The Afina software is intended for importing, processing and displaying of retinal images and for the measurement of autofluorescence in the superior field (a circular 50-degree field of view at 20 degrees above fixation) of the retina. Afina is indicated for use only with images collected with validated ophthalmic cameras.
Device Story
Afina is software for processing retinal autofluorescence (AF) images; inputs consist of at least 8 AF images of the superior retinal field (DICOM study) captured via fundus camera. Software combines images to enhance quality; identifies pixels and contiguous pixel clusters (spots) exhibiting brightness above retinal background; quantifies findings as total spot count and total pixel sum. Output is a single-field patient report presenting enhanced images with identified findings and quantified metrics. Used by healthcare professionals in clinical settings; does not interact directly with patients. Provides objective measurements to support clinical decision-making; does not provide specific clinical diagnosis. Benefits include standardized, objective quantification of retinal AF findings to aid professional assessment.
Clinical Evidence
Clinical study evaluated repeatability and reproducibility of Pixel Count and Spot Count measurements in 32 subjects (ages 44-81) with various retinal conditions and normal eyes. 25 eyes provided qualified data. Results showed Spot Count repeatability %CV of 11.6% and reproducibility %CV of 19.9%. Pixel Count repeatability %CV was 8.7% and reproducibility %CV was 14.4%. No adverse events reported. Study concluded device is fit for purpose as a measurement tool.
Technological Characteristics
Software-only device; runs on Linux platform. Performs image processing, registration (fovea/optic nerve head identification), and segmentation of retinal autofluorescence images. Connectivity via DICOM standard. Cybersecurity managed via HIPAA-compliant, HITRUST-certified cloud hosting. Software level of concern: moderate.
Indications for Use
Indicated for use by healthcare professionals to import, process, and display retinal images and measure autofluorescence in the superior retinal field (50-degree circular field, 20 degrees above fixation) using validated ophthalmic cameras.
Regulatory Classification
Identification
A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.
Special Controls
*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
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November 2, 2018
NeuroVision Imaging, LLC Pepe Davis VP of Regulatory Affairs and Ouality Assurance 1395 Garden Highway, Suite 250 Sacramento, CA 95833
Re: K181501
Trade/Device Name: Afina Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: NFJ Dated: September 20, 2018 Received: September 24, 2018
Dear Pepe Davis:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
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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 (reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/CombinationProducts/GuidanceRegulatoryInformation/ucm597488.html; good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/) and CDRH Learn (http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/DICE) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely yours,
Alexander Beylin -S 2018.11.02 11:04:52 -04'00' for Malvina B. Eydelman, M.D. Director Division of Ophthalmic and Ear, Nose and Throat Devices Office of Device Evaluation Center for Devices and Radiological Health
Enclosure
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# Indications for Use
510(k) Number (if known) K181501
Device Name Afina
Indications for Use (Describe)
The Afina software is intended for importing, processing and displaying of retinal images and for the measurement of autofluorescence in the superior field (a circular 50-degree field of view at 20 degrees above fixation) of the retina. Afina is indicated for use only with images collected with validated ophthalmic cameras.
Type of Use (Select one or both, as applicable):
| <span style="font-size:10pt"><input checked="true" type="checkbox"/> Prescription Use (Part 21 CFR 801 Subpart D)</span> |
|--------------------------------------------------------------------------------------------------------------------------|
| <span style="font-size:10pt"><input type="checkbox"/> Over-The-Counter Use (21 CFR 801 Subpart C)</span> |
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# 510(k) Summary
This 510(k) summary of safety and effectiveness information is being submitted in accordance with the requirements of SMDA 1990 and 21 CFR 807.92
### 1 SUBMITTER'S NAME, ADDRESS, CONTACT PERSON AND DATE PREPARED (21 CFR §807.92(A) (1))
- Applicant: NeuroVision Imaging, LLC 1395 Garden Highway, Suite 250 Sacramento, CA 95833 Establishment Registration Number: 3012256561
Pepe Davis Contact Person: (925) 286-7432 pdavis@neurovision.com
October 22, 2018 Date Prepared:
#### 2 NAME OF DEVICE AND CLASSIFICATION (21 CFR §807.92(A) (2))
Trade Name: Afina (Release 1.1) Common Name: Picture archiving and communications system Regulation Number: 21 CFR 892.2050 Classification Name: System, Image Management, Ophthalmic Product Code: NFJ Device Class: Class II
#### Predicate Device (21 CFR §807.92(a) (3)) 3
Predicate Device: Zeiss Retina Workplace, K170638
This predicate has not been subject to a design-related recall.
