Average Dice Score > 0.7, overall pixel based accuracy > 80%
More than 5,000 patient images collected during clinical trials of volunteers and patients with chronic liver disease
—
Evaluation on more than 1,500 images from 35-40 patients
>1 (sonographers)
Shear wave quality assessment
—
Dice Score >= 0.7, sensitivity/specificity >= 80%
Dice Scores >= 0.7, sensitivity and specificity >= 80%
More than 5,000 patient images collected during clinical trials of volunteers and patients with chronic liver disease
—
Evaluation on more than 1,500 images from 35-40 patients
>1 (experts)
Indications for Use
Velacur is intended to provide estimates of tissue stiffness generated from shear wave speed measurements (40-70 Hz) and coefficient of attenuation. The device is indicated to non-invasively determine liver tissue stiffness and attenuation. These are meant to be used in conjunction with other clinical indicators in order to assist in clinical management of patients with liver disease.
Device Story
Portable ultrasound elastography system for point-of-care use in clinics/hospitals by medical professionals. Input: volumetric ultrasound scans of liver while mechanical activation unit (placed under supine patient) generates shear waves (40-70 Hz). Device calculates tissue stiffness (shear modulus) and ultrasound attenuation. Includes deep learning algorithms for organ/liver segmentation and shear wave quality assessment to assist user in data collection. Output: stiffness and attenuation estimates displayed to clinician. Clinical decision-making: assists in management of patients with liver disease by providing non-invasive tissue characterization.
Clinical Evidence
Bench testing included performance verification, phantom attenuation testing, inter-operator variability, human factors, and software feature validation. AI/ML algorithms validated on >1,500 images from 35-40 patients (independent from training set of >5,000 images). Organ Segmentation Guide: Dice >0.7, pixel accuracy >80%. Wave Quality Guide: Dice >0.7, sensitivity/specificity >80%. Ground truth established by expert sonographers/scientists via consensus/voting. Comparison to MRE and MR-PDFF performed.
Technological Characteristics
Portable ultrasound pulsed echo imaging system. Components: activation unit (vibration 40-70 Hz) and ultrasound transducer. Connectivity: standalone. Software: deep learning-based organ segmentation and shear wave quality algorithms. Standards: IEC 60601-1-2 Ed 4.0 (EMC).
Indications for Use
Indicated for non-invasive determination of liver tissue stiffness and attenuation in patients with liver disease (ages 18-70, all genders). Used as an adjunct to other clinical indicators for patient management.
Regulatory Classification
Identification
An ultrasonic pulsed echo imaging system is a device intended to project a pulsed sound beam into body tissue to determine the depth or location of the tissue interfaces and to measure the duration of an acoustic pulse from the transmitter to the tissue interface and back to the receiver. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.
Special Controls
*Classification.* Class II (special controls). A biopsy needle guide kit intended for use with an ultrasonic pulsed echo imaging system only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.
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April 20, 2023
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Sonic Incytes % Rhona Shanker President Z & B Enterprises, Inc. 12154 Darnestown Road. #236 GAITHERSBURG MD 20878
Re: K223287
Trade/Device Name: Velacur Regulation Number: 21 CFR 892.1560 Regulation Name: Ultrasonic Pulsed Echo Imaging System Regulatory Class: Class II Product Code: IYO, ITX Dated: March 17, 2023 Received: March 17, 2023
Dear Rhona Shanker:
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
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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 postmarketing safety reporting (21 CFR 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 (OS) 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 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,
Yanna S. Kang -S
Yanna Kang, Ph.D. Assistant Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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# Indications for Use
510(k) Number (if known) K223287
Device Name Velacur
Indications for Use (Describe)
Velacur is intended to provide estimates of tissue stiffness generated from shear wave speed measurements (40-70 Hz) and coefficient of attenuation. The device is indicated to non-invasively determine liver tissue stiffness and attenuation. These are meant to be used in conjunction with other clinical indicators in order to assist in clinical management of patients with liver disease.
