(231 days)
Yes
The device description explicitly states that the HepaFat-AI Analysis Software performs image analysis based on "Artificial Intelligence (AI) technology" and is composed of "2 convolutional neural networks."
No
The device is indicated to assess and monitor liver fat content, aiding in diagnosis and screening. It does not provide treatment or directly alleviate symptoms.
Yes
The device "is indicated to: Assess the volumetric liver fat fraction, proton density fat fraction and steatosis grade in individuals with confirmed or suspected fatty liver disease." This assessment provides information that helps in the diagnosis or monitoring of a disease state.
Yes
The device description explicitly states that the "HepaFat-AI Analysis Software" is considered the medical device from a regulatory perspective and is a software platform designed to analyze MRI datasets. While it requires input from an MRI machine (hardware), the device itself is the software performing the analysis and generating the report.
Based on the provided information, HepaFat-AI is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostics are devices intended for use in the collection, preparation, and examination of specimens taken from the human body (such as blood, urine, tissue) to provide information for diagnostic purposes.
- HepaFat-AI's Function: HepaFat-AI analyzes magnetic resonance imaging (MRI) datasets, which are images acquired from the patient's body using a non-invasive imaging modality. It does not process biological specimens.
- Input Data: The input to HepaFat-AI is raw DICOM images from an MRI scan, not biological samples.
Therefore, HepaFat-AI falls under the category of medical image analysis software, which is a type of medical device, but not an IVD.
No
The letter does not mention that the FDA has reviewed, approved, or cleared a PCCP for this device.
Intended Use / Indications for Use
HepaFat-AI is indicated to:
· Assess the volumetric liver fat fraction, proton density fat fraction and steatosis grade in individuals with confirmed or suspected fatty liver disease;
When interpreted by a trained physician, the results can be used to
- · monitor liver fat content in patients undergoing weight loss management and can be used to
· aid in the assessment and screening of living donors for liver transplant.
Product codes
LNH
Device Description
The HepaFat-AI Analysis System is a software platform designed to automatically analyse magnetic resonance imaging (MRI) datasets to generate an estimate of the patient's volumetric liver fat fraction (VLFF). To carry out an analysis, the user simply uploads raw DICOM images to the HepaFat-AI Analysis System. No user input is required for the analysis thus minimising the impact of human error on obtained results. The HepaFat-AI system requires image input data that have been acquired according to the HepaFat-Scan protocol.
The key components for the HepaFat-AI Analysis System for volumetric liver fat fraction measurement are:
- Magnetic Resonance Imaging Protocol: The use of a specific magnetic resonance imaging protocol for acquisition of the raw image data. The imaging protocol is critical to ensure the quality of the end results. Its adherence is verified by the IOC Module, an automated algorithm that checks the correctness of each parameter in the protocol.
- HepaFat-AI Analysis Software: Custom-designed image analysis software performing the ● Alpha measurement and anomaly detection based on Artificial Intelligence (AI) technology. It is composed of 2 convolutional neural networks. The primary network is for the prediction of Alpha and a secondary network is for anomaly detection. This element is considered the medical device for a regulatory perspective. Following the training of the AI tool, the system is 'locked-down' for final validation prior to release in commercial use to ensure reproducibility of the results.
- Volumetric Liver Fat Fraction Measurement: An additional software module (algorithmic) ● that incorporates a conversion lookup table relating Alpha to volumetric liver fat fraction (VLFF) is added to allow production of a volumetric liver fat fraction report.
- Proton Density Fat Fraction Measurement: An additional software module (algorithmic) that incorporates a conversion lookup table relating VLFF to proton density fat fraction (PDFF) is added to allow production of a proton density fat fraction report.
- . Steatosis Grade Measurement: An additional software module (algorithmic) that incorporates a conversion lookup table relating VLFF to the steatosis grade.
The output of the HepaFat-AI Analysis System is an automated report. This report is populated with the information stored in the DICOM header of the MRI images, and the analysis result Alpha converted into a VLFF value and a steatosis grade, associated confidence interval and normal range. The report also contains pictures of the 3 TEs of the analysed slice. This is essential for the radiologist to check if the image analysed is a liver image, and the result provided is consistent with other relevant clinical results.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Magnetic Resonance
Anatomical Site
Liver
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Trained physician
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Specifically, HepaFat-Scan data were acquired in two independent studies of 145 adult and paediatric NAFLD/NASH patients who also received a liver biopsy with histological scoring of steatosis. The diagnostic performance of HepaFat-Scan and HepaFat-AI at three previously determined VLFF thresholds (equivalent to three steatosis grade boundaries) were then determined and are shown in the table below.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Software Verification and Validation:
Resonance Health has followed the principles developed by the IMDRF published by the FDA: Software as a Medical Device (SaMD): Clinical Evaluation. Final Guidance for Industry and FDA Staff. December 2017, to design the performance testing that support HepaFat-AI
HepaFat-AI Clinical Evaluation can be broken down as follows:
- Valid Clinical Association: using HepaFat-Scan as the reference standard.
