AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
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K201369 · Sep 16, 2020
AVA (Augmented Vascular Analysis)
See-Mode Technologies Pte, Ltd.
Retrospective clinical ultrasound image datasets from multiple centers
Retrospective clinical datasets were used to validate the performance of AI-based algorithms for carotid IMT measurement, text recognition of annotations, and Doppler waveform analysis/classification.
Retrospective dataset; Multi-center; AI algorithm validation; Clinical ultrasound images
Retrospective dataset of 205 longitudinal B-mode carotid images
>1 (expert readers)
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—
Vascular ultrasound annotation text recognition
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Accuracy 92% to 96%
Retrospective vascular ultrasound dataset of 783 to 1432 images
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—
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Doppler velocity measurement
—
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Correlation coefficient of 0.98 for PSV and 0.97 for EDV
Retrospective dataset of 1117 images
>1 (clinicians)
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Lower limb Doppler waveform classification
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93% overall accuracy
Collection of 150 images
>1 (expert readers)
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—
Indications for Use
Analysis and reporting of vascular ultrasound images.
Device Story
AVA is a standalone, cloud-based software for vascular ultrasound analysis. It accepts DICOM-compliant images from ultrasound machines. Using AI/ML algorithms, it performs vessel wall segmentation, IMT measurement, Doppler velocity extraction (PSV/EDV), and annotation reading. It generates reports for clinician review, quality control, and finalization. Used by physicians and technicians in clinical settings to assist in cardiovascular health assessment. The software provides an adjunct tool for clinical decision-making; all outputs require clinician verification. Benefits include automated, standardized measurements and improved workflow efficiency compared to manual analysis.
Clinical Evidence
Bench testing only. Retrospective study of 205 B-mode carotid images showed IMT correlation of 0.89 with expert readers (outperforming predicate's 0.6). Text recognition accuracy for annotations ranged 92-96% (n=783-1432). Doppler signal processing (PSV/EDV) showed correlation coefficients of 0.98 and 0.97 (n=1117). Waveform type classifier (monophasic/biphasic/triphasic) achieved 93% accuracy (n=150).
Technological Characteristics
Standalone software; cloud-hosted; web-browser accessible. Uses AI/ML neural networks for image segmentation and signal processing. Inputs: DICOM-compliant vascular ultrasound images. Outputs: Vascular ultrasound reports. Compliant with IEC 62304:2006/AC:2015. Software level of concern: Moderate.
Indications for Use
Indicated for analysis, measurement, and reporting of DICOM-compliant vascular ultrasound images from carotid and lower limb arteries in the general population receiving such scans. Includes vessel wall segmentation, carotid intima-media thickness (IMT) measurement, Doppler velocity finding, and annotation reading. Intended for use by trained medical professionals (physicians, sonographers, radiologists, cardiologists). Not for independent medical advice or treatment recommendations.
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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September 16, 2020
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See-Mode Technologies Pte. Ltd. % Mr. Alan Donald President Matrix Medical Consulting, Inc. 8880 Rio San Diego Drive, Suite 800 SAN DIEGO CA 92108
Re: K201369
Trade/Device Name: AVA (Augmented Vascular Analysis) Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: August 17, 2020 Received: August 19, 2020
Dear Mr. Donald:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or post-marketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting
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combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about 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-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,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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# Indications for Use
510(k) Number (if known) K201369
Device Name
AVA (Augmented Vascular Analysis)
#### Indications for Use (Describe)
See-Mode AVA (Augmented Vascular Analysis) is a stand-alone, image processing software for analysis, measurement, and reporting of DICOM-compliant vascular ultrasound images obtained from carotid and lower limb arteries. The analysis includes segmentation of vessels walls and measurement of the intima-media thickness (IMT) of the carotid artery in B-Mode images, finding velocities in Doppler images, and reading annotations on the images. The software generates a vascular ultrasound report based on the image analysis results to be reviewed and approved by a qualified clinician after performing quality control. The client software is designed to run on a standard desktop or laptop computer. See-Mode AVA is intended to be used by trained medical professionals, including but not limited to physicians and medical technicians. The software is not intended to be used as an independent source of medical advice, or to determine or recommend a course of action or treatment for patients.
