K233875 · Brainomix Limited · JAK · May 13, 2024 · Radiology
Device Facts
Record ID
K233875
Device Name
Brainomix 360 e-Lung
Applicant
Brainomix Limited
Product Code
JAK · Radiology
Decision Date
May 13, 2024
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.1750
Device Class
Class 2
Attributes
Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K233875 · May 13, 2024
Brainomix 360 e-Lung
Brainomix Limited
Research registry (Boston Medical Center); Commercial database of clinical imaging data
Retrospective validation of lung segmentation functionality using clinical CT datasets to demonstrate performance across diverse demographic and clinical subgroups.
Retrospective lung segmentation validation study; Retrospective study
Patients undergoing CT chest studies for evaluation of lung disease; Sample Size: 100; Number of Sites: Not specified (data from two sources: Boston Medical Center and a commercial database)
Ground truth mask generated from the consensus of three experienced US board certified radiologists
Dice Similarity Coefficients (DSC)
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 within the Brainomix 360 platform; it processes CT thoracic datasets to provide quantitative pulmonary tissue analysis. The device takes CT slice data as input; it uses non-adaptive deterministic image processing algorithms to perform 3D segmentation of lung masks, volumetric measurements, and density evaluations (structural density filtering and histogram evaluation). It operates on off-the-shelf physical or virtual servers. The software generates screen displays, computations, and aggregate statistics, which are exported as CSV, Excel, or PDF reports. It is used by physicians to enhance clinical workflows by automating time-consuming manual tasks like lung segmentation and density quantification. The output assists the physician in examining radiological findings to form a potential diagnosis. The device does not perform 3D rendering or functions that cannot be accomplished by manual tracing; it serves as a workflow enhancement tool.
Clinical Evidence
Retrospective study (N=100) validated lung segmentation accuracy against a ground truth consensus of three board-certified radiologists. Primary endpoint was Dice Similarity Coefficient (DSC). Results showed a median DSC of 0.978 (IQR: 0.974-0.980), exceeding the acceptability criterion of >0.95. Performance was consistent across demographic subgroups (age, gender, BMI, race), scanner manufacturers (Siemens, GE, Philips, Toshiba), and imaging parameters (slice thickness, contrast). Density analysis was validated using synthetic digital phantom data and a real-world bridging study, achieving a minimum DSC of 0.80.
Technological Characteristics
Software-only device; runs on off-the-shelf physical or virtual servers (Ubuntu Linux). Uses DICOM standard for data input/output. Core technology includes non-adaptive deterministic image processing algorithms for 3D lung segmentation, volumetric measurement, and density evaluation (Hounsfield unit-based). No 3D rendering capability. Connectivity via DICOM query/retrieve. Compliant with ISO 14971:2019 for risk management.
Indications for Use
Indicated for physicians to provide quantitative support in the examination of radiological findings in CT thoracic datasets, specifically for the assessment of pulmonary and thoracic tissue (lung parenchyma).
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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May 13, 2024
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Brainomix Limited % Thais Sala Senior Quality Assurance and Regulatory Affairs Manager First Floor, Seacourt Tower West Way OXFORD. OX2JJ UNITED KINGDON
Re: K233875
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: December 7, 2023 Received: April 15, 2024
Dear Thais Sala:
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 (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/cdrb/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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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-devices/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-Rav Systems Team DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Ouality Center for Devices and Radiological Health
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#### Indications for Use
510(k) Number (if known) K233875
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 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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Image /page/3/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 light blue to dark blue. The letters "B", "R", "N", "O", "M", "X" are dark gray, while the letters "A" and "I" are a gradient of light blue to dark blue.
### K233875
# 510(K) Summary Brainomix Limited – Brainomix 360 e-Lung
| Date Prepared: | 07Dec2023 |
|--------------------------|----------------------------------------------------------------------------|
| Applicant's Name: | Brainomix Limited |
| Applicant's Address: | First Floor, Seacourt Tower, West Way<br>Oxford, OX2 0JJ<br>United Kingdom |
| Official Contact: | Thais Sala<br>+44 (0) 7375 967 695<br>tsala@brainomix.com |
| Device Proprietary Name: | Brainomix 360 e-Lung |
| Regulation Name: | Computed tomography x-ray system |
| Regulatory Class: | Class II |
| Product Code: | JAK |
| Regulation Number: | 21 C.F.R. §892.1750 |
#### 1. Predicate Device
Brainomix 360 e-Lung is Substantially Equivalent to the following Legally Marketed device:
Trade Name: Vida | Vision Manufacturer: VIDA Diagnostics Inc. Regulation Number: 21 C.F.R. §892.1750 Regulatory Class: Class II Regulation Name: Computed tomography x-ray system Product Code: JAK Submission Number: K200990
#### 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.
e-Lung can be used to support the physician in the examination of radiological findings that may be indicative of chest diseases e.g. when examining the pulmonary and thoracic tissue (i.e. lung parenchyma) in CT thoracic datasets. These radiological findings can then be evaluated by the
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Image /page/4/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 light blue, while the letters "AI" in "BRAINOMIX" are also in the same light blue gradient, contrasting with the rest of the word which is in a dark gray color.
