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Intended use:

HepaFatSmart is intended for the quantitative measurement of volumetric liver fat fraction (VLFF), proton density fat fraction (PDFF) and steatosis grading.

HepaFatSmart is an application that is used for the non-invasive evaluation of liver tissue by utilising magnetic resonance images to evaluate 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.

Indications for use:

Support clinical diagnoses in individuals with confirmed or suspected fatty liver disease;

Support the subsequent clinical decision making processes for patients under management for fatty liver related disease or metabolic syndromes;

Aid in the assessment and screening of living donors for liver transplant.

Results, when interpreted by a trained physician can be used to support clinical diagnoses about the status of liver fat content, the subsequent clinical decision making processes for the management of fatty liver related diseases, metabolic syndromes, liver donor screening and lifestyle change. HepaFatSmart can be used to analyse the MRI images of patients of all populations independent of age and gender, with suspected clinical conditions related to the level of liver fat.

HepaFatSmart is an SaMD designed to automatically analyse magnetic resonance imaging (MRI) datasets for quantitative assessment of a patient's liver fat, in form of volumetric liver fat fraction (VLFF), proton density fat fraction (PDFF), and steatosis grade. It is an Al assisted, automated version of HepaFat-Scan (another SaMD of Resonance Health). To carry out an analysis, the user simply uploads DICOM images to FAST, Resonance Health's secured user portal and job management system. No other user input is required for the analysis thereby minimising the impact of human error on obtained results. HepaFatSmart requires DICOM images as input data that have been acquired according to the HepaFatSmart (same as HepaFat-Scan) protocol.

The provided documentation describes the acceptance criteria and a study proving that HepaFatSmart (V2.0.0) meets these criteria, demonstrating its substantial equivalence to the predicate device HepaFat-Scan and improved performance over HepaFat-AI.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of predetermined acceptance criteria for the new device prior to testing. Instead, it demonstrates the device's performance by comparing it to a reference standard (HepaFat-Scan) and an existing predicate (HepaFat-AI). The "acceptance" is implied by demonstrating substantial equivalence to HepaFat-Scan and improvement over HepaFat-AI, primarily through quantitative metrics like bias, repeatability, and limits of agreement, as well as sensitivity and specificity for clinically relevant thresholds.

However, based on the provided data, we can infer the performance metrics used for comparison, which implicitly serve as the targets for acceptance.

Inferred Acceptance Criteria (Implicitly compared to HepaFat-Scan and better than HepaFat-AI):

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size:

  • Repeatability Study: n = 42 subjects initially; n = 41 subjects for HepaFatSmart analysis (one case identified as high iron and excluded by the new algorithm).
  • Validation Study: n = 300 initially; n = 281 datasets successfully passed the IQC rules and were used for analysis.

• Data Provenance: The document states the data used for the validation study comprised "fully quarantined 300 validation subjects with different clinical conditions across a broad age range and fat level scanned from different MRI centres with different MRI makes and models." While explicit countries of origin are not specified, the mention of "different MRI centres" and "different MRI makes and models" suggests a diverse, real-world dataset. The data appears to be retrospective, as it refers to a "quarantined dataset" used for validation.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

The ground truth for this study is established by the reference standard HepaFat-Scan. HepaFat-Scan is described as a "Standalone software application to facilitate the import and visualization of multi-slice, gradient-echo MRI data sets... to provide objective and reproducible determination of the triglyceride fat fraction in magnetic resonance images of the liver." It performs quantitative triglyceride fat fraction measurements.

The document states that HepaFat-Scan is "Algorithmic, with human interaction for Region of Interest (ROI) selection." The "User" for HepaFat-Scan is listed as "Resonance Health's trained analyst."

Therefore:

• Number of Experts/Analysts: The document doesn't specify a number, but rather a type of user: "Resonance Health's trained analyst." This implies trained personnel, but not necessarily multiple independent experts in a consensus-building scenario.
• Qualifications of Experts: "Resonance Health's trained analyst." No further specific qualifications (e.g., years of experience, radiologist vs. technician) are provided for the individuals performing the HepaFat-Scan ground truth ROI selection. The emphasis is on the software being the reference standard.