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 text "K201369" at the top. Below the text is a logo for See-Mode Technologies. The logo features a stylized brain with different colored sections, including yellow, green, red, and blue. The company name is written in a simple, sans-serif font.
#### SECTION 5 510(k) Summary
This "510(k) Summary" was prepared per section 807.92(c).
### ADMINISTRATIVE INFORMATION
| Date of Preparation: | September 14, 2020 |
|----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Prepared by: | Sadaf Monajemi, PhD. Cofounder and Director |
| Manufacturer: | See-Mode Technologies Pte. Ltd.<br>32 Carpenter Street #03-01<br>Singapore 059911<br>SINGAPORE<br>Email: sadaf@see-mode.com<br>Tel: +61 415 952 782<br>www.see-mode.com |
| Official Contact: | Dr. Sadaf Monajemi, PhD, Cofounder and Director<br>See-Mode Technologies<br>32 Carpenter Street #03-01<br>Singapore 059911<br>SINGAPORE<br>Email: sadaf@see-mode.com<br>www.see-mode.com |
### DEVICE NAME AND CLASSIFICATION
Trade/Proprietary Name: AVA (Augmented Vascular Analysis) Common Name: Picture archiving and communications system Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Classification Name: System, Image Processing, Radiological Review Panel: Radiology Regulatory Class: Class II Product Code: LLZ
### INTENDED USE
Analysis and reporting of vascular ultrasound images.
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Image /page/4/Picture/0 description: The image shows a logo for See-Mode Technologies. The logo features a stylized brain with different colored sections: yellow, green, red, and dark blue. Below the brain graphic, the text "See-Mode Technologies" is written in a simple, sans-serif font, with "See-Mode" in a larger, bolder font than "Technologies."
## INDICATIONS FOR USE
See-Mode AVA (Augmented Vascular Analysis) is a stand-alone, image processing software for analysis, measurement, and reporting of DICOM-compliant vascular ultrasound images obtained from carotid and lower limb arteries. The analysis includes segmentation of vessels walls and measurement of the intima-media thickness (IMT) of the carotid artery in B-Mode images, finding velocities in Doppler images, and reading annotations on the images. The software generates a vascular ultrasound report based on the image analysis results to be reviewed and approved by a qualified clinician after performing quality control. The client software is designed to run on a standard desktop or laptop computer. See-Mode AVA is intended to be used by trained medical professionals, including but not limited to physicians and medical technicians. The software is not intended to be used as an independent source of medical advice. or to determine or recommend a course of action or treatment for patients.
## DEVICE DESCRIPTION
See-Mode AVA (Augmented Vascular Analysis) is a standalone software for analysis and reporting of vascular ultrasound images. There is no dedicated medical equipment required for operation of this software except for an ultrasound machine that is the source of image acquisition. The software runs on a standard off-the-shelf computer and is accessible within a web browser.
See-Mode AVA takes as input DICOM-compliant vascular ultrasound images. The software uses proprietary algorithms for image analysis. including segmentation of vessel walls and measurement of the intima-media thickness (IMT) of the carotid artery in B-Mode images and finding peak systolic and end diastolic velocities (PSV and EDV) from Doppler images. The software generates a vascular ultrasound report based on the image analysis results to be reviewed and approved by a qualified clinician after performing quality control. Any information within this report must be fully reviewed and approved by a qualified clinician before the vascular ultrasound report is finalized.
See-Mode AVA is not intended to be used as an independent source of analysis and reporting vascular ultrasound images. Any information provided by the software has to be reviewed by a qualified clinician (including sonographers, radiologists, and cardiologists) and can be modified to correct any possible mistakes. The software provides multiple methods for performing quality control and modification of image analysis results. When the vascular ultrasound report is finalized by a qualified clinician, See-Mode AVA exports the report. This report can be used adjunctly with other medical data by a physician to help in the assessment of the cardiovascular health of the patient.
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Image /page/5/Picture/0 description: The image shows a logo for See-Mode Technologies. The logo features a stylized brain graphic with different sections colored in yellow, green, red, and gray. The text "See-Mode Technologies" is placed to the left of the brain graphic, with "See-Mode" in a larger, bolder font and "Technologies" in a smaller font below it.