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#### DEVICE DESCRIPTION (21 CFR §807.92(A) (4)) বা
Afina is software that transforms a set of field-identical autofluorescent (AF) images captured with a fundus camera into a single enhanced image with improved quality characteristics. It identifies and quantifies AF findings over the retinal AF background within the derived image.
Afina supports licensed healthcare professionals in importing, processing and displaying of retinal images and for the measurement of autofluorescence in the superior field of the retina.
Functionally, Afina imports a set of at least 8 AF images taken of the superior field of the retina (also referred to as a DICOM study), combines them in order to enhance image quality, and then identifies pixels and areas of contiquous pixels (spots) that exhibit autofluorescence that is brighter than the retinal AF background. Once identified, these pixels are quantified as both a count of spots and an overall sum of pixels. These data are consolidated into a single-field patient report that presents images of the field with AF findings identified and spot and pixel counts quantified.
A risk assessment has been performed to identify potential risks of Afina. The risks were evaluated and reduced as far as possible to ensure the safety and effectiveness of Afina. The software is to be used by healthcare professionals and does not directly interact with patients. The report produced by the Afina software provides an objective measurement of autofluorescence but does not provide specific clinical diagnosis. Interpretation of the measurements are made by healthcare professionals.
#### INDICATIONS FOR USE (21 CFR §807.92(A) (5)) 5
The Afina software is intended for importing, processing and displaying of retinal images and for the measurement of autofluorescence in the superior field (a circular 50-degree field of view at 20 degrees above fixation) of the retina. Afina is indicated for use only with images collected with validated ophthalmic cameras.
#### Technological Characteristics (21 CFR §807.92(a) (6)) 6
The Afina software is technologically similar to its predicate, the Zeiss Retina Workplace. Both devices are software analysis tools which import DICOM images of the retina and process them to produce segmented images and measurements of findings of interest. The Afina software can identify the fovea and optic nerve head to register images similarly to the Zeiss Retina Workplace. The Afina software combines fundus images and identifies findings of interest in autofluorescence images of the retina similarly to the Zeiss Retina Workplace's identification of findings of interest in OCT images of the retina. Overlays of interest are similarly presented on enhanced fundus images highlighting pixels and spots of autofluorescence above the retinal background autofluorescence. A measurement of this autofluorescence is provided
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as a pixel count and as a spot count (where contiguous regions of highlighted pixels are counted).
The following table summarizes the characteristics of the Afina software as compared to the predicate device.
| Characteristic | Proposed Device | Predicate Device |
|----------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Trade Name | NeuroVision Afina | Zeiss Retina Workplace 2.0 |
| Product code(s) | NFJ | NFJ |
| Device | System, Image Management, Ophthalmic | System, Image Management, Ophthalmic |
| Regulation Number | 892.2050 | 892.2050 |
| Classification | Class II | Class II |
| Intended Use | Intended to import, process and display fundus images for viewing and quantification of posterior segment findings | Intended to import, process and display fundus images for viewing and quantification of posterior segment findings |
| Indications for Use | The Afina software is intended for importing, processing and displaying of retinal images and for the measurement of autofluorescence in the superior field (a circular 50-degree field of view at 20 degrees above fixation) of the retina. | The Retina Workplace is a FORUM application intended for processing and displaying image and optical coherence tomography data. It is also intended for generating reports that contain results from optical coherence tomography and fundus photography.<br>The Retina Workplace uses CIRRUS algorithms and normative databases as quantitative tool for the comparison of macular |
| Characteristic | Proposed Device | Predicate Device |
| Trade Name | NeuroVision Afina | Zeiss Retina Workplace 2.0 |
| | | thickness data to a database<br>of normal subjects.<br><br>The Retina Workplace is<br>intended to aid trained<br>healthcare professionals in<br>the detection, monitoring and<br>management of ocular<br>diseases including, but not<br>limited to, macular holes,<br>cystoid macular edema,<br>diabetic retinopathy and age-<br>related macular degeneration. |
| Prescription or OTC? | Prescription | Prescription |
| Software/Hardware? | Software Only | Software Only |
| Software Platform | Linux | Windows |
| Image data management? | Yes | Yes |
| Import of images of the<br>retina | Yes | Yes |
| Acceptance of images | Yes | Yes |
| Process images and data<br>from DICOM files | Yes | Yes |
| Register fundus images | Yes | Yes |
| Identify center of fundus | Yes | Yes |
| Characteristic | Proposed Device | Predicate Device |
| Trade Name | NeuroVision Afina | Zeiss Retina Workplace 2.0 |
| Analyze retinal images to identify and measure fundus findings of interest | Yes | Yes |
| Export reports with segmentation overlays | Yes | Yes |
| Export reports with measurements of fundus findings | Yes | Yes |
| Centralized storage of images and data? | No | No |
Table 1: Substantial Equivalence Comparison
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#### Performance Data (21 CFR §807.92(B)) 7
#### Non-clinical Performance Data (21 CFR §807.92(b) (1)) 7.1
The Afina 1.1 software was identified as having a moderate level of concern as defined in FDA guidance document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software documentation included:
Afina Software Development Plan
Afina Risk Management Plan
Afina Risk Analysis
Afina Risk Management Report
Afina Software Requirements
Afina Architecture Design Description
Afina Software Design Description
Afina Requirements Trace Matrix
Afina Software Verification Test Plan
Afina Software System Level Verification Test Report
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Afina Software Unit Level Verification Test Report Afina Validation Test Plan
Afina 1.1 Validation Test Report
Afina 1.1.0 Version Description Document
Full characterization of the technical parameters of all the components of the software, including a description of the algorithms were provided in the Afina Software Requirements, the Afina Architecture Design Description, and the Afina Software Design Description.