4. Adjudication Method for the Test Set

The primary ground truth is derived from the HepaFat-Scan software with human ROI selection. The document does not describe any specific adjudication method (e.g., 2+1, 3+1 consensus by multiple readers) for establishing the ground truth measurements. The HepaFat-Scan output, with its human-selected ROI, appears to be accepted as the reference.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, a traditional MRMC comparative effectiveness study was not explicitly done to show human readers improve with AI vs. without AI assistance.

This study is focused on the standalone performance of HepaFatSmart and its equivalence/superiority to existing automated or semi-automated software solutions (HepaFat-Scan and HepaFat-AI). HepaFatSmart is an "AI-assisted, automated version" of HepaFat-Scan. HepaFat-Scan itself involves "human interaction for Region of Interest (ROI) selection." The document implies that HepaFatSmart's full automation (no user input for analysis, AI-predicted ROI) minimizes human error and makes it potentially better or comparable to the human-assisted HepaFat-Scan.

The statement: "Bias and both repeatability coefficients for the HepaFatSmart are slightly better than those obtained from the repeated scans of HepaFat-Scan, indicating the performance of the HepaFatSmart is comparable (no worse) than human for the repeatability data analysed. This does not suggest yet that the HepaFatSmart is better than human analyst as the original human analysis (HepaFat-Scan) historically used two small liver ROIs rather than a single large liver ROI used in the HepaFatSmart with potentially slightly larger sampling error in the original HepaFat-Scan analysis."
And: "HepaFatSmart demonstrated 100% repeatable (reproducible) in the repeatability study using the same datasets analysed twice with zero VLFF difference between the first and second analyses, which is better than human analysis with HepaFat-Scan."
These statements imply a comparison to "human analysis" (as part of HepaFat-Scan's workflow), but it's not a formal MRMC study as typically understood where human readers' diagnostic performance is measured with and without AI assistance on the same cases.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was Done

Yes, a standalone performance study was foundational to this submission. HepaFatSmart is described as a "Standalone software platform" that "automatically analyse... MRI datasets... No other user input is required for the analysis thereby minimising the impact of human error on obtained results." The entire study evaluates the performance of this algorithm-only approach (HepaFatSmart) by comparing its output directly to the output of the human-assisted reference standard (HepaFat-Scan) and the previous AI version (HepaFat-AI).

7. The Type of Ground Truth Used

The ground truth used for the test set is expert-assisted software output (HepaFat-Scan).
Specifically, it's the "HepaFat-Scan" software's quantitative measurement of Volumetric Liver Fat Fraction (VLFF), which involves human interaction for Region of Interest (ROI) selection. This serves as the "reference standard" against which HepaFatSmart's automated results are compared.

8. The Sample Size for the Training Set

The document does not provide the sample size directly for the training set of the HepaFatSmart AI model. It notes that the system is "completely 'locked down' for final validation prior to release in commercial use to ensure reproducibility of the results" after training.

9. How the Ground Truth for the Training Set was Established

The document briefly mentions that HepaFatSmart uses "one (1) convolutional neural network (CNN) performing liver ROI detection... Following the training of the Al assisted device, the system is completely 'locked down' for final validation..."
Given that HepaFatSmart is an "AI-assisted, automated version of HepaFat-Scan," it is highly probable that the ground truth for training its CNN for ROI detection was established by using data annotated or measured previously by the HepaFat-Scan methodology, likely involving the "Resonance Health's trained analyst" for ROI selection. However, the specifics of this process (e.g., how many cases, who annotated, adjudication) are not detailed in the provided text.

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LiverSmart is indicated to:

For Liver Iron Concentration

1. measure liver iron concentration in individuals with confirmed or suspected systemic iron overload;

2. monitor liver iron burden in transfusion dependents and patients with sickle cell disease receiving blood transfusions:

3. aid in the identification and monitoring of non-transfusion-dependent thalassemia patients receiving therapy with Deferasirox.

For Liver Fat Assessment

1. 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:

2. monitor liver fat content in patients undergoing weight loss management;

3. aid in the assessment and screening of living donors for liver transplant.