# SUBSTANTIAL EQUIVALENCE
# 1. Predicate Device
Manufacturer: AtheroPoint LLC Trade Name: AtheroEdge 510(k) Identifier: K122022 Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Classification Name: System, Image Processing, Radiological Review Panel: Radiology Regulatory Class: Class II Product Code: LLZ Date Cleared: September 26, 2012
- 2. Tabular Comparison of Features and Specifications of the AVA Device and Predicate Device
| | Subject Device | Predicate Device | Notes |
|------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------|
| Manufacturer | See-Mode Technologies Pte. Ltd. | Atheropoint LLC | |
| Product name | See-Mode AVA (Augmented Vascular<br>Analysis) | AtheroEdge | |
| 510(k) number | Via this submission | K122022 | |
| Classification | Class II - 90 LLZ 892.2050/LLZ | Class II - 90 LLZ<br>892.2050/LLZ | Same |
| Intended Use | Analysis and reporting of vascular<br>ultrasound images | Analysis and reporting of<br>vascular ultrasound images | Same |
| Indications for Use | See-Mode AVA (Augmented Vascular<br>Analysis) is a stand-alone, image<br>processing software for analysis,<br>measurement, and reporting of<br>DICOM-compliant vascular ultrasound<br>images obtained from carotid and lower | The AtheroEdgeTM<br>software is a<br>Windows-based application<br>program used on a personal<br>computer for an automatic<br>measurement of the | Nearly the<br>same. See<br>discussion<br>below. |
| | limb arteries. The analysis includes<br>segmentation of vessels walls and<br>measurement of the intima-media<br>thickness (IMT) of the carotid artery in<br>B-Mode images, finding velocities in<br>Doppler images, and reading annotations<br>on the images. The software generates a<br>vascular ultrasound report based on the<br>image analysis results to be reviewed and<br>approved by a qualified clinician after<br>performing quality control. The client<br>software is designed to run on a standard<br>desktop or laptop computer.<br>See-Mode AVA is intended to be used by<br>trained medical professionals, including<br>but not limited to physicians and medical<br>technicians. The software is not intended<br>to be used as an independent source of<br>medical advice, or to determine or<br>recommend a course of action or<br>treatment for patients. | Intima-Media Thickness<br>(IMT) of the carotid artery<br>from images obtained from<br>ultrasound systems. | |
| Image Source | Ultrasound images | Ultrasound images | Same |
| Rx only? | Yes | Yes | Same |
| Operating<br>Platform | The software runs on a standard<br>"off-the-shelf" computer and can be<br>accessed within the software client web<br>browser. | Stand-alone application<br>program for use on a<br>personal computer with<br>Microsoft Windows. | Nearly the<br>same. See<br>discussion<br>below. |
| Image Format | DICOM | DICOM, JPEG and Windows<br>BMP | Same |
| Image storage and<br>report generation | Yes | Yes | Same |
| Automatic<br>distance<br>measurement of<br>the Intima- Media<br>thickness of<br>carotid artery | Yes | Yes | Same |
| Image<br>Compression | JPEG Loss-less | JPEG Loss-less | Same |
| Target Population | As the device is prescription only, the<br>target population of the device is anyone<br>in the general population that receives<br>the relevant carotid or lower limb artery<br>ultrasound scan | As the device is prescription<br>only, the target population of<br>the device is anyone in the<br>general population that<br>receives the relevant carotid<br>ultrasound scan | Same |
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Image /page/6/Picture/0 description: The image shows a logo for See-Mode Technologies. The logo features a stylized brain with different colored sections, including yellow, green, red, and blue. The text "See-Mode Technologies" is written in black font below the brain graphic.
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Image /page/7/Picture/0 description: The image shows a logo for See-Mode Technologies. The logo features a stylized brain with different sections colored in yellow, green, red, and blue. The lower portion of the brain is gray, and the words "See-Mode Technologies" are written in black text below the brain graphic. The word "See-Mode" is in a larger, bolder font than "Technologies."
# 3. Discussion of Similarities and Differences and Explanation of Differences
## Similarities:
Both devices have the same intended use and are indicated for the same use -providing a software based user's interface to view, process, and analyze carotid ultrasound images. Both devices use algorithms to segment and measure intima media thickness, and can store the results and generate reports.
