(261 days)
Yes
The document explicitly mentions "Machine Learning Validation for Organ Guide Extension" and "Machine Learning Validation for Wave Quality Guide," and provides details about the training and test sets used for these components.
Yes
The device is described as aiding in the clinical management of patients with liver diseases by non-invasively determining liver tissue stiffness, attenuation, and Velacur Determined Fat Fraction. These measurements are used in conjunction with other clinical indicators, which falls under the definition of a therapeutic device designed to assist in diagnosis and treatment management.
Yes
The device is intended to non-invasively determine liver tissue stiffness, attenuation, and Velacur Determined Fat Fraction (VDFF), and these measurements are "meant to be used in conjunction with other clinical indicators in order to aid in clinical management of patients with liver diseases, including hepatic steatosis," which clearly indicates a diagnostic purpose.
No
The device description explicitly mentions hardware components like an "activation unit" that vibrates and an "ultrasound transducer" used to take scans. While it includes software algorithms, it is not solely software.
Based on the provided text, the Velacur device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Velacur's Function: Velacur is a non-invasive device that uses ultrasound and mechanical vibrations to measure physical properties of the liver tissue (stiffness, attenuation, and fat fraction) in situ (within the body). It does not analyze samples taken from the patient.
The device description clearly states it measures properties of the liver tissue within the organ of the patient and uses an ultrasound transducer placed on the patient's skin. This is a form of in vivo diagnostic imaging, not in vitro testing.
No
The provided text does not contain any explicit statements indicating that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The 'Control Plan Authorized (PCCP) and relevant text' section explicitly states "Not Found."
Intended Use / Indications for Use
Velacur is intended to provide estimates of tissue stiffness generated from shear wave speed measurements (40-70 Hz), ultrasound attenuation and Velacur Determined Fat Fraction (VDFF). The Velacur Determined Fat Fraction combines ultrasound attenuation and backscatter coefficient measurements. The device is indicated to non-invasively determine liver tissue stiffness, attenuation, and Velacur Determined Fat Fraction. VDFF is not intended to be used in pediatric patients. These are meant to be used in conjunction with other clinical indicators in order to aid in clinical management of patients with liver diseases, including hepatic steatosis.
The device is intended to be used in a clinical setting and by trained medical professionals.
Product codes
IYO, ITX
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 at frequencies 40, 50, and 60 Hz 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. The device includes two algorithms designed to help users detect good quality shear waves and identify liver tissue. From the scan data, the device calculates tissue stiffness and attenuation.
Minor hardware and software changes were made to the organ guide (cleared in K223287) was also extended to add more optional overlays on top of the liver overlay to help with optimizing the scan and training users to obtain adequate images. The significant change is the addition of a new output measure for Velacur, an ultrasound derived fat fraction (VDFF).
Mentions image processing
Not Found
Mentions AI, DNN, or ML
Machine Learning Validation for Organ Guide Extension
Machine Learning Validation for Wave Quality Guide
Input Imaging Modality
ultrasound
Anatomical Site
liver
Indicated Patient Age Range
VDFF is not intended to be used in pediatric patients.
Intended User / Care Setting
The device is intended to be used in a clinical setting and by trained medical professionals.
Description of the training set, sample size, data source, and annotation protocol
Validation of Velacur Determined Fat Fraction algorithm (VDFF)
112 patients from 4 sites were used in for parameter fitting (training). All MRI scans were assessed to create the final measurement.
| Parameter Fitting Cohort (Training) |
|---|
| Number of patients: 112 |
| Number of Sites: 4 |
| Sex (% Female): 53% Female |
| Age (mean ± std): 57.4 ± 11.7 |
| BMI (mean ± std): 30 ± 4.4 |
| Race/Ethnicity (% white): 36 % |
| MRI PDFF (mean ± std): 14.2 ± 8.23 |
Machine Learning Validation for Organ Guide Extension
More than 5,000 patient images were used for training. 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. Ground truth was established using manual image segmentation by experts in the field of sonography and/or ultrasound elastography.
Machine Learning Validation for Wave Quality Guide
More than 15,000 patient images from 100+ patients were used for 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. Ground truth was established using manual image segmentation by experts in the field of ultrasound elastography.