physician 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
#### 4.1 Clinical Study (lung segmentation)
A retrospective study has been carried out to validate the lung segmentation functionality of e-Lung. 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. The similarity between the device lung masks and the ground truth lung masks was evaluated using Dice Similarity Coefficients (DSC), which index the amount of overlap between the two masks. The acceptability criterion for the study was an average DSC across all cases of over 0.95 (defined by the lower bound of the confidence interval).
The dataset for this validation study comprised tomography (CT) chest studies performed in the clinical context of evaluation of lung disease. Cases were selected from two sources: from a research registry held at Boston Medical Center (N=38) and from a commercial database of clinical imaging data (N=62). The number of cases from different scanner manufacturers and models
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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 a sans-serif font. The "A" in "BRAINOMIX" is designed with a gradient fill, transitioning from blue to green.
is shown in Table 1. A range of convolution kernels and acquisition parameters were included in the dataset, including 24 cases acquired with contrast.
The dataset was enriched to ensure a distribution of clinical and demographic variables (e.g. age, gender, race/ethnicity, clinical site, BMI, smoking history and radiological findings) that allows generalizability to the patient population for whom use is intended. The median age was 61 years (inter-quartile range [IQR]: 52.5-70, total range: 23-100). The number of cases in relevant demographic or clinical subgroups is shown in Table 2.
All cases were successfully processed by the device. All cases showed a DSC of above the acceptability criterion (DSC=0.95). The distribution of DSC is shown in Figure 1. The median DSC was 0.978 (IQR: 0.974-0.980). Hence, the study met the acceptability criterion. Performance was consistent for the left and right lungs (left: median DSC=0.976, IQR: 0.972-0.978; right: median DSC=0.979, IQR: 0.976-0.981). Table 3 reports the median (and IQR) DSC for clinically-relevant subgroups, including age, gender, race/ethnicity, BMI, smoking status, radiological findings, hospital location, scanner manufacturer, slice thickness, KvP and contrast. Lung segmentation performance of the device was consistent across all subgroups.
| Manufacturer | Model | N |
|--------------|------------------------|----|
| SIEMENS | All | 36 |
| | SOMATOM Perspective | 22 |
| | SOMATOM go.Up | 6 |
| | SOMATOM Definition AS+ | 3 |
| | SOMATOM Definition AS | 2 |
| | Sensation 16 | 2 |
| | SOMATOM go.Top | 1 |
| GE | All | 28 |
| | Revolution Apex | 9 |
| | Revolution Frontier | 8 |
| | Revolution CT | 7 |
| | LightSpeed VCT | 2 |
| | BrightSpeed S | 1 |
| | Discovery STE | 1 |
| Philips | All | 23 |
| | IQon - Spectral CT | 12 |
| | Brilliance 16 | 4 |
| | Brilliance 64 | 4 |
| | Brilliance 6 | 2 |
| | Gemini | 1 |
| Toshiba | All | 13 |
| | Aquilion | 9 |
Table 1. Table showing the number of cases in the lung segmentation different scanner manufacturers and models.
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Image /page/6/Picture/0 description: The image shows the logo for BRAINOMIX. The logo consists of a stylized brain image on the left, followed by the word "BRAINOMIX" in a sans-serif font. The "A" and "I" in "BRAINOMIX" are in a lighter blue color, while the rest of the letters are in a darker gray color. The brain image is also in the lighter blue color.
Aquilion ONE 4
#### Distribution of DSC, Ground Truth vs Device
Image /page/6/Figure/4 description: This image is a histogram showing the distribution of DSC values. The x-axis represents the DSC values, ranging from 0.95 to 0.99, while the y-axis represents the frequency, ranging from 0 to 40. The histogram shows a distribution that is skewed to the left, with the highest frequency of DSC values around 0.97 to 0.98. The image also includes the text 'W = 0.913, p < .001'.