The predicate device was cleared based on non-clinical supportive information, clinical images and data. Comparative non-clinical test results demonstrate that See-Mode AVA performs at least equivalently to the predicate device. Acceptance criteria and verification and validation data demonstrate that the device performs per its specifications and indications for use.
The comparison of technological characteristics, non-clinical performance data, clinical images, and software validation data demonstrate that See-Mode AVA is as safe, and effective when compared to the predicate device that is currently marketed for the same intended use and indications for use.
## Differences:
#### 1. Image processing algorithm:
The predicate device was designed using technology that was available a few years ago, [i.e., software programming using traditional image processing algorithms
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Image /page/8/Picture/0 description: The image shows a logo for See-Mode Technologies. The logo features a stylized brain with different sections colored in yellow, green, red, and gray. The text "See-Mode" is positioned to the left of the brain graphic, with the word "Technologies" appearing in a smaller font size below "See-Mode."
(such as edge tracking and gradient based algorithms)]. See references cited below for the predicate device1,2.
With the advancements in the field of artificial intelligence and machine learning, See-Mode AVA incorporates a logical update to use artificial intelligence for image analysis. Machine learning and artificial intelligence (AI) have been proven as an efficient and accurate method for analyzing medical images and is used among a wide range of legally marketed medical devices. See-Mode AVA benefits from the established machine learning methods to analyze ultrasound images and represents an improved technology that provides a clinically meaningful advantage over the legally marketed predicate device. See-Mode has conducted an Analytical Study to substantiate this claim (see Performance Data below).
#### 2. Operating platform:
The predicate device is designed to be installed on a personal computer with Microsoft Windows.
See-Mode AVA benefits from the advancements of cloud computing and is a software program running on a cloud platform and can be accessed using multiple platforms, including Windows.
Although the Predicate device slightly differs from the Subject device with regards to the operating platform, there are several examples of FDA cleared medical devices that use cloud platforms for medical image analysis (examples: Aidoc Briefcase Software - 510(k) Number K180647, CardioLogs ECG Analysis Platform -510(k) Number K170568, Arterys Cardio DL - 510(k) Number K163253). Based on this, and in combination with identification of hazards and appropriate risk controls related to this difference, we conclude that this difference in technology platform has no adverse impact on the safety or efficacy of the Subject device. Additionally, the fact that all users are utilizing the same (e.g., latest version) of the AVA software is considered to be an improvement to both safety and efficacy when compared to the predicate device.
#### 3. Analyzing lower limb ultrasound images:
<sup>1 &</sup>quot;Completely Automated Multiresolution Edge Snapper-A New ... " https://ieeexplore.ieee.ora/iel5/83/6151934/06026248.pdf.
<sup>2 &</sup>quot;Automated high-performance cIMT measurement techniques using ... " https://www.ncbi.nlm.nih.aov/pubmed/222555864.
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Image /page/9/Picture/0 description: The image shows a logo for See-Mode Technologies. The logo features a stylized brain with different colored sections: yellow, green, red, and gray. The text "See-Mode" is written in bold black letters to the left of the brain graphic, with the word "Technologies" appearing in a smaller font size below it.
See-Mode AVA can be used for analysis and reporting of lower limb ultrasound scans. Using the text recognition and the signal processing algorithm, the software can read doppler velocities and annotations from the images, detect the waveform type, populate them in the report and generate qualitative drawings of the stenosis in lower limb arteries based on doppler velocity ratios.
### PERFORMANCE DATA
Safety and performance of the subject device has been evaluated, verified and validated according to the software specifications and applicable performance standards. See-Mode Technologies has performed software verification testing for the subject device according to the FDA's guidance document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices", as well as "IEC 62304:2006/AC: 2015 - Medical Device Software - Software Lifecycle Processes".
The performance of the device has been evaluated on a retrospective dataset from multiple centers. A summary of the performance data is provided below:
- . Performance of the segmentation of B-mode carotid ultrasound images and measurement of intima-media thickness (IMT): The performance of the neural network was evaluated on a retrospective dataset of 205 longitudinal B-mode carotid images that were obtained using commercially available ultrasound systems using commercial linear transducers (including a 12-3 MHz linear transducer, and a 12-5 MHz linear transducer). The performance of the subject device was compared against 2 expert readers' measurements and the reported performance of the predicate device. It was observed that the results of the algorithm is strongly correlated with experts' annotations with IMT correlation coefficient of 0.89 with the average of two experts, while the IMT correlation coefficient between the two experts was 0.86. It was also observed that the results of the subject device outperforms the reported results of the predicate device with reported correlation coefficient of 0.6.