LiverSmart is software that utilizes two existing FDA cleared devices, HepaFat-AI (K201039) and FerriSmart (K182218) and combines their respective results into a singular consolidated multiparametric 'LiverSmart' report.

LiverSmart automatically sorts and sends magnetic resonance imaging (MRI) datasets to each of the existing HepaFat-AI and FerriSmart devices and then receives results from those devices to generate a summary report which combines the HepaFat-AI results (an estimate of the patient's volumetric liver fat fraction (VLFF), proton density fat fraction (PDFF), steatosis grade), and the FerriSmat result (an estimate of the patient's liver iron concentration (LIC)).

To conduct analysis, the user simply uploads a single zipped folder containing HepaFat-AI and FerriSmart DICOM images, acquired in accordance with their respective acquisition protocols, to the LiverSmart software. No user input is required for the analysis thereby minimising the impact of human error. The LiverSmart software requires image input data that has been acquired in accordance with the existing and now well established HepaFat-AI (K201039) and FerriSmart (K182218) imaging protocols.

LiverSmart has two new components that are in addition to the existing components of HepaFat-AI and FerriSmart, namely a:

• (i) Data Preparation Module; and
• (ii) Report Generation Module

The rest of the components for LiverSmart are the existing components of the FDA cleared HepaFat-AI and FerriSmart devices, as follows:

For HepaFat-AI:

• (i) Magnetic Resonance Imaging Protocol
• (ii) HepaFat-AI Analysis Software
• (iii) Volumetric Liver Fat Fraction Measurement
• (iv) Proton Density Fat Fraction Measurement
• (v) Steatosis Grade Measurement

For FerriSmart:

• (i) Magnetic Resonance Imaging Protocol
• (ii) FerriSmart Analysis Software
• (iii) Liver Iron Concentration Measurement

The above HepaFat-AI and FerriSmart components are the same as previously provided to the FDA as the time HepaFat-AI and FerriSmart regulatory clearances were sought (and subsequently obtained).

The provided document, K213776, describes the LiverSmart device, which combines the functionalities of two previously cleared devices, FerriSmart (K182218) and HepaFat-AI (K201039). The core claim is that LiverSmart is substantially equivalent to these predicates, not that it offers improved performance beyond what they individually provide. Therefore, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this context refer to demonstrating that LiverSmart accurately integrates the functions of its predicates and produces identical results from the same input data, as opposed to proving novel clinical performance.

Here's an analysis based on the provided text:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for LiverSmart are implicitly derived from the established performance of its predicate devices, FerriSmart and HepaFat-AI, and the requirement for LiverSmart to accurately integrate and reproduce their results.

Study Proving Acceptance Criteria (Verification Testing)

2. Sample size used for the test set and the data provenance:

• The document states "Verification testing confirms that the data preparation module of LiverSmart detects anomalies..." and "Additionally, LiverSmart yields identical results...when the same image datasets are analysed by HepaFat-AI and FerriSmart devices independently."
• Sample Size: The exact sample size used for this verification testing (test set) is not explicitly stated in the provided text.
• Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective. It only mentions using "the same image datasets" as analyzed by the predicate devices.

3. Number of experts used to establish the ground truth for the test set and qualifications of those experts:

• This question is not applicable in the traditional sense for this specific 510(k) submission. The LiverSmart device is an integration of two already cleared devices. The "ground truth" for the individual measurements (LIC, VLFF, PDFF, steatosis grade) would have been established during the original clearance processes for FerriSmart and HepaFat-AI, likely through comparison with biopsy or other approved methods.
• For LiverSmart, the verification testing focuses on the concordance of results between LiverSmart and its predicates, not on establishing new clinical ground truth for the measurements themselves. Therefore, no new "experts" are noted as establishing ground truth for the LiverSmart test set in this document. The "trained physician" mentioned in the Indications for Use is for interpretation, not for establishing algorithmic ground truth.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• The document does not specify any adjudication method for the verification testing. The primary "adjudication" is the direct comparison of LiverSmart's output to the output of FerriSmart and HepaFat-AI on the same datasets, aiming for "identical results."