- Analytical Validation: HepaFat-AI software has been developed, verified and validated following the Design Control principles and in accordance with the General Principles of Software Validation; Final Guidance for Industry and FDA Staff. U.S. Department Of Health and Human Services Food and Drug Administration. January 2002.
- Clinical Validation: the different studies conducted gather data to support the relevance of HepaFat-AI use as intended. This includes sensitivity, specificity, the negative percent agreement (NPA) and positive percent agreement (PPA) of HepaFat-AI, repeatability, and user testing results. Literature review and demographics of the validation datasets support the patient population as stated in the Indications for Use. Specifically, HepaFat-Scan data were acquired in two independent studies of 145 adult and paediatric NAFLD/NASH patients who also received a liver biopsy with histological scoring of steatosis. The diagnostic performance of HepaFat-Scan and HepaFat-AI at three previously determined VLFF thresholds (equivalent to three steatosis grade boundaries) were then determined and are shown in the table below. HepaFat-AI has a very similar diagnostic ability compared to HepaFat-Scan across all thresholds when both methods are tested directly against biopsy grades of steatosis. In particular, at the clinically important boundary separating no steatosis from any steatosis (Grade 0 vs Grades 1-3), the sensitivities and specificities of HepaFat-AI are no worse than HepaFat-Scan.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
| Steatosis Boundary | Grade 0 vs Grades 1-3 | Grades 0 & 1 vs Grades 2 & 3 | Grades 0-2 vs Grade 3 |
|---|---|---|---|
| VLFF Threshold (%) | 4.1 | 12.1 | 16.2 |
| HepaFat-Scan | |||
| Sensitivity (%) (95% CI) | 96.1 (91.2% to 98.3%) | 88.6 (79.7% to 93.9%) | 94.4 (74.2% to 99.0%) |
| Specificity (%) (95% CI) | 88.2 (65.7% to 96.7%) | 78.8 (67.5% to 86.9%) | 74.8 (66.6% to 81.5%) |
| HepaFat-AI | |||
| Sensitivity (%) (95% CI) | 97.6 (93.3% to 99.2%) | 86.1 (76.8% to 92.0%) | 100.0 (81.6% to 100.0%) |
| Specificity (%) (95% CI) | 88.2 (65.7% to 96.7%) | 74.8 (66.6% to 81.5%) | 71.4 (62.4% to 78.1%) |
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device 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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Image /page/0/Picture/0 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.
Resonance Health Analysis Services Pty Ltd. % Ms. Alison Laws CEO 141 Burswood Road Burswood. Western Australia 6100 AUSTRALIA
December 7, 2020
Re: K201039
Trade/Device Name: HepaFat-AI Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic resonance diagnostic device Regulatory Class: Class II Product Code: LNH Dated: October 15, 2020 Received: November 2, 2020
Dear Ms. Laws:
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
1
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 (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 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.
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
2
Indications for Use
510(k) Number (if known) K201039
Device Name HepaFat-AI
Indications for Use (Describe)
HepaFat-AI is indicated to:
· Assess the volumetric liver fat fraction, proton density fat fraction and steatosis grade in individuals with confirmed or suspected fatty liver disease;
When interpreted by a trained physician, the results can be used to
- · monitor liver fat content in patients undergoing weight loss management and can be used to
· aid in the assessment and screening of living donors for liver transplant.
| 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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3
K201039
510(K) SUMMARY
This Summary has been prepared in accordance with 21 CFR 807.92.
