FDA 510(k) Search - By Innolitics (SaMD and AI Experts)

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

For Liver Fat Assessment

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

Device Description

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).

AI/ML Overview

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.

Acceptance Criteria Category	Specific Criteria (Implicitly from document)	Reported Device Performance and Evidence (from document)
Functional Equivalence	Detect anomalies in sequence acquisition and report accurate error messages.	"Verification testing confirms that the data preparation module of LiverSmart detects anomalies in the sequence acquisition and reports the accurate error message. If an error is detected LiverSmart prevents further analysis."
Result Concordance	Yield identical results for VLFF, PDFF, steatosis grade, and LIC when compared to independent analysis by HepaFat-AI and FerriSmart.	"Additionally, LiverSmart yields identical results for VLFF, PDFF, steatosis grade, and LIC, when the same image datasets are analysed by HepaFat-AI and FerriSmart devices independently."
Safety and Effectiveness	Maintain the safety and effectiveness profile established by the predicate devices.	"LiverSmart is based upon the same technologies, operating principle, and software technology as the two predicate devices. Risk activities were conducted in concurrence with established medical device development standards and guidance." and "Resonance Health believes that enough evidence has been presented in this dossier to conclude that LiverSmart is safe, effective and performs as well as two the predicates."
Quality Systems	Designed and manufactured under Quality System Regulations (21 CFR § 820 and ISO 13485 Standards).	"LiverSmart is designed and manufactured under the Quality System Regulations as outlined in 21 CFR § 820 and ISO 13485 Standards."
Harmonization	Conformity with design controls; test methods are the same as those documented in previously cleared submissions of predicates.	"A statement of conformity with design controls is included in this submission." and "The test methods used are the same as those documented in the previously cleared submissions of the predicate devices, FerriSmart (K182218) and HepaFat-AI (201039)."

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).

Summary

Regulation Number and Section

§ 892.1001 Liver iron concentration imaging companion diagnostic for deferasirox.

(a)
Identification. The liver iron concentration imaging companion diagnostic for deferasirox is an image processing device intended to aid in the identification and monitoring of non-transfusion-dependent thalassemia patients receiving therapy with deferasirox. The device calculates a numeric value for liver iron concentration based on magnetic resonance images acquired under controlled conditions. The calculated numeric value is used to assess the need for deferasirox treatment and for monitoring treatment in patients with non-transfusion-dependent thalassemia. The liver iron concentration imaging companion diagnostic for deferasirox is essential to the safe and effective use of deferasirox in patients with non-transfusion-dependent thalassemia.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include nonclinical and clinical performance testing demonstrating the bias, precision, repeatability, and reproducibility of liver iron concentration measurements.
(2) Labeling must include specifying:
(i) Instructions for acceptance testing of images prior to processing;
(ii) Data processing quality assurance protocols; and
(iii) The sensitivity and specificity of liver iron concentration measurements.