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• An MRMC comparative effectiveness study was not conducted for LiverSmart as described in this document. LiverSmart is presented as an integration of existing cleared devices, not an improvement or replacement necessitating a comparative effectiveness study involving human readers' diagnostic performance. The document focuses on the algorithmic output matching previously cleared algorithms.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

• Yes, the verification testing described for LiverSmart is a standalone (algorithm only) performance assessment. The text states LiverSmart "automatically sorts and sends...receives results...to generate a summary report" and "No user input is required for the analysis thereby minimising the impact of human error." The verification confirms that LiverSmart's algorithmic output is identical to that of its predicate algorithms on the same input.

7. The type of ground truth used:

• For the verification of LiverSmart, the "ground truth" is effectively the results produced by the FDA-cleared predicate devices (FerriSmart and HepaFat-AI) when processing the same image datasets. The goal was to demonstrate "identical results."
• The original ground truth methodologies (e.g., expert consensus, pathology, outcomes data) for the measurements themselves (LIC, VLFF, PDFF, steatosis grade) would have been established and reviewed during the original 510(k) clearances for FerriSmart and HepaFat-AI, but are not detailed here for LiverSmart.

8. The sample size for the training set:

• The document states that LiverSmart utilizes the "existing components of the FDA cleared HepaFat-AI and FerriSmart devices." It further notes that "The above HepaFat-AI and FerriSmart components are the same as previously provided to the FDA at the time HepaFat-AI and FerriSmart regulatory clearances were sought (and subsequently obtained)."
• Therefore, the training sets (and their sizes) for the underlying convolutional neural networks (CNNs) would pertain to the development of FerriSmart and HepaFat-AI. The sample size for these training sets is not provided in this document (K213776).

9. How the ground truth for the training set was established:

• Similar to point 8, the ground truth for the training sets would have been established during the development and clearance of FerriSmart and HepaFat-AI.
• The document mentions "Convolutional neural networks for the image analysis" for both LiverSmart and its predicates, implying that these models were trained. However, this document (K213776) does not detail how the ground truth for the training sets of the predicate devices was established. This information would be found in the original 510(k) submissions for FerriSmart (K182218) and HepaFat-AI (K201039).

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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.

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.

Here's a summary of the acceptance criteria and study details for HepaFat-AI, based on the provided text:

Acceptance Criteria and Device Performance

Note: The document states that "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." The acceptance criteria were implicitly met by demonstrating comparable performance to the predicate device (HepaFat-Scan), which was previously cleared.

Study Details

1. Sample Size used for the test set and data provenance:

   • Sample Size: 145 adult and pediatric NAFLD/NASH patients.
   • Data Provenance: Not explicitly stated (e.g., country of origin), but implies clinical data from "two independent studies." The document doesn't specify if it's retrospective or prospective for the specific test set as an independent sample, but mentions it was used for "Clinical Validation."

2. Number of experts used to establish the ground truth for the test set and their qualifications:

   • Not specified within the provided text. The ground truth was established via "liver biopsy with histological scoring of steatosis," but the number and qualifications of the pathologists performing the scoring are not detailed.

3. Adjudication method for the test set:

   • Not specified. The ground truth was based on "histological scoring of steatosis," implying a single interpretation per biopsy, though it doesn't detail any multi-reader adjudication process for the biopsies themselves.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

   • No MRMC comparative effectiveness study involving human readers with and without AI assistance is described. The study compared the standalone performance of HepaFat-AI to HepaFat-Scan against biopsy ground truth.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

   • Yes, a standalone performance study was done. The reported sensitivities and specificities are for the HepaFat-AI algorithm itself, compared directly to biopsy results.

6. The type of ground truth used:

   • Pathology (liver biopsy with histological scoring of steatosis).

7. The sample size for the training set:

   • Not specified in the provided text. The document states, "Following the training of the AI tool, the system is 'locked-down' for final validation prior to release in commercial use," but does not give the number of samples used for this training.

8. How the ground truth for the training set was established:

   • Not specified in the provided text. It can be inferred that the ground truth for training would also involve histology, given the ground truth for the validation set, but the method for establishing it on the training set is not detailed.