GENERAL INFORMATION
| Date Prepared | 4th December 2020 |
|---|---|
| Submitted by | Resonance Health Analysis Service Pty Ltd |
| 141 Burswood Rd | |
| Burswood 6100 | |
| AUSTRALIA | |
| Main Contact | Ms Alison Laws |
| CEO | |
| alisonl@resonancehealth.com | |
| Tel: +61 8 9286 5300 | |
| Fax: +61 8 9286 5399 | |
| US Contact (US Agent) | Michael van der Woude |
| Director & GM | |
| Emergo Global Representation LLC | |
| 2500 Bee Cave Road, Building 1, Suite 300 | |
| Austin, TX 78746 | |
| Phone: 512 3279997 | |
| Fax: 512 3279998 | |
| Email: USAgent@ul.com |
Device Information
| Name of Device | HepaFat-AI |
|---|---|
| Trade/proprietary Name | HepaFat-AI |
| Classification | Class II |
| Product Code | 90-LNH |
| CFR Section | 892.1000 Magnetic Resonance Diagnostic Device |
| Panel | Radiology |
4
Description of the Device
The HepaFat-AI Analysis System is a software platform designed to automatically analyse magnetic resonance imaging (MRI) datasets to generate an estimate of the patient's volumetric liver fat fraction (VLFF). To carry out an analysis, the user simply uploads raw DICOM images to the HepaFat-AI Analysis System. No user input is required for the analysis thus minimising the impact of human error on obtained results. The HepaFat-AI system requires image input data that have been acquired according to the HepaFat-Scan protocol.
The key components for the HepaFat-AI Analysis System for volumetric liver fat fraction measurement are:
- Magnetic Resonance Imaging Protocol: The use of a specific magnetic resonance imaging protocol for acquisition of the raw image data. The imaging protocol is critical to ensure the quality of the end results. Its adherence is verified by the IOC Module, an automated algorithm that checks the correctness of each parameter in the protocol.
- HepaFat-AI Analysis Software: Custom-designed image analysis software performing the ● Alpha measurement and anomaly detection based on Artificial Intelligence (AI) technology. It is composed of 2 convolutional neural networks. The primary network is for the prediction of Alpha and a secondary network is for anomaly detection. This element is considered the medical device for a regulatory perspective. Following the training of the AI tool, the system is 'locked-down' for final validation prior to release in commercial use to ensure reproducibility of the results.
- Volumetric Liver Fat Fraction Measurement: An additional software module (algorithmic) ● that incorporates a conversion lookup table relating Alpha to volumetric liver fat fraction (VLFF) is added to allow production of a volumetric liver fat fraction report.
- Proton Density Fat Fraction Measurement: An additional software module (algorithmic) that incorporates a conversion lookup table relating VLFF to proton density fat fraction (PDFF) is added to allow production of a proton density fat fraction report.
- . Steatosis Grade Measurement: An additional software module (algorithmic) that incorporates a conversion lookup table relating VLFF to the steatosis grade.
The output of the HepaFat-AI Analysis System is an automated report. This report is populated with the information stored in the DICOM header of the MRI images, and the analysis result Alpha converted into a VLFF value and a steatosis grade, associated confidence interval and normal range. The report also contains pictures of the 3 TEs of the analysed slice. This is essential for the radiologist to check if the image analysed is a liver image, and the result provided is consistent with other relevant clinical results.
Intended Use
The intended use of HepaFat-AI is:
HepaFat-AI is intended for quantitative measurement of the triglyceride fat fraction in magnetic resonance images of the liver, also known as volumetric liver fat fraction (VLFF).
Indications for Use
HepaFat-AI is indicated to:
5
-
Assess the volumetric liver fat fraction, proton density fat fraction and steatosis grade in ● individuals with confirmed or suspected fatty liver disease;
When interpreted by a trained physician, the results can be used to -
Monitor liver fat content in patients undergoing weight loss management and can be used to ●
-
. Aid in the assessment and screening of living donors for liver transplant
Predicate Information
HepaFat-AI is substantially equivalent to the predicate device HepaFat-Scan (Resonance Health Analysis Services) — K122035.
SUBSTANTIAL EQUIVALENCE INFORMATION
The table below summarizes the main similarities and differences between HepaFat-AI and the predicate.