Figure 1.Histogram showing the distribution of Dice Similarity Coefficients (DSC) between ground truth lung masks and e-Lung device lung masks. The results of the Shapiro-Wilks normality test is also shown.
Table 2. Median Dice Similarity Coefficients (DSC) and interquartile ranges (IQR) for cases stratified by relevant demographic, clinical and imaging variables. The number of cases per subgroup (N) is also shown.
| Subgroup Variable | N | Median DSC [IQR] |
|---------------------------|----|-----------------------|
| Age: | | |
| Under 60 | 45 | 0.978 [0.974 - 0.980] |
| 60 or over | 55 | 0.978 [0.974 - 0.980] |
| Gender: | | |
| Female | 55 | 0.978 [0.973 - 0.979] |
| Male | 45 | 0.978 [0.976 - 0.981] |
| Race / Ethnicity: | | |
| Black or African American | 14 | 0.974 [0.970 - 0.978] |
| White | 11 | 0.978 [0.976 - 0.978] |
| Hispanic or Latino | 7 | 0.974 [0.970 - 0.978] |
| Asian | 3 | 0.980 [0.979 - 0.981] |
| Subgroup Variable | N | Median DSC [IQR] |
| Other | 3 | 0.974 [0.972-0.978] |
| Unknown / declined | 62 | 0.978 [0.975 - 0.980] |
| BMI: | | |
| Underweight | 3 | 0.985 [0.980 - 0.985] |
| Healthy Weight | 6 | 0.977 [0.973 - 0.978] |
| Overweight | 17 | 0.977 [0.972 - 0.980] |
| Obese | 12 | 0.974 [0.971 - 0.978] |
| Unknown | 72 | 0.978 [0.975 - 0.980] |
| Smoking Status: | | |
| Current smoker | 25 | 0.978 [0.977 - 0.981] |
| History of smoking | 18 | 0.978 [0.978 - 0.979] |
| Non-smoker | 23 | 0.975 [0.972 - 0.978] |
| Unknown | 34 | 0.978 [0.972 - 0.980] |
| Radiological Findings: | | |
| Ground Glass | 20 | 0.979 [0.972-0.979] |
| Linear Opacities | 35 | 0.978 [0.977-0.981] |
| Airways Abnormalities | 22 | 0.979 [0.978-0.981] |
| Extrapulmonary Findings | 18 | 0.978 [0.974-0.979] |
| Nodules | 48 | 0.978 [0.976-0.980] |
| Atelectasis | 12 | 0.974 [0.971-0.978] |
| Hospital Location: | | |
| Massachusetts | 38 | 0.976 [0.972 - 0.978] |
| New York | 31 | 0.978 [0.978 - 0.980] |
| Ohio | 17 | 0.978 [0.976 - 0.980] |
| New Jersey | 4 | 0.962 [0.958 - 0.966] |
| Wisconsin | 4 | 0.980 [0.978 - 0.980] |
| Subgroup Variable | N | Median DSC [IQR] |
| Florida | 3 | 0.972 [0.970 - 0.976] |
| Maryland | 2 | 0.974 [0.971 - 0.976] |
| South Dakota | 1 | 0.980 [NA] |
| Scanner Manufacturer: | | |
| SIEMENS | 36 | 0.979 [0.977 - 0.979] |
| GE Medical Systems | 28 | 0.976 [0.976 - 0.981] |
| Philips | 23 | 0.975 [0.970 - 0.978] |
| Toshiba | 13 | 0.980 [0.977 - 0.981] |
| Slice Thickness: | | |
| Under 3mm | 70 | 0.978 [0.973 - 0.979] |
| 3mm or over | 30 | 0.979 [0.977 - 0.980] |
| KvP: | | |
| 100 | 3 | 0.978 [0.976 - 0.978] |
| 110 | 24 | 0.979 [0.978 - 0.980] |
| 120 | 55 | 0.978 [0.972 - 0.980] |
| 130 | 5 | 0.977 [0.972 - 0.978] |
| 140 | 13 | 0.975 [0.974 - 0.981] |
| Contrast: | | |
| Without contrast | 76 | 0.978 [0.976 - 0.980] |
| With contrast | 24 | 0.974 [0.970 - 0.978] |
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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 sans-serif font. The brain icon is teal, and the letters "AI" in "BRAINOMIX" are also teal, while the rest of the letters are dark gray.
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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 the same gradient of blue and green, while the rest of the letters are in black.