| Type of Use (Select one or both, as applicable) |
|-------------------------------------------------|
|-------------------------------------------------|
| <div style="display:inline-block;"> <span style="font-size: 16px;">☑</span> Prescription Use (Part 21 CFR 801 Subpart D)</div> | <div style="display:inline-block;"> <span style="font-size: 16px;">☐</span> Over-The-Counter Use (21 CFR 801 Subpart C)</div> |
|--------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|
|--------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|
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Velacur Model: LI-1005
Image /page/3/Picture/2 description: The image contains the logo for SonicIncytes. The logo consists of a blue rounded square with a sound wave graphic inside, followed by the company name "SonicIncytes" in a blue sans-serif font. The sound wave graphic is composed of vertical lines of varying heights, representing sound frequencies.
## Section 5 - 510(k) Summary
### Sonic Incytes Velacur system
### l. Submitter:
Sonic Incytes #309-1788 West 5th Avenue Vancouver, BC V6J 1P2 Canada Telephone: +1 604 875 4111 Extension: 54851
Contact person: Rhona Shanker Date Prepared: 19 April 2023
- II. Device
Name of Device: Velacur
Model: LI-1005
Common Name: Ultrasound elastography system
| Classification Name | Regulation | Product Code |
|------------------------------------------|------------------|--------------|
| Ultrasonic Pulsed Echo Imaging<br>System | 21 CFR §892.1560 | IYO |
| Diagnostic Ultrasonic<br>Transducer | 21 CFR §892.1570 | ITX |
### Predicate Device
Liver Incytes (K201597) manufactured by Sonic Incytes Medical Corp., Vancouver, Canada, and cleared on July 31, 2020.
### Device Description
Velacur is a portable device intended to non-invasively measure the stiffness and attenuation of the liver via measurement of liver tissue shear modulus and ultrasound attenuation. This is done by measuring the wavelength or wave speed of mechanically created shear waves within the organ of the patient. Attenuation is measured directly via the loss in power of the ultrasound beam.
The device is designed to be used at the point of care, in clinics and hospitals. The device is used by a medical profession, an employee of the clinic/hospital. The activation unit is placed under the patient, while lying supine on an exam bed. The activation unit vibrates causing shear waves within the liver of the patient. The ultrasound transducer is placed on the patient's skin, over the intercostal space, and is used to take volumetric scans of the liver while shear waves are occurring. From the scan data, the device calculates tissue stiffness and attenuation.
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Velacur Model: LI-1005
Minor hardware and software changes were made to the device. One such change was the change in the vibration frequencies produced by the activation unit to create shear waves in the liver. The shear waves are now produced at 40, 50, and 60 Hz, as compared to the predicate device (45, 50, 55, 60Hz). The significant change was the addition two deep learning based algorithms. The first being an organ/liver segmentation algorithm. The second algorithm changes the predicate's rules-based scan guality to a deep learning based shear wave quality algorithms are designed to help users better detect good quality shear waves and identify liver tissue, to facilitate the data collection for elasticity and attenuation calculations performed by the device. The organ/liver segmentation algorithm can only segment the liver is present in the B-Mode ultrasound image and should not be used as a liver detection algorithm. The shear wave quality algorithm was tested to both detect and segment the shear waves. These elasticity and attenuation calculations were cleared in the predicate device.
### Intended Use/ Indication for Use
Velacur is intended to provide estimates of tissue stiffness generated from shear wave speed measurements (40-70Hz) and coefficient of attenuation. The device is indicated to non-invasively determine liver tissue stiffness and attenuation. These are meant to be used in conjunction with other clinical indicators in order to assist in clinical management of patients with liver disease.
### Substantial Equivalence
The candidate device has an equivalent intended use and indications for use as the predicate device. Further, it has the same operating principle.
