word is in dark gray.
e-Lung can be used to support the physician in the documentation of radiological findings that may be indicative of chest diseases when examining the pulmonary and thoracic tissue (i.e. lung parenchyma) in CT thoracic datasets. These radiological findings are then evaluated in conjunction with a range of ancillary information to form a potential diagnosis or list of likely diagnoses.
e-Lung is designed to analyze pulmonary CT slice data and display analysis results. Each voxel of the scan is measured by Hounsfield units (HU), a measurement of x-ray attenuation that is applied to each volume element in three-dimensional space. The HU are utilized to distinguish between air, water, tissue and bone, such distinction is common in the industry.
e-Lung provides computed tomography (CT) viewing, and parenchymal density analysis in one application. e-Lung provides quantitative measurements and tabulates quantitative properties.
e-Lung focuses on what is visible to the eye and applies volumetric methods that might otherwise be too time consuming to use.
The software does not perform any function which cannot be accomplished by a trained user utilizing manual tracing methods; the software does not reconstruct a 3D rendering image of the lung; the intent of the software is to enhance the workflow by saving time and automating potential error prone manual tasks.
e-Lung has functions for loading, and saving datasets, and will generate screen displays, computations and aggregate statistics. e-Lung data output may be exported to a CSV, Excel or PDF file.
### 3. Intended Use / Indications for Use
The e-Lung software provides reproducible CT values for pulmonary tissue, which is essential for providing quantitative support in the examination of radiological findings. These radiological findings can then be evaluated by the physician in conjunction with a range of ancillary information to form a potential diagnosis or list of likely diagnoses. The e-Lung software package is intended to be a workflow enhancement and visualization tool for the assessment of CT thoracic datasets. e-Lung can be used to support the physician when examining the pulmonary and thoracic tissue (i.e. lung parenchyma) in CT thoracic datasets. 3D segmentation, volumetric measurements, density evaluations, and reporting tools are combined with a dedicated workflow.
#### 4. Performance Data
e-Lung complies with DICOM (Digital Imaging and Communications in Medicine) – developed by the American College of Radiology and the National electrical Manufacturers Association. NEMA PS 3.1 – 3.20.
The lung segmentation performance of the updated algorithm was validated through a head-to-head comparison between proposed and predicate devices. The study evaluated the accuracy of the e-Lung lung mask generation compared to a ground truth mask generated from the consensus of three experienced US board certified radiologists, who segmented the lungs following their usual standard of care. It was demonstrated that the Dice Similarity Coefficient (DSC) values were significantly higher
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Image /page/6/Picture/0 description: The image shows the logo for Brainomix. The logo consists of a stylized brain icon on the left, followed by the word "BRAINOMIX" in a sans-serif font. The brain icon is a gradient of blue and green, while the letters "AI" in the word "BRAINOMIX" are also in the same gradient of blue and green, while the rest of the letters are in a dark gray color.
for the AI/ML segmentation algorithm method (proposed device) than the segmentation method of the predicate device (V=11628, p<0.0001) (see Figure 1). Furthermore, the clinical study also entailed a secondary objective, which was to demonstrate that device generalizability across a range of clinically relevant parameters, including demographics, clinical variables (BMI, smoking status, radiological findings) and scanner or image variables (location, scanner manufacturer, slice thickness, KvP and reconstruction method) was not impacted by the changes to the algorithm. It was concluded that the proposed device's Al/ML segmentation algorithm works effectively across all patient types.
Image /page/6/Figure/3 description: The image is a histogram comparing the Dice Similarity Coefficient of two segmentation methods, CNN and Predicate. The x-axis represents the Dice Similarity Coefficient, ranging from 0.95 to 0.99, while the y-axis represents the count. The histogram shows the distribution of Dice Similarity Coefficients for each method, with CNN having a higher concentration of values around 0.99 and Predicate having a broader distribution with a peak around 0.97.
Figure 1. Histogram showing the distribution of Dice Similarity Coefficients (DSC) for the lung images segmented using the AI/ML segmentation algorithm (pink) vs the predicate segmentation algorithm (blue).
The longitudinal assessment feature and outputs to PACS and cloud were thoroughly verified as part of the software verification and validation activities. Software performance, validation and verification testing demonstrated that e-Lung met all design requirements and specifications.
#### 5. Prescriptive Statement
Caution: Federal law restricts this device to sale by or on the order of a physician.
#### 6. Safety and Effectiveness
Brainomix 360 e-Lung has been designed, verified and validated in compliance with 21 CFR, Part 820.30 requirements. The device has been designed to meet the requirements associated with ISO 14971:2019 (risk management).
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Image /page/7/Picture/0 description: The image shows the logo for BRAINOMIX. The logo consists of a stylized brain icon on the left, followed by the word "BRAINOMIX" in a bold, sans-serif font. The brain icon is a gradient of light blue to dark blue, and the "AI" in "BRAINOMIX" is also in the same gradient of blue, while the rest of the letters are in dark gray.