Description of the test set, sample size, data source, and annotation protocol
Validation of Velacur Determined Fat Fraction algorithm (VDFF)
70 new patients from 3 separate sites (with different Velacur operators) were used for the final validation. All MRI scans were assessed to create the final measurement.
| Validation Cohort |
|---|
| Number of patients: 70 |
| Number of Sites: 3 |
| Sex (% Female): 75.7% Female |
| Age (mean ± std): 46.9 ± 13.8 |
| BMI (mean ± std): 30.9 ± 7.58 |
| Race/Ethnicity (% white): 69 % |
| MRI PDFF (mean ± std): 10.1 ± 9.75 |
MRI proton density fat fraction was used as the ground truth for validation.
Machine Learning Validation for Organ Guide Extension
Evaluation was completed on more than 800 images from 21 patients.
Volunteers and patients were recruited from all genders, ages between 18-70 and all ethnicities, with from 33-50% minority representation.
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.
All data was collected using the Velacur system.
Data used in the validation of the algorithm performance was obtained from separate patients, sites and collected by different users than the data used for training in order to ensure data independence.
Machine Learning Validation for Wave Quality Guide
Evaluation was completed on more than 4,000 images from 36 patients.
Volunteers and patients were recruited from all genders, ages between 18-70 and all ethnicities, with from 33-50% minority representation.
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.
All data was collected using the Velacur system.
Data used in the validation of the algorithm performance was obtained from separate patients, sites and collected by different users than the data used for training in order to ensure data independence.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance Studies Performed
- Performance verification testing of the Velacur Determined Fat Fraction output.
- Backscatter testing on phantoms, using phantoms with known backscatter parameters.
- Sweep guidance tool testing.
- Human Factors testing.
- Validation testing of the organ guide extension.
- Software Verification and Validation.
Validation of Velacur Determined Fat Fraction algorithm (VDFF)
Study Type: Validation study comparing VDFF to MRI-PDFF.
Sample Size: Validation Cohort: 70 patients.
Key Results: The final correlation coefficient [95% C1] in the validation cohort was 0.85 [0.77-0.91]. The AUC [95% Cl] for detection of 5% steatosis, which is the consensus level for the diagnosis of any steatosis, was 0.97 [0.89-0.99].
Machine Learning Validation for Organ Guide Extension
Study Type: Validation of Machine Learning algorithm for Organ Guide Extension.
Sample Size: Evaluation on more than 800 images from 21 patients.
Key Results:
Dice Coefficient > 0.7
Pixel accuracy > 80%
Machine Learning Validation for Wave Quality Guide
Study Type: Validation of Machine Learning algorithm for Wave Quality Guide.
Sample Size: Evaluation on more than 4,000 images from 36 patients.
Key Results:
Dice Coefficient > 0.7
Sensitivity and Specificity > 80%
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Validation of Velacur Determined Fat Fraction algorithm (VDFF)
Correlation coefficient (r) between VDFF and MRI-PDFF: 0.85 [0.77-0.91]
AUC [95% Cl] for detection of 5% steatosis: 0.97 [0.89-0.99]
Machine Learning Validation for Organ Guide Extension
Dice Coefficient > 0.7
Pixel accuracy > 80%
Predicate Device(s)
Velacur (K232459)
Reference Device(s)
Siemens Acuson Sequoia (K183575)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.1560 Ultrasonic pulsed echo imaging system.
(a)
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.(b)
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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September 4, 2024
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Sonic Incytes % Rhona Shanker President Z & B Enterprises, Inc. 12154 Darnestown Road, #236 GAITHERSBURG MD 20878
Re: K233977
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: August 1, 2024 Received: August 2, 2024
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 (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpm/pm.cfm identifies combination product submissions. The general controls of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100. Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
1
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatoryinformation/postmarketing-safety-reporting-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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advice-comprehensive-regulatory-assistance/unique-deviceidentification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reporting-mdr-how-report-medicaldevice-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/deviceadvice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuingeducation/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-devices/deviceadvice-comprehensive-regulatory-assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Marjan Nabili -S
Yanna Kang, Ph.D. Assistant Director Mammography and Ultrasound Team 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
2
Indications for Use
510(k) Number (if known) K233977
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), ultrasound attenuation and Velacur Determined Fat Fraction (VDFF). The Velacur Determined Fat Fraction combines ultrasound attenuation and backscatter coefficient measurements. The device is indicated to non-invasively determine liver tissue stiffness, attenuation, and Velacur Determined Fat Fraction. VDFF is not intended to be used in pediatric patients. These are meant to be used in conjunction with other clinical indicators in order to aid in clinical management of patients with liver diseases, including hepatic steatosis.
The device is intended to be used in a clinical setting and by trained medical professionals.
| 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/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 is a rounded square with a series of vertical lines of varying heights, resembling sound waves. The text is in a sans-serif font, with the first letter of each word capitalized.