A comprehensive risk analysis was provided for the software with detailed description of the hazards, their causes and severity as well as acceptable methods for control of the identified hazards.
The cybersecurity considerations of data confidentiality, data availability, denial of service attacks and malware were adequately addressed using a HIPAA compliant and HITRUST certified cloud hosting platform. Risks related to failure of various software components and their potential impact on patient reports and operator failures were also adequately addressed in the risk analysis. This software documentation information provided sufficient evidence of safe and effective software performance.
Performance testing, in the form of software bench testing, was conducted in the form of Unit, and System level verification against the documented product requirements. Verification and validation testing was performed to evaluate the performance and functionality of the software and the software has been found to perform as intended. Each function and/or feature was tested by means of an appropriate test case for the test specification. The verification testing demonstrates that the device performance complies with specifications and requirements identified for the Afina software. The software verification activities were divided into two phases:
• Unit level test cases which included verification of specific image processing functionality as described in the Afina Software Requirements and the Afina Architecture Description. These included functional tests for rejecting images, aligning images, enhancing images and segmentation.
• System level test cases which included verification of file transfer interfaces including image import, report output and cybersecurity.
Use Case validation was completed by medical professionals using a production equivalent implementation of the Afina with verified software including representative data that are representative of clinical cases). The validation participants executed test cases that simulated the use of the software in a clinical environment and completed questionnaires rating the various aspects of the software.
Test plans and reports were provided including passffail criteria and results for verification and validation activities at the unit and system level. Verification and validation activities met their acceptance criteria successfully per test plan and proved that the product, Afina, meets its requirements and intended use.
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#### Clinical Performance Data (21 CFR §807.92(b) (2)) 7.2
A clinical study was performed to evaluate the repeatability and reproducibility of the Afina autofluorescence measurement for both Pixel Count and Spot Count. The study population included subjects with diseases that manifest in the retina (i.e. various levels and forms of cognitive impairment and subjects with retinal disease exhibiting autofluorescent findings) and normal subjects in order to demonstrate repeatability and reproducibility over a representative dynamic range.
# Summarv of Protocol
Thirty-two (32) eligible subjects were screened, consented and enrolled between April 21, 2017 and August 25, 2017. One eye of each subject underwent up to three repeated superior retina imaging runs with repositioning between runs (3 focal planes per run, with up to six images per focal plane) on each of three (3) randomly assigned Autofluorescence (AF) fundus cameras meeting the specification requirements for Afina (designated as Camera 1, 2 and 3). The operators were trained to follow the camera Instructions for Use (IFU) and the Afina image capture procedure outlined in the Afina IFU. The three trained camera operators were designated as Operator A, B and C, with two trained back-up operators. To evaluate reproducibility. Operator A used Camera 1. Operator B used Camera 2. and Operator C used Camera 3. Subjects completed imaging during one visit, and within one week of consent.
## Results
The demographics of the enrolled study population are shown in Table 2. The gender of the study subjects was reasonably distributed and the age of the study subjects ranged from 44 years to 81 years, with a median age of 72. Therefore, this is a good sample population for the purposes of characterizing the precision of the measurements provided by the Afina software.