#### Digital Phantom (Density Evaluations) 4.2
Specific validation has been carried out to test the density analysis output (structural density filtering and density histogram evaluation). This testing made use of synthetic digital phantom data together with a realworld data bridging study created to provide a ground truth against which the performance of the density evaluations can be assessed and demonstrated.
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Image /page/9/Picture/0 description: The image shows the word "BRAINOMIX" in all caps. To the left of the word is a blue and white graphic that resembles a brain. The "A" in "BRAINOMIX" is also blue, while the rest of the letters are black.
The density evaluations are validated by ensuring a good Dice score (min 0.80) between the e-Lung structural densities and histogram densities and those pre-defined parameters generated in the digital phantom dataset.
#### 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).
# 7. Summary of Technological Characteristics
The proposed device (Brainomix 360 e-Lung) have the same intended use and substantially similar indications for use, core functionalities and outputs compared to the predicate device (VIDA | vision). Both devices have substantially equivalent indications for use, technical approaches and roles within a clinical workflow relative to the analysis of pulmonary tissue on CT Thoracic datasets. The proposed device does not introduce any new risks when compared to the predicate.
Where the proposed device and predicate device differ in technical characteristics is that:
- . e-Lung offers a subset of the intended use and functionality offered by the predicate (e.g., proposed device does not provide reconstruction of two-dimensional images into a threedimensional image format) and therefore, the risks associated with these types of analysis are not applicable to the proposed device.
- e-Lung offers a peripheral sub-compartment of the lungs which is generated based on the lung segmentation and a depth selected by the user from a range of 5 and 25mm at 5mm increments. In the proposed device, the preferred depth is defined by a trained clinical professional with an admin user privilege, the user can also choose not to have a peripheral sub-compartment. Describing the distribution of radiographic features in terms of their proximity to the center of the body or the periphery can be helpful in diagnosing the causative disease. This device is intended to support the user in the examination of radiological findings as an enhancement and visualization tool which provides reproducible CT values for pulmonary tissue. Therefore, the technical differences in sub-compartment method between the proposed and predicate device do not raise different questions of safety and effectiveness.
- . e-Lung offers density histogram evaluation and structural density filtering evaluation where the metrics for the density evaluations are defined by a trained clinical professional with an admin user privilege. The density evaluations support the intended use of the device (examination of the pulmonary and thoracic tissue) and are volumetric calculations of imaging features with parameters defined by a trained user. Furthermore, the outputs of the density evaluation have been validated as part of this submission. Therefore, the technical
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Image /page/10/Picture/0 description: The image shows the logo for BRAINOMIX. The logo consists of a stylized brain image on the left, followed by the word "BRAINOMIX" in a sans-serif font. The brain image is a gradient of light blue to dark blue, and the letters "AI" in the word "BRAINOMIX" are also in the same gradient of blue, while the rest of the letters are in a dark gray color.
differences in density evaluations between the proposed and predicate device do not raise different questions of safety and effectiveness.
- . The proposed device uses a non-adaptive deterministic algorithm to automatically segment the lung mask, similarly to the predicate device, with comprehensive validation so therefore does not add any new risks or raise different questions of safety and effectiveness given the minor difference in algorithms.
We conclude that Brainomix 360 e-Lung is safe and effective, raises no unanswered questions with regards to safety and efficacy and is substantially equivalent to the chosen predicate, VIDA|vision.