Section 5 - 510(k) Summary
Sonic Incytes Velacur system
-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: 15 December 2023
- II. Device
Name of Device: Velacur
Model: LI-1005
Common Name: Ultrasound elastography system
| Classification Name | Requlation | Product Code |
|---|---|---|
| Ultrasonic Pulsed Echo | ||
| Imaging System | 21 CFR §892.1560 | IYO |
| Diagnostic Ultrasonic | ||
| Transducer | 21 CFR §892.1570 | ITX |
Predicate Device
Velacur (K232459) manufactured by Sonic Incytes Medical Corp., Vancouver, Canada, and cleared on September 12, 2023.
Reference device: Siemens Acuson Sequoia (K183575) cleared on March 20, 2019.
Device Description
The device that is the subject of this submission is the same one cleared under K232459, Velacur.
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 orqan 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 at frequencies 40, 50, and 60 Hz 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
4
Velacur Model: LI-1005
Image /page/4/Picture/1 description: The image shows the logo for SonicIncytes. The logo consists of a blue icon on the left, which resembles a sound wave inside a rounded square. To the right of the icon is the company name, "SonicIncytes", also in blue. The font is sans-serif and modern.
volumetric scans of the liver while shear waves are occurring. The device includes two algorithms designed to help users detect good quality shear waves and identify liver tissue. From the scan data, the device calculates tissue stiffness and attenuation.
Minor hardware and software changes were made to the organ guide (cleared in K223287) was also extended to add more optional overlays on top of the liver overlay to help with optimizing the scan and training users to obtain adequate images. The significant change is the addition of a new output measure for Velacur, an ultrasound derived fat fraction (VDFF).
Intended Use/ Indication for Use
Velacur is intended to provide estimates of tissue stiffness generated from shear wave speed measurements (40-70 Hz), ultrasound attenuation and Velacur Determined Fat Fraction (VDFF). The Velacur Determined Fat Fraction combines ultrasound attenuation and backscatter coefficient measurements. The device is indicated to non-invasively determine liver tissue stiffness, attenuation, and Velacur Determined Fat Fraction. VDFF is not intended to be used in pediatric patients. These are meant to be used in conjunction with other clinical indicators in order to aid in clinical management of patients with liver diseases, including hepatic steatosis.
The device is intended to be used in a clinical setting and by trained medical professionals.
Substantial Equivalence
The candidate device has the same intended use and indications for use as the predicate device in that the outputs are meant to be used in conjunction with other clinical indicators in order to aid in clinical management of patients with liver diseases.
The technology used in the candidate and predicate device is based on ultrasound to measure elastography and attenuation (ACE). The systems measure the same physical variables, tissue stiffness and ultrasound attenuation, and therefore the devices are substantially equivalent in their basic technology.
Sonic Incytes has introduced a third Velacur output, the Velacur Determined Fat Fraction (VDFF), in addition to the elasticity and ACE output. The Siemens Sequoia Ultrasonically Derived Fat Fraction (UDFF) was used as the reference device for this output measure. The same as UDFF, the VDFF algorithm combines ultrasound attenuation measurements and a computed backscatter coefficient (BSC). The same as UDFF, MRI proton density fat fraction was used as the ground truth for validation. The performance testing acceptance criteria were based on UDFF testing and results.
The candidate device with the described changes does not raise any new issues of safety or effectiveness.
Performance Data
The following testing was performed:
- Performance verification testing of the Velacur Determined Fat Fraction output .
- Backscatter testing on phantoms, using phantoms with known backscatter parameters .
- Sweep quidance tool testing ●
5
Velacur Model: LI-1005
Image /page/5/Picture/1 description: The image contains the logo for SonicIncytes. The logo consists of a blue square with white vertical lines of varying heights, resembling sound waves, on the left. To the right of the square is the company name, "SonicIncytes," written in a blue, sans-serif font. The overall design is clean and modern.
- Human Factors testing .
- Validation testing of the organ guide extension ●
- Software Verification and Validation .