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FerriSmart is indicated to:
· measure liver iron concentration in individuals with confirmed or suspected systemic iron overload;
· monitor liver iron burden in transfusion dependent thalassemia patients with sickle cell disease receiving blood transfusions:
• aid in the identification and monitoring of non-transfusion-dependent thalassemia patients receiving therapy with deferasirox.

FerriSmart is a stand-alone software application that automatically analyses multi-slice, spin-echo MRI data sets encompassing the abdomen to determine the signal decay rate (R>) that is used to characterize iron loading in the liver, which is then transformed by a defined calibration curve to provide a quantitative measure of liver iron concentrations in vivo.

The software application is a measuring medical device intended to be hosted either in a cloudbased or on site hosted platform and used directly by the radiographer. It does not drive the MRI machine and does not come into direct contact with patients.

The key components of FerriSmart are:

• Specific Magnetic Resonance Imaging Protocol: 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 IQC Module, an automated algorithm that checks the correctness of the parameters of the data acquisition protocol.
• FerriSmart AI Analysis Software: Custom-designed image analysis software performing the R2 measurement based on AI (Artificial Intelligence) technology.
• An additional software module (algorithmic) that Liver Iron Measurement: incorporates a calibration curve relating R2 to liver iron concentration (LIC) is added to allow production of a liver iron concentration report.

The result report provides the patient's average LIC reported in micromole and milligram per gram dry weight of liver. The images analysed are included in the report for review by the radiologist. The results are intended to assist in clinical diagnosis, and/or in making decisions concerning clinical management.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

LIC Threshold: 3.0 mg Fe/g dry tissue (interrupt deferasirox for NTDT):
Sensitivity: 96% (95% CI 94-97%)
Specificity: 95% (95% CI 92-98%)

LIC Threshold: 3.2 mg Fe/g dry tissue (historical HHC definition, lower optimal for chelation):
Sensitivity: 94% (95% CI 92-96%)
Specificity: 95% (95% CI 92-98%)

LIC Threshold: 5.0 mg Fe/g dry tissue (consider deferasirox for NTDT):
Sensitivity: 91% (95% CI 89-94%)
Specificity: 97% (95% CI 95-99%)

LIC Threshold: 7.0 mg Fe/g dry tissue (upper optimal for chelation, increased risk):
Sensitivity: 92% (95% CI 90-95%)
Specificity: 97% (95% CI 95-98%)

LIC Threshold: 15.0 mg Fe/g dry tissue (greatly increased cardiac risk, increase deferasirox dose):
Sensitivity: 89% (95% CI 85-93%)
Specificity: 98% (95% CI 98-99%)

Overall, most sensitivities and specificities are above 90%, with the exception of specificity at 1.8 mg Fe/g dry tissue (80%) and sensitivity at 15.0 mg Fe/g dry tissue (89%). These exceptions are deemed acceptable for clinical use. |
| Usability | All participants found the product easy to use, fast, and technically reliable (no bugs). |
| Software Verification & Validation | Developed, verified, and validated following Design Control principles and General Principles of Software Validation guidelines. |

Study Information

1. Sample sizes used for the test set and the data provenance:

   • Repeatability Study Test Set: 60 subjects scanned twice. The provenance of this data (country of origin, retrospective/prospective) is not specified.
   • Clinical Study Test Set: 971 datasets from multiple makes and models of MRI scanners. The provenance (country of origin, retrospective/prospective) is not specified.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • The ground truth for the clinical study was the predicate device, FerriScan R2-MRI Analysis System. For the predicate, "human interaction for Region of Interest (ROI) selection" was noted.
   • The text does not specify the number or qualifications of experts involved in establishing the FerriScan results used as ground truth for this FerriSmart study. It only mentions that FerriScan is used "in-house by Resonance Health's analysts."

3. Adjudication method for the test set:

   • The text does not explicitly state an adjudication method (such as 2+1 or 3+1) for the comparison between FerriSmart and FerriScan results, or for the FerriScan results themselves. The ground truth was based on the FerriScan device output.

4. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No, an MRMC comparative effectiveness study was not done. The study's clinical performance evaluation was a standalone performance assessment of FerriSmart against a predicate device (FerriScan), not a comparison of human readers with vs. without AI assistance. The user of FerriSmart is stated to be a radiologist, who oversees the report, but the study focuses on the algorithm's performance relative to the predicate.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Yes, a standalone performance study was done. FerriSmart is described as a "stand-alone software application" which "automatically analyses" MRI data. The clinical study assessed its R2 and LIC measurements and diagnostic performance against the predicate without direct human intervention in the analysis process besides the radiologist reviewing the final report. FerriSmart uses an algorithm for automatic quality checks, whereas the predicate "requires human input" for some checks.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • The ground truth for the study was the results from the predicate device, FerriScan R2-MRI Analysis System. FerriScan itself uses an algorithmic approach with human interaction for ROI selection and aims to provide quantitative measures of LIC, which would ultimately correlate to other clinical ground truths like liver biopsy in its own validation studies (as hinted by the comparison to biopsy error rates).

7. The sample size for the training set:

   • The document states, "FerriSmart AI Analysis Software has been trained on FerriScan data." However, the sample size for the training set is not explicitly provided in the given text.

8. How the ground truth for the training set was established:

   • The ground truth for the FerriSmart training set was established using data processed by the predicate device, FerriScan R2-MRI Analysis System. The text explicitly states, "FerriSmart AI Analysis Software has been trained on FerriScan data." This implies that the outputs from FerriScan (R2 measurements and LIC values) were used as the target for the AI's learning process.

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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.

Standalone software application to facilitate the import visualization of multi-slice, gradient-echo MRI 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.

Here's an analysis of the HepaFat-Scan device, outlining its acceptance criteria and the study used for validation, based on the provided text:

HepaFat-Scan Acceptance Criteria and Validation Study

The provided 510(k) summary for HepaFat-Scan focuses on demonstrating substantial equivalence to existing predicate devices, rather than establishing specific quantitative acceptance criteria in the traditional sense of a performance study with defined thresholds for sensitivity, specificity, etc. The primary acceptance criterion here is "acceptable agreement" with a recognized ground truth method across a clinically relevant range.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not specify numerical thresholds (e.g., correlation coefficients, mean absolute error limits) for "acceptable agreement." This is common in substantial equivalence claims where the goal is to show the new device performs similarly to established methods, rather than surpassing a specific, pre-defined performance metric.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: Not explicitly stated. The document refers to "in-vivo human clinical studies" where volumetric fat fractions were determined from liver biopsy samples. The exact number of patients or biopsy samples is not provided.
• Data Provenance: "in-vivo human clinical studies." The country of origin is not specified, but the applicant's address is Australia, suggesting the studies could have been conducted there. The studies were likely retrospective or a combination of retrospective and prospective, as biopsy data is often collected as part of routine patient care.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated, however, the ground truth was established by determining "volumetric fat fractions from liver biopsy samples... using a stereological method based on the Delesse principle." This implies the involvement of pathologists or trained laboratory technicians skilled in histopathology and stereological analysis.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly mentioned. Since the ground truth involved a quantitative stereological method on biopsy samples, it's less likely to involve a consensus-based adjudication process typical of qualitative image interpretations. The "agreement" was likely a direct comparison of the HepaFat-Scan's quantitative output against the quantitative biopsy-derived fat fraction.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted in the context of human readers improving with AI assistance. The study focused on the standalone performance of the HepaFat-Scan software compared to biopsy, not on its impact on human interpretation.

6. Standalone Performance (Algorithm Only without Human-in-the-loop Performance)

• Standalone Performance: Yes, a standalone performance study was conducted. The HepaFat-Scan is described as a "Standalone software application," and its "Accuracy and reproducibility of the quantification of the triglyceride fat fraction has been demonstrated through a combination of bench testing and in-vivo human clinical studies." This indicates the algorithm's output was directly compared to the ground truth.