### 8. Substantial Equivalence
| Characteristics/Para | Proposed Device | Predicate Device |
|----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| meter | Brainomix 360 e-Lung | VIDA vision |
| 510(k) Number | K233875 | K200990 |
| Product Code | JAK | JAK |
| Regulation Number | 21 CFR §892.1750 | 21 CFR §892.1750 |
| Regulation Name | System, X-Ray, Tomography,<br>Computed | System, X-Ray, Tomography,<br>Computed |
| Intended<br>Use/Indications for<br>Use | The e-Lung software provides<br>reproducible CT values for<br>pulmonary tissue, which is essential<br>for providing quantitative support in<br>the examination of radiological<br>findings. These radiological findings<br>can then be evaluated by the<br>physician in conjunction with a range<br>of ancillary information to form a<br>potential diagnosis or list of likely<br>diagnoses. The e-Lung software<br>package is intended to be a<br>workflow enhancement and<br>visualization tool for the assessment<br>of CT thoracic datasets. e-Lung can<br>be used to support the physician<br>when examining the pulmonary and<br>thoracic tissue (i.e. lung<br>parenchyma) in CT thoracic datasets.<br>3D segmentation, volumetric<br>measurements, density evaluations,<br>and reporting tools are combined<br>with a dedicated workflow. | The VIDA vision software provides<br>reproducible CT values for pulmonary<br>tissue, which is essential for providing<br>quantitative support for diagnosis and<br>follow up examinations. VIDA vision<br>can be used to support the physician in<br>the diagnosis and documentation of<br>pulmonary tissue images (e.g.,<br>abnormalities) from CT thoracic<br>datasets. Three-D segmentation and<br>isolation of sub-compartments,<br>volumetric analysis, density<br>evaluations, low density cluster<br>analysis and reporting tools are<br>combined with a dedicated workflow.<br>The VIDA vision software package is<br>also intended to be a real-time<br>interactive evaluation in space and<br>time for CT volume data sets that<br>provides the reconstruction of two<br>dimensional images into a three-<br>dimensional image format. |
| Characteristics/Parameter | Proposed Device<br>Brainomix 360 e-Lung | Predicate Device<br>VIDA vision |
| Image Source<br>Modalities | CT | CT |
| DICOM<br>Conformance | Yes | Yes |
| Comparative<br>Review | 2D | 2D, 3D |
| 3D Lung Mapping | No | Yes |
| 3D Measurements | 3D volume of masks | Volume Effective Diameter |
| 2D Measurements | None | Line and ROI tools with statistics<br><br>Diameter 2D<br><br>Area |
| Density<br>Measurements | Admin user defined density<br>histogram evaluation and structural<br>density filtering evaluation | Minimum, maximum and average HU |
| Deployment | Standard off-the-shelf server or<br>virtual server | Standalone computer / distributed |
| OS | Ubuntu Linux | Windows |
| User Interface | Yes | Yes |
| 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 space<br>('voxel'). The HU are utilised to<br>distinguish between air, water,<br>tissue and bone, such distinction is<br>common in the industry.<br><br>A (non-Al) image processing<br>approach is applied to CT imaging<br>data to automatically segment lung<br>mask. | Each voxel of the scan is measured by<br>Hounsfield Units (HU), a measurement<br>of x-ray attenuation that is applied to<br>each volume element in three<br>dimensional space ('voxel'). The HU are<br>utilised to distinguish between air,<br>water, tissue and bone, such<br>distinction is common in the industry.<br><br>A non-adaptive deep learning-based<br>algorithm is applied to the CT imaging<br>data to automatically segment lung<br>regions. |
| Workflow | Automated segmentation<br><br>Automated measurements<br>(including those based on user<br>configurations) | Automated contouring<br><br>Automated measurements<br>Manual Correction<br><br>Distinct user workflows: |
| Characteristics/Parameter | Proposed Device | Predicate Device |
| | Brainomix 360 e-Lung | VIDA vision |
| Graphic User<br>Interface | Yes | Yes |
| Interactive 3D<br>Visualisation | No | Yes |
| Input/Output | Users can browse, select, and load CT scan files. Users can save and load analyses, export via reporting tools. CT scan files are organized by patient in the scan viewer. User can generate a report that displays quantitative data items that can be saved. DICOM info displayed. Data import through DICOM query/retrieve available. | Users can browse, select, and load CT scan files. Users can save and load analyses, export via reporting tools. CT scan files can be organized by user-defined projects, and tracked by usage. User can generate a report that displays quantitative data items that can be saved. DICOM info displayed. Data import through DICOM query/retrieve available. |
| Path Planning | No | Yes – airways and lung tissue |
| User Editing | Yes | Yes |
| Reports | Yes – CSV, Excel and PDF format | Yes – CSV and PDF format configurable for specific use cases |
| Scan Quality<br>Assessment | Scan protocol is assessed for compatibility with software Incompatibility issues flagged during import and on report | Scan protocol is assessed for compatibility with software Incompatibility issues flagged during import and on report Scanner calibration assessment Warning issued for out-of-range air/blood measurements |
A table comparing the key features of the subject and predicate devices is provided below.
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Image /page/11/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, and the "A" in "BRAINOMIX" is also in the same gradient.
Head office
First Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom
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Image /page/12/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 "A" in "BRAINOMIX" is a gradient of blue and teal, while the rest of the letters are gray.
Oxford OX2 0JJ, United Kingdom
# 9. Conclusion
In conclusion, Brainomix 360 e-Lung has the same intended use and is substantially equivalent in technological characteristics, safety, and performance characteristics to the legally marketed predicate device, VIDA|vision (K200990). Brainomix 360 e-Lung is therefore substantially equivalent to the selected legally marketed predicate device and does not raise any questions of safety or effectiveness.
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Learn the FDA Browser
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.