Recognized Consensus Standards Used
The system complies with the same standards as the predicate, the standards are:
IEC 60601-1-2 Edition 4.1: Medical Electrical Equipment - Part 1-2: General Requirements for Basic Safety and Essential Performance - Collateral Standard: Electromagnetic Disturbances -Requirements and Tests
ANSI AAMI 60601-1:2005/(R)2012 And A1:2012: Medical Electrical Equipment - Part 1: General Requirements for Basic Safety and Essential Performance (IEC 60601-1:2005, MOD)
IEC 60601-1-6 Edition 3.1 2013-10: Medical Electrical Equipment - Part 1-6: General Requirements for Basic Safety and Essential Performance - Collateral Standard: Usability
IEC 62304:2006/A1:2015: Medical Device Software - Software Life Cycle Processes [Including Amendment 1 (2016)
IEC 60601-2-37 Edition 2.1 2015 Medical Electrical Equipment - Part 2-37: Particular Requirements for The Basic Safety and Essential Performance Of Ultrasonic Medical Diagnostic And Monitoring Equipment
IEC 62359: Edition 2.1 2017-09: Ultrasonics - Field Characterization - Test Methods for The Determination of Thermal and Mechanical Indices Related to Medical Diagnostic Ultrasonic Fields
ISO 14971 Third Edition 2019-12: Medical Devices - Application of Risk Management to Medical Devices
ISO 10993-1 fifth edition 2018-08: Biological Evaluation of Medical Devices - Part 1: Evaluation and Testing Within A Risk Management Process
Validation of Velacur Determined Fat Fraction algorithm (VDFF)
Velacur Determined Fat Fraction was tested in a cohort of patients with both Velacur and MRI-PDFF scans. 112 patients from 4 sites were used in for parameter fitting (training) and 70 new patients from 3 separate sites (with different Velacur operators) were used for the final validation. All MRI scans were assessed to create the final measurement.
Acceptance criteria were based on the correlation coefficient (r) between VDFF and MRI-PDFF, and the detection of 5% steatosis. Demographic distribution of the patients is summarized below:
| | Parameter Fitting
Cohort (Training) | Validation Cohort |
|--------------------|----------------------------------------|-------------------|
| Number of patients | 112 | 70 |
| Number of Sites | 4 | 3 |
6
| Sex (% Female) | 53% Female | 75.7% Female |
|---|---|---|
| Age (mean ± std) | 57.4 ± 11.7 | 46.9 ± 13.8 |
| BMI (mean ± std) | 30 ± 4.4 | 30.9 ± 7.58 |
| Race/Ethnicity (% white) | 36 % | 69 % |
| MRI PDFF (mean ± std) | 14.2 ± 8.23 | 10.1 ± 9.75 |
The final correlation coefficient [95% C1] in the validation cohort was 0.85 [0.77-0.91]. The AUC [95% Cl] for detection of 5% steatosis, which is the consensus level for the diagnosis of any steatosis, was 0.97 [0.89-0.99].
Machine Learning Validation for Organ Guide Extension
- . The Organ Guide was extended to include surrounding organs and features.
- Dice Coefficient and pixel accuracy were used for characterization and validation of the . performance of the organ guide extension. The acceptance criteria were:
- o Dice Coefficient > 0.7
- Pixel accuracy > 80% O
- . More than 5,000 patient images were used for training. 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 800 images from 21 patients. ●
- Volunteers and patients were recruited from all genders, ages between 18-70 and all ethnicities, with from 33-50% minority representation.
- . 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.
- . All data was collected using the Velacur system.
- Ground truth was established using manual image segmentation by experts in the field . of sonography and/or ultrasound elastography.
- . Data used in the validation of the algorithm performance was obtained from separate patients, sites and collected by different users than the data used for training in order to ensure data independence.
Machine Learning Validation for Wave Quality Guide
- . The Wave Quality Guide is an algorithm to measure the area of good quality waves within the image or volume. This is shown to the user through an optional semi transparent overlay, and the quality bar in the interface.
- Dice Coefficient and sensitivity/specificity were used for characterization and validation ● of the performance of the wave quality guide. The acceptance criteria were:
- Dice Coefficient > 0.7 O
- Sensitivity and Specificity > 80% O
7
Velacur Model: LI-1005
Incytes
- More than 15,000 patient images from 100+ patients were used for 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 4,000 images from 36 patients. ●
- Volunteers and patients were recruited from all genders, ages between 18-70 and all ● ethnicities, with from 33-50% minority representation.
- 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.
- All data was collected using the Velacur system. ●
- Ground truth was established using manual image segmentation by experts in the field of ultrasound elastography.
- . Data used in the validation of the algorithm performance was obtained from separate patients, sites and collected by different users than the data used for training in order to ensure data independence.
Conclusion
The conclusion drawn from the testing described above demonstrate that the device is substantially equivalent to the predicate device with respect to safety, effectiveness and performance.