The technology used in the candidate and predicate device is based on ultrasound to measure elastography and attenuation. The systems measure the same physical variables, tissue stiffness and ultrasound attenuation, and therefore the devices are substantially equivalent in their basic technology. The candidate device with the described changes does not raise any new issues of safety or effectiveness.
### Performance Data
The following non-clinical testing was performed:
- Electrical safety, electromagnetic interference
- The validation of the changes from the predicate were tested and documented based on the tests performed on phantoms with known elasticity and attenuation. Design changes were also tested against the predicate design on healthy volunteers.
- . The candidate device was tested in a cohort of patients and volunteers with several novice users.
Specifically, non-clinical bench testing included:
- . Performance verification testing
- Attenuation testing with phantoms
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Image /page/5/Picture/1 description: The image shows the logo for SonicIncytes. The logo consists of a blue icon on the left and the company name "SonicIncytes" in blue text on the right. The icon appears to be a stylized representation of sound waves or a signal processing graph.
- Inter-operator variability
- Human Factors testing
- Device Lifespan analysis
- Sweep guidance tool testing
- . Software feature testing: Quality Factor, Organ Segmentation Guide and Wave Quality Guide.
### Recognized Consensus Standards Used
Non-clinical testing to assure compliance with EMC was performed and the device was found to conform to the applicable medical device safety standard. The system complies with
- । Electromagnetic compatibility (EMC) testing.
- o IEC 60601-1-2 Edition 4.0 (only the potentially affected tests by the changes)
The conclusions drawn from the testing demonstrate that the device is substantially equivalent to the predicate device with respect to safety, efficacy, and performance.
### Artificial Intelligence/Machine Learning Validation for Organ Segmentation Guide and Wave Quality Guide
Summary test statistics or other test results including acceptance criteria or other information supporting the appropriateness of the characterized performance
- . Dice Coefficient, pixel accuracy, and sensitivity and specificity were used for validation and characterization of the performance of the algorithms. Both algorithms were validated on patient data from the US and Canada.
- o For the Organ Segmentation Guide, the average Dice Score was greater than 0.7, and overall pixel based accuracy was greater than 80%.
- For the Wave Quality Guide, the Dice Scores and sensitivity/specificity were used to o measure algorithm performance in segmentation and detection. The Dice Scores were 0.7 or higher and the sensitivity and specificity was 80% or greater.
- . More than 5,000 patient images were used for training each algorithm. Training data was collected during clinical trials of volunteers and patients with chronic liver disease of all severities. Data was collected from sites across the US and Canada.
- Evaluation was completed on more than 1,500 images, from 35-40 patients.
Demographic distribution including:
- Volunteers and patients were recruited from all genders, ages between 18-70.
- Volunteers and patients of all ethnicities were included, with 33-50% minority representation.
- Data was collected from sites in the US and Canada
Information about clinical subgroups and confounders present in the dataset:
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Velacur Model: LI-1005
- Evaluation data was collected from volunteers and patients with non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. All patients were recruited from hepatology clinics and represent a group with more severe liver disease than the general public.
Information about equipment and protocols used to collect images:
- All data was collected using the Velacur system, with comparison to Magnetic Resonance Elastography or Magnetic Resonance Proton Density Fat Fraction where appropriate.
Information about how the reference standard was derived from the dataset (i.e., the "truthing" process)
- . Ground truth was established using manual image segmentation by experts in the field of sonography and/or ultrasound elastography.
- . For the Organ Segmentation Guide, all validation images were segmented by at least three sonographers who all had more than 20 years experience in abdominal ultrasound imaging. Ground truth was established using a pixel based voting method.
- . For the Wave Quality Guide, all experts hold a masters or PhD in a relevant field. All experts have at least 10 years of experience with ultrasound imaging and elastography. Ground truth was established through expert consensus.
Description of how independence of test data from training data was ensured
- . Data used in the evaluation of the algorithm performance was taken from separate patients, sites and collected by different users than the data used for training in order ensure data independence.
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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.
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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.
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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.
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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.
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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.
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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
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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.