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Table 2: Demographic Summary
| | n/N | % | |
|---------------------------|-------|-------|--|
| Gender | | | |
| Male | 18/32 | 56% | |
| Female | 14/32 | 44% | |
| Eye | | | |
| Left (OS) | 9/32 | 28% | |
| Right (OD) | 23/32 | 72% | |
| Age (year) | | | |
| N | 32 | | |
| Mean | 68 | | |
| SD | 11 | | |
| Median | 72 | | |
| Min | 44 | | |
| Max | 81 | | |
| 40 - 49 | 4/32 | 12.5% | |
| ૨૦ - ૨૦ | 4/32 | 12.5% | |
| 60 - 69 | 5/32 | 15.6% | |
| 70 - 79 | 15/32 | 46.9% | |
| 80 - 89 | 4/32 | 12.5% | |
| Ethnicity | | | |
| Asian/Pacific Islander | 2/32 | 6.3% | |
| Black or African American | 2/32 | 6.3% | |
| Caucasian | 27/32 | 84.3% | |
| Other | 1/32 | 3.1% | |
Of the 32 eyes imaged (288 planned measurement runs), 25 eyes had Afina qualified run data (194 completed measurements). The range of the autofluorescence measurements obtained from the 25 study eyes with Afina measurements was 2,222 to 58,110 for Pixel Count and 37 to 1350 for Spot Count.
Table 3 summarizes the results for the Spot Count measurements.
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| Statistics | All<br>Camera/Operator<br>Combinations | Camera 1 /<br>Operator A | Camera 2 /<br>Operator B | Camera 3 /<br>Operator C |
|--------------------------|----------------------------------------|--------------------------|--------------------------|--------------------------|
| Number of Eyes | 25 | 22 | 25 | 24 |
| Number of Successful | 194 | 63 | 66 | 65 |
| Mean Spot Count Value | 451.1 | 429.0 | 429.2 | 495.4 |
| Total Standard Deviation | 214.4 | 212.3 | 192.4 | 229.8 |
| Repeatability SD1 | 52.4 | 46.8 | 48.6 | 60.4 |
| Repeatability %CV2 | 11.6 | 10.9 | 11.3 | 12.2 |
| Reproducibility SD3 | 89.9 | - | - | - |
| Reproducibility %CV4 | 19.9 | - | - | - |
Table 3: Spot Count Precision Summary
1 Repeatability SD = Estimate of the standard deviation among measurements taken on the same eye using the same camera in the same testing session with repositioning.
2 Repeatability %CV = Repeatability SD = Mean x 100. measurements taken on the same eye using different cameras, including repeatability.
Reproducibility SD =Estimate of the standard deviation among measurements taken 3 with different camera/operators
- 4 Reproducibility %CV = Reproducibility SD + Mean x 100.
Table 4 summarizes the results for the Pixel Count measurement analysis. Because the measurement fits a constant %CV model, only the Repeatability %CV and Reproducibility %CV are reported here. The repeatability SD (0.087 on the log scale but variable in the original scale) is not presented in this summary table.
Table 4: Pixel Count Precision Summary
| Statistics | All<br>Camera/Operator<br>Combinations | Camera 1 /<br>Operator A | Camera 2 /<br>Operator B | Camera 3 /<br>Operator C |
|-------------------------|----------------------------------------|--------------------------|--------------------------|--------------------------|
| Number of Subjects | 25 | 22 | 25 | 24 |
| Number of Successful | 194 | 63 | 66 | 65 |
| Mean Pixel Count Value1 | 8398 | 7726 | 8842 | 8600 |
| Repeatability % CV1 | 8.7 | 9.5 | 8.1 | 8.2 |
| Reproducibility % CV1 | 14.4 | - | - | - |
1 Calculated according to NIST Engineering Statistics Handbook, Section 1.3.6.6.9. Lognormal Distribution: http://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm
Subjects were assessed during and after the imaging visit, and received a follow-up telephone call no later than two days following the visit, to assess for any adverse effects. There were no
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adverse events, serious adverse events, or anticipated adverse device effects reported during the study.
## Study Conclusion
The repeatability and reproducibility study showed the device to be safe and characterized well the variability of the measurements provided by the Afina software. There were no safety events reported.
The variability of the autofluorescence measurements provided by the Afina software shows the device is fit for its purpose as a measurement tool.
#### 7.3 Conclusions (21 CFR §807.92(b) (3))
The verification and validation testing demonstrate that the Afina software performs as intended and is functionally equivalent to the predicate device. The non-clinical and clinical performance data confirm that differences in technological characteristics do not raise different questions of safety and effectiveness.
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Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
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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.
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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.
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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.
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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.
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Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
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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.
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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.