| HepaFat-AI | HepaFat-Scan | |
|---|---|---|
| Regulatory Class | II | II |
| 510(k) number | K201039 | K122035 |
| Classification Name | System, Nuclear Magnetic | |
| Resonance Imaging, System, Image | ||
| Processing Radiological | System, Nuclear Magnetic Resonance | |
| Imaging, System, Image Processing | ||
| Radiological | ||
| CFR Section | 892.1000 | 892.1000 |
| Product Code and | ||
| Classification Panel | 90 LNH | 90 LNH |
| Device Name | HepaFat-AI | HepaFat-Scan |
| Trade/Common Name | HepaFat-AI | HepaFat-Scan |
| Description | Standalone software platform | |
| designed to automatically analyse | ||
| within seconds magnetic resonance | ||
| imaging (MRI) datasets to generate | ||
| an estimate of the patient's | ||
| volumetric liver fat fraction (VLFF), | ||
| converted into proton density fat | ||
| fraction (PDFF) and steatosis grade. | ||
| No user input is required for the | ||
| analysis thus minimising the impact | ||
| of human error on obtained results. | Standalone software application to | |
| facilitate the import and visualization | ||
| of multi-slice, gradient-echo MRI | ||
| data sets encompassing the abdomen, | ||
| with functionality independent of the | ||
| MRI equipment, to provide objective | ||
| and reproducible determination of the | ||
| triglyceride fat fraction in magnetic | ||
| resonance images of the liver. It | ||
| utilises magnetic resonance images | ||
| that exploit the difference in | ||
| resonance frequencies between | ||
| hydrogen nuclei in water and | ||
| triglyceride fat. The quantitative | ||
| triglyceride fat fraction is based on | ||
| the measurement of a magnetic | ||
| resonance parameter that reflects the | ||
| ratio of the proton density signal of | ||
| triglyceride fat to the total proton | ||
| density signal in the liver. | ||
| HepaFat-AI | HepaFat-Scan | |
| Technology | Convolutional neural networks for | |
| the image analysis. | ||
| Algorithmic for the images quality | ||
| checking and Alpha conversion into | ||
| VLFF. | Algorithmic, with human interaction | |
| for Region of Interest (ROI) | ||
| selection. | ||
| Intended purpose(s) | 1. Supporting clinical diagnoses | |
| about the status of liver fat content. |
- Supporting the subsequent
clinical decision-making processes. - Supporting the use in clinical
research trials, directed at studying
changes in liver fat as a result of
interventions. | 1. Supporting clinical diagnoses
about the status of liver fat content. - Supporting the subsequent
clinical decision-making processes. - Supporting the use in clinical
research trials, directed at studying
changes in liver fat as a result of
interventions. - It contains an image viewer for
importing DICOM images, browsing
through patient datasets, viewing
images and performing region of
interest analysis. |
| Intended Use | HepaFat-AI is intended for
quantitative measurement of the
triglyceride fat fraction in magnetic
resonance images of the liver, also
known as volumetric liver fat
fraction (VLFF).
It utilises magnetic resonance images
that exploit the difference in
resonance frequencies between
hydrogen nuclei in water and
triglyceride fat. The quantitative
triglyceride fat fraction is based on
the measurement of a magnetic
resonance parameter that reflects the
ratio of the proton density signal of
triglyceride fat to the total proton
density signal in the liver.
When interpreted by a trained
physician, the results provide
information that can aid in
diagnosis. | HepaFat-Scan is a software device
intended for quantitative
measurement of the triglyceride fat
fraction in magnetic resonance
images of the liver. It utilises
magnetic resonance images that
exploit the difference in resonance
frequencies between hydrogen nuclei
in water and triglyceride fat. The
quantitative triglyceride fat fraction is
based on the measurement of a
magnetic resonance parameter that
reflects the ratio of the proton density
signal of triglyceride fat to the total
proton density signal in the liver.
When interpreted by a trained
physician, the results provide
information that can aid in diagnosis. |
| Indications | HepaFat-AI is indicated to:
Assess the volumetric liver fat
fraction, proton density fat
fraction and steatosis grade
in individuals with
confirmed or suspected fatty
liver disease; | HepaFat-Scan is a software device
intended for quantitative
measurement of the triglyceride fat
fraction in magnetic resonance
images of the liver. It utilises
magnetic resonance images that
exploit the difference in resonance
frequencies between hydrogen nuclei |
| | HepaFat-AI | HepaFat-Scan |
| | When interpreted by a trained physician, the results can be used to Monitor liver fat content in patients undergoing weight loss management and can be used to Aid in the assessment and screening of living donors for liver transplant | in water and triglyceride fat. The quantitative triglyceride fat fraction is based on the measurement of a magnetic resonance parameter that reflects the ratio of the proton density signal of triglyceride fat to the total proton density signal in the liver.