7. Type of Ground Truth Used

• Type of Ground Truth: The primary ground truth for the in-vivo human clinical studies was pathology-derived data. Specifically, "Volumetric fat fractions were determined from liver biopsy samples... using a stereological method based on the Delesse principle, which directly measures volumetric fat fraction."

8. Sample Size for the Training Set

• Training Set Sample Size: Not provided. The document describes the validation study (test set) but does not disclose information about the training data used to develop the HepaFat-Scan algorithm.

9. How the Ground Truth for the Training Set Was Established

• Ground Truth for Training Set: Not provided. The document focuses exclusively on the validation study and its ground truth. Information on how the algorithm was developed or trained, including the ground truth used for that process, is not included in this summary.

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The FerriScan R2-MRI Analysis System is intended to measure liver iron concentration to aid in the identification and monitoring of non-transfusiondependent thalassemia patients receiving therapy with deferasirox.

The FerriScan R2-MRI Analysis System is a post-processing software tool that measures liver iron concentration based on the proton transverse relaxation rate (R2) of MRI images. R2 values are converted to liver iron concentration measurements using a calibration curve.

The FerriScan R2-MRI Analysis System is a post-processing software tool that measures liver iron concentration (LIC). It is intended to aid in the identification and monitoring of non-transfusion-dependent thalassemia (NTDT) patients receiving deferasirox therapy.

Here's an analysis of the acceptance criteria and the studies proving the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text excerpts do not explicitly list formal "acceptance criteria" with numerical thresholds for performance metrics. Instead, the document describes the required performance testing and the results of those tests, along with risks and mitigation measures that implicitly define acceptable performance. The key performance indicators evaluated are: Precision, Bias, Repeatability, Reproducibility, Sensitivity, and Specificity.

Therefore, the table below consolidates the relevant performance data from the provided text, indicating how the device performed against the implicitly required characteristics.

• Validation Study (233 patients): Bland-Altman 95% limits of agreement: 74% and -71%. |
  | Bias (systematic measurement error) | - Calibration Study (105 patients): Bland-Altman 95% limits of agreement with liver biopsy: -56% to 50% with bias of -3%.
• Validation Study (233 patients): Bland-Altman 95% limits of agreement: 74% and -71% with a bias of 1.9%.
• Mean percentage differences in LIC between FerriScan and liver biopsy were not significantly different than zero in either study. |
  | Repeatability (precision under same conditions over short period) | - 60 individuals tested twice: Standard deviation in R2 measurement: 8.1%.
• Consistent with 7.7% random error from initial 10-patient calibration study. |
  | Reproducibility (precision under different locations/operators) | - Phantom testing (K043271): Coefficient of variability across 13 different scanners: < 2.1%. |
  | Sensitivity at various LIC thresholds (95% CI) | - 1.8 mg Fe/g dw: 94% (86-97)
• 3.2 mg Fe/g dw: 94% (85-98)
• 7.0 mg Fe/g dw: 89% (79-95)
• 15 mg Fe/g dw: 85% (70-94) |
  | Specificity at various LIC thresholds (95% CI) | - 1.8 mg Fe/g dw: 100% (88-100)
• 3.2 mg Fe/g dw: 100% (91-100)
• 7.0 mg Fe/g dw: 96% (86-99)
• 15 mg Fe/g dw: 89% (83-96) |
  | Acceptance testing of images prior to processing | Labeling specifies instructions (e.g., FerriScan Phantom Pack use, acquisition settings, visual inspection, motion assessment). |
  | Data processing quality assurance protocols | Labeling describes protocols for phantom and patient image processing (e.g., ROI selection, noise assessment, motion correction). |

2. Sample Sizes and Data Provenance

• Test Sets (Clinical Studies for Precision and Bias):
  • Calibration Study: 105 patients.
  • Validation Study (subgroup from ESCALATOR trial): 233 patients.
  • Repeatability Study: 60 individuals.
  • Sensitivity/Specificity Study (from K043271): No specific number provided for the sensitivity/specificity calculation itself, but likely derived from the calibration study population.
• Data Provenance: The document does not explicitly state the country of origin for the patient data for these specific performance studies. However, the contact for the device is in Australia, and the deferasirox trials mentioned were likely international. The studies described are clinical studies, implying prospective data collection focused on the device's performance in patient populations, although the sensitivity/specificity study dates back to the original 510(k) (K043271), making it potentially a retrospective analysis of previously collected data.