## 7. Cybersecurity
Brainomix 360 e-Lung has been designed to follow the FDA Cybersecurity Guidance and IEC 81001-5-1.
## 8. Summary of Technological Characteristics
Brainomix 360 e-Lung principal workflow for chest CT datasets includes the following key steps:
- Chest CT images loading. Brainomix 360 e-Lung provides an automated workflow which will । : automatically process image data received by the system.
- 2. lmage processing function. Brainomix 360 e-lung processes chest CT scans and first generates a lung mask followed by any structural density filters or density histogram filters that have been pre-configured by an admin user.
- 3. Generation of summary results report
- 4. Image and results viewing. This is a non-diagnostic DICOM application allowing a trained clinician to view the CT image and associated results within the web UI. Results can also be sent and reviewed in PACS. The user is then able to edit the periphery of the lungs for that case and view updated quantifications. If multiple scans are available for a patient, images and results can be viewed simultaneously. These results may then be evaluated in conjunction with a range of ancillary information as part of the patient pathway.
Brainomix 360 e-Lung includes similar chest CT processing features and technological characteristics as compared to the predicate device. The intended use and principles of operation of the subject device are the same as those of the predicate device. There are no changes to the predicate e-Lung implementation that change the previously cleared features. Significant differences between the subject and predicate e-Lung devices are as follows:
- . Improved algorithm for lung segmentation (AI/ML algorithm)
- . Automated grouping of scans that have had the same workflow applied and calculation of differences in density evaluations between CTs (longitudinal assessment feature)
- . Outputs in PACS and Cloud
Furthermore, minor improvements were also carried out with the aim of increasing the user's comfort when interacting with the device by providing additional PACS/DICOM viewing functionalities of adjustable slab thickness reconstructions, ability to view scans at original resolution and dynamic MPR with cross hair. Minor changes also include improvements in the visualization of outputs, additional user-defined options in relation to the structural density filters and improvement to the radiologist workflow by enabling networking and linking cases.
### 9. Clinical Characteristics
Brainomix 360 e-Lung can be used to support the physician when examining the pulmonary and thoracic tissue by providing quantifications of radiographic features through density evaluations and volumetric measurements including lung volume derived from a segmentation of the lungs.
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Image /page/8/Picture/0 description: The image shows the logo for BRAINOMIX. The logo consists of a stylized brain icon on the left, followed by the word "BRAINOMIX" in a sans-serif font. The brain icon is a gradient of blue and green, while the letters "AI" in the word "BRAINOMIX" are also in a similar gradient.
Quantification of these features provides additional objective and reproducible data which can be used by physicians in addition to the current standard of care to aid diagnosis and longitudinal assessment of lung disease.
#### Substantial Equivalence 10.
A table comparing the key features of the proposed and predicate device is provided below.
| Characteristics/<br>Parameter | Proposed Device<br>Brainomix 360 e-Lung | Predicate Device<br>Brainomix 360 e-Lung | Comparison |
|----------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 510(k) Number | K242411 | K233875 | N/A |
| Product Code | JAK | JAK | Identical |
| Regulation<br>Number | 21 CFR §892.1750 | 21 CFR §892.1750 | Identical |
| Regulation Name | System, X-Ray, Tomography,<br>Computed | System, X-Ray, Tomography,<br>Computed | Identical |
| Intended<br>Use/Indications<br>for Use | The e-Lung software provides<br>reproducible CT values for<br>pulmonary tissue, which is<br>essential for providing<br>quantitative support in the<br>examination of radiological<br>findings. These radiological<br>findings can then be evaluated by<br>the physician in conjunction with a<br>range of ancillary information to<br>form a potential diagnosis or list<br>of likely diagnoses. The e-Lung<br>software package is intended to<br>be a workflow enhancement and<br>visualization tool for the<br>assessment of CT thoracic<br>datasets. e-Lung can be used to<br>support the physician when<br>examining the pulmonary and<br>thoracic tissue (i.e. lung<br>parenchyma) in CT thoracic<br>datasets. 3D segmentation,<br>volumetric measurements, density<br>evaluations, and reporting tools<br>are combined with a dedicated<br>workflow. | The e-Lung software provides<br>reproducible CT values for<br>pulmonary tissue, which is<br>essential for providing<br>quantitative support in the<br>examination of radiological<br>findings. These radiological<br>findings can then be evaluated by<br>the physician in conjunction with a<br>range of ancillary information to<br>form a potential diagnosis or list<br>of likely diagnoses. The e-Lung<br>software package is intended to<br>be a workflow enhancement and<br>visualization tool for the<br>assessment of CT thoracic<br>datasets. e-Lung can be used to<br>support the physician when<br>examining the pulmonary and<br>thoracic tissue (i.e. lung<br>parenchyma) in CT thoracic<br>datasets. 3D segmentation,<br>volumetric measurements, density<br>evaluations, and reporting tools<br>are combined with a dedicated<br>workflow. | Identical |