- Performance of the text recognition algorithm for reading annotations from the images: The performance of the text recognition algorithm was evaluated on a retrospective vascular ultrasound dataset. The performance was measured separately for reading different types of annotations (such as reading the vessel names or doppler velocities) with different number of test images varying from 783 to 1432 images. The text recognition algorithm had a high accuracy in reading different types of annotation varving from 92% to 96%.
- Performance of the signal processing algorithm for analysing doppler .
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Image /page/10/Picture/0 description: The image shows a logo for See-Mode Technologies. The logo features a stylized brain with different colored sections: yellow, green, red, and blue. Below the brain graphic, the text "See-Mode" is displayed in a bold, sans-serif font, with the word "Technologies" written in a smaller font size underneath.
waveforms: The performance of the signal processing algorithm for reading peak systolic velocity (PSV) and end diastolic velocity (EDV) was evaluated against the annotations (i.e. PSV and EDV values) on the images annotated by clinicians at the time of image acquisition. The comparison was done on a dataset of 1117 images where each image contains one EDV and one PSV value annotated on the image. Correlation coefficient and Bland-Altman plots were used to evaluate the performance of the signal processing algorithm. Correlation coefficient of 0.98 and 0.97 was obtained for reading PSV and EDV respectively.
- Performance of the waveform type classifier on lower limb doppler images: The performance of the doppler waveform type classifier (monophasic, biphasic, or triphasic) on lower limb images was evaluated on a collection of 150 images that represents the breadth of the use cases observed in the clinical field for classifying the waveform type in lower limb arteries. The performance was evaluated against the annotations (i.e. waveform type) by expert readers. It was observed that the algorithm was in strong agreement with the expert annotations with a 93% overall accuracy in detecting the waveform type.
Based on the performance data as well as the Verification and Validation Results, See-Mode Technologies believes that the subject device is substantially equivalent to the predicate and outperforms regarding the segmentation of B-mode carotid ultrasound images and measurement of intima-media thickness (IMT).
See-mode's risk analysis for AVA was completed, with the hazard risk analysis submitted as part of this application. All the risks identified with the subject device are acceptable and have been reduced as far as possible, in accordance with ISO 14971:2007 Medical devices -Application of risk management to medical devices.
The proposed medical device is considered as a Moderate Level of Concern following FDA Guidance document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and the required software documentation per FDA's guidance have been provided as part of the submission.
## Conclusions
In terms of user safety, similar devices have been utilized for many years with an excellent record of safety. The software technologies are the same as those in the predicate device and other legally marketed devices, and they are used for similar applications in similar manners. There are no new additional safety concerns raised by these technologies.
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Image /page/11/Picture/0 description: The image shows a logo for See-Mode Technologies. The logo features a stylized brain with different colored sections: yellow, green, red, blue, and gray. The text "See-Mode Technologies" is located to the left of the brain graphic, with "See-Mode" in a larger, bolder font than "Technologies."
Performance of the segmentation of B-mode carotid ultrasound images and measurement of intima-media thickness (IMT) outperformed the predicate device. The text recognition algorithm for reading annotations of images, the signal processing algorithm for analysing doppler waveforms and the waveform type classifier on lower limb doppler images were all strongly correlated with the expert readers' annotations.. The high degree of reliability in performance showcases the safety and efficacy of See-Mode's AVA. Furthermore, risks identified during the risk management process have been mitigated as far as possible and all residuals risks have been deemed acceptable.
The predicate device was cleared based on non-clinical supportive information. Similar non-clinical and the analytical study highlighted, shows that AVA's acceptance criteria are adequate for the intended use of the device. The comparison of technological characteristics, indications for use, non-clinical performance data, software verification and validation, and risk analysis demonstrates that the subject device is safe and effective and is substantially equivalent to the predicate device that has the same intended use.
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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.
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.