3. Number of Experts and Qualifications for Ground Truth

The document does not specify the number or qualifications of experts used to establish the ground truth for the test sets.

4. Adjudication Method for the Test Set

The document does not describe any expert adjudication method (e.g., 2+1, 3+1) for the test sets.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study evaluating human readers with vs. without AI assistance is mentioned. The device is a "standalone" image post-processing system that provides a numerical LIC value, not an AI-assisted interpretation by a human reader.

6. Standalone Performance (Algorithm Only without Human-in-the-Loop)

Yes, standalone performance was done. The FerriScan R2-MRI Analysis System is described as a "post-processing software tool that measures liver iron concentration." The performance metrics (precision, bias, repeatability, reproducibility, sensitivity, specificity) are inherently measures of the algorithm's direct output (LIC value) compared to the ground truth. There is no mention of "human-in-the-loop" interaction for interpreting the R2-MRI calculation itself.

7. Type of Ground Truth Used

The primary ground truth used for assessing the device's performance (precision, bias, sensitivity, and specificity) was:

• Atomic absorption spectrometry from liver biopsy: This is considered the reference measurement of LIC.

8. Sample Size for the Training Set

The document does not explicitly state the sample size used for training the FerriScan R2-MRI Analysis System. The calibration study (105 patients) was used to "define an empirically-derived relationship" between R2 values and LIC, which implies it contributed to the creation or refinement of the calibration curve within the software. It's possible this study served as or contributed to the training data.

9. How the Ground Truth for the Training Set Was Established

Assuming the "calibration study" described (105 patients) contributed to establishing the relationship used within the software (effectively the "training" aspect for the calibration curve), the ground truth for this calibration was established using atomic absorption spectrometry from liver biopsy.

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The MRI-Q System is intended to provide additional processing of images for the analysis of multi-slice gradient echo MRI data sets of cardiac tissue to quantify the signal decay rate (R2*) and time (T2*).

The MRI-Q System is a software device that provides quantitative analysis of magnetic resonance image signal decay parameters in cardiac tissue. The device contains an image viewer for importing DICOM images, browsing through datasets and performing quantitative region of interest analysis. The software device has capability to measure the signal decay rate (R2*) and time (T2*) in heart tissue.

Stand alone software package used to facilitate the import, visualization and analysis of multi-slice gradient echo MRI data sets encompassing cardiac tissue, to quantify the signal decay rate (R2*) and time (T2*).

This document is a 510(k) summary for the MRI-Q System, a software device for analyzing cardiac MRI data. It does not contain the specific details about acceptance criteria, study design, or performance metrics that would typically be found in a study report. The document primarily focuses on establishing substantial equivalence to predicate devices for regulatory approval.

Therefore, I cannot provide a table of acceptance criteria and reported device performance or details about a study proving the device meets acceptance criteria based only on the provided text.

However, I can extract information related to the device description and intended use, which are foundational for any performance criteria:

Device Description and Intended Use:

• Function: Standalone software package used to facilitate the import, visualization, and analysis of multi-slice gradient echo MRI data sets encompassing cardiac tissue.
• Quantifies: Signal decay rate (R2*) and time (T2*).
• Operational Principle: Based on fitting signal decay curves to magnetic resonance image signal intensities acquired at different echo times on a voxel-by-voxel (3-D pixel) basis to determine R2* and T2*.
• Intended Use: To provide additional processing of images for the analysis of multi-slice gradient echo MRI data sets of cardiac tissue to quantify the signal decay rate (R2*) and time (T2*).
• Specific Capabilities: Image viewer for importing DICOM images, browsing datasets, and performing quantitative region of interest analysis.

To answer the specific questions about acceptance criteria and study details, more comprehensive information, typically found in a clinical study report or a more detailed section of the 510(k) submission, would be required. The provided text is a summary for regulatory approval, comparing the new device to existing predicate devices, rather than a detailed performance study report.

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