| Image Source<br>Modalities | CT | CT | Identical |
| DICOM<br>Conformance | Yes | Yes | Identical |
| Interface Output | Web UI, PACS and cloud | Web UI | Different |
| Comparative<br>Review | 2D | 2D | Identical |
| 3D Lung Mapping | No | No | Identical |
| Characteristics/<br>Parameter | Proposed Device<br>Brainomix 360 e-Lung | Predicate Device<br>Brainomix 360 e-Lung | Comparison |
| 3D<br>Measurements | 3D volume of masks | 3D volume of masks | Identical |
| 2D<br>Measurements | None | None | Identical |
| Density<br>Measurements | Admin user defined density<br>histogram evaluation and<br>structural density filtering<br>evaluation | Admin user defined density<br>histogram evaluation and<br>structural density filtering<br>evaluation | Identical |
| Deployment | Standard off-the-shelf server or<br>virtual server | Standard off-the-shelf server or<br>virtual server | Identical |
| OS | Ubuntu Linux | Ubuntu Linux | Identical |
| User Interface | Yes | Yes | Identical |
| Algorithm | Each voxel of the scan is measured<br>by Hounsfield Units (HU), a<br>measurement of x-ray attenuation<br>that is applied to each volume<br>element in three-dimensional<br>space ('voxel'). The HU are utilized<br>to distinguish between air, water,<br>tissue and bone, such distinction is<br>common in the industry.<br>An AI/ML image processing<br>approach is applied to CT imaging<br>data to automatically segment<br>lung mask. | Each voxel of the scan is measured<br>by Hounsfield Units (HU), a<br>measurement of x-ray attenuation<br>that is applied to each volume<br>element in three-dimensional<br>space ('voxel'). The HU are utilized<br>to distinguish between air, water,<br>tissue and bone, such distinction is<br>common in the industry.<br>A (non-AI) image processing<br>approach is applied to CT imaging<br>data to automatically segment<br>lung mask. | Similar, both<br>devices<br>segment a<br>lung mask,<br>however,<br>the<br>proposed<br>device<br>utilizes an<br>AI/ML<br>algorithm to<br>perform this<br>task |
| Workflow | Automated segmentation<br>Automated measurements<br>(including those based on user<br>configurations)<br>Automated grouping of scans<br>from the same patient and<br>automated measurements | Automated segmentation<br>Automated measurements<br>(including those based on user<br>configurations) | Similar,<br>added<br>longitudinal<br>assessment<br>feature<br>(grouping of<br>scans from<br>the same<br>patient) |
| Graphic User<br>Interface | Yes | Yes | Identical |
| Interactive 3D<br>Visualization | No | No | Identical |
| Input/Output | Users can browse, select, and load<br>CT scan files. Users can save and<br>load analyses, export via reporting<br>tools. CT scan files are organized<br>by patient in the scan viewer. User<br>can generate a report that<br>displays quantitative data items<br>that can be saved. DICOM info<br>displayed. Data import through<br>DICOM query/retrieve available. | Users can browse, select, and load<br>CT scan files. Users can save and<br>load analyses, export via reporting<br>tools. CT scan files are organized<br>by patient in the scan viewer. User<br>can generate a report that<br>displays quantitative data items<br>that can be saved. DICOM info<br>displayed. Data import through<br>DICOM query/retrieve available. | Identical |
| Path Planning | No | No | Identical |
| User Editing | Yes | Yes | Identical |
| Reports | Yes – CSV, Excel and PDF format | Yes – CSV, Excel and PDF format | Identical |
| Characteristics/<br>Parameter | Proposed Device<br>Brainomix 360 e-Lung | Predicate Device<br>Brainomix 360 e-Lung | Comparison |
| Scan Quality<br>Assessment | Scan protocol is assessed for<br>compatibility with software Incompatibility issues flagged<br>during import and on report | Scan protocol is assessed for<br>compatibility with software Incompatibility issues flagged<br>during import and on report | Identical |
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Image /page/9/Picture/0 description: The image shows the logo for BRAINOMIX. The logo consists of a stylized brain icon on the left, followed by the word "BRAINOMIX" in a sans-serif font. The brain icon is split into two halves, with a gradient of blue and green. The word "BRAINOMIX" is in a dark gray color, with the "A" in "BRAINOMIX" having a blue gradient.
Head office
First Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom
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Image /page/10/Picture/0 description: The image shows the logo for BRAINOMIX. The logo consists of a teal-colored graphic on the left, resembling a brain. To the right of the graphic is the word "BRAINOMIX" in a sans-serif font, with the "AI" in a gradient of teal to blue.
First Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom
Table 1. Comparison of the key features of the subject and predicate device.
## 11. Conclusion
Brainomix 360 e-Lung includes similar chest CT processing features and technological characteristics as compared to the predicate device. The intended use and principles of operation of the subject device are the same as those of the predicate device. The differences in technological characteristics for the proposed device do not raise different questions of safety or effectiveness.
The proposed device does not raise different questions of safety and demonstrates substantial equivalence to the predicate, Brainomix 360 e-Lung (K233875).
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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.