When interpreted by a trained physician, the results provide information that can aid in diagnosis. |
| User | Radiologist | Resonance Health's trained analyst |
| Hosting platform | Cloud-based or onsite platform | Resonance Health's internal server |
| Image-type utilized | Magnetic Resonance | Magnetic Resonance |
| Image format | DICOM | DICOM |
| Data Acquisition method | Gradient Recalled Echo (GRE) | Gradient Recalled Echo (GRE) |
| Anatomical Sites | Liver | Liver |
| Result report content | Unique Report ID Patient ID, patient name and date of birth for full identification of the patient. Scan date, and analysis date. Referrer and MRI centre. Results displayed: VLFF (%), PDFF (%) and Steatosis grade, associated with confidence intervals and normal range. Pictures of the 3 TEs of the analysed slice. Liver colour map (for illustration purpose only, not for diagnostic) | Unique Report ID Patient ID, patient name and date of birth for full identification of the patient. Scan date, and analysis date. Referrer and MRI centre. Results displayed: VLFF (%) associated with confidence intervals and normal range. Picture of the analysed slice. |
| Result report format | HTML and PDF | PDF |
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7
Furthermore the substantial equivalence has been demonstrated clinically by processing the same anonymized clinical MRI datasets by both techniques, which:
- assessed the limit of agreement between the two analysis techniques; ●
- determined the performance of HepaFat-AI (NPA and PPA); and ●
- assessed the repeatability of HepaFat-AI. .
8
PERFORMANCE DATA
The following bench performance data were provided in order to support the substantial equivalence determination
Software Verification and Validation
Resonance Health has followed the principles developed by the IMDRF published by the FDA: Software as a Medical Device (SaMD): Clinical Evaluation. Final Guidance for Industry and FDA Staff. December 2017, to design the performance testing that support HepaFat-AI
HepaFat-AI Clinical Evaluation can be broken down as follows:
- Valid Clinical Association: using HepaFat-Scan as the reference standard. ●
- . Analytical Validation: HepaFat-AI software has been developed, verified and validated following the Design Control principles and in accordance with the General Principles of Software Validation; Final Guidance for Industry and FDA Staff. U.S. Department Of Health and Human Services Food and Drug Administration. January 2002.
- . Clinical Validation: the different studies conducted gather data to support the relevance of HepaFat-AI use as intended. This includes sensitivity, specificity, the negative percent agreement (NPA) and positive percent agreement (PPA) of HepaFat-AI, repeatability, and user testing results. Literature review and demographics of the validation datasets support the patient population as stated in the Indications for Use. Specifically, HepaFat-Scan data were acquired in two independent studies of 145 adult and paediatric NAFLD/NASH patients who also received a liver biopsy with histological scoring of steatosis. The diagnostic performance of HepaFat-Scan and HepaFat-AI at three previously determined VLFF thresholds (equivalent to three steatosis grade boundaries) were then determined and are shown in the table below. HepaFat-AI has a very similar diagnostic ability compared to HepaFat-Scan across all thresholds when both methods are tested directly against biopsy grades of steatosis. In particular, at the clinically important boundary separating no steatosis from any steatosis (Grade 0 vs Grades 1-3), the sensitivities and specificities of HepaFat-AI are no worse than HepaFat-Scan.
| Steatosis
Boundary | Grade 0 vs
Grades 1-3 | Grades 0 & 1 vs
Grades 2 & 3 | Grades 0-2 vs
Grade 3 |
|-----------------------------|--------------------------|---------------------------------|--------------------------|
| VLFF Threshold
(%) | 4.1 | 12.1 | 16.2 |
| HepaFat-Scan | | | |
| Sensitivity (%)
(95% CI) | 96.1 (91.2% to 98.3%) | 88.6 (79.7% to 93.9%) | 94.4 (74.2% to 99.0%) |
| Specificity (%)
(95% CI) | 88.2 (65.7% to 96.7%) | 78.8 (67.5% to 86.9%) | 74.8 (66.6% to 81.5%) |
| HepaFat-AI | | | |
| Sensitivity (%)
(95% CI) | 97.6 (93.3% to 99.2%) | 86.1 (76.8% to 92.0%) | 100.0 (81.6% to 100.0%) |
| Specificity (%)
(95% CI) | 88.2 (65.7% to 96.7%) | 74.8 (66.6% to 81.5%) | 71.4 (62.4% to 78.1%) |
Sensitivities and specificities of HepaFat-Scan and HepaFat-AI generated for the 145 cases with biopsy at the three previously determined VLFF thresholds.
9
Conclusion
The 510(k) premarket notification for HepaFat-AI contains adequate information and data to enable the FDA-CDRH to determine substantial equivalence to the predicate devices. Resonance Health Analysis Services Pty Ltd believes that enough evidences have been presented in this Dossier to conclude that HepaFat-AI is safe, effective and performs as well as the predicate.