HepaFatSmart (V2.0.0)

K231459

Resonance Health Analysis Services Pty Ltd · cleared 2023-06-20 · product code LNH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“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.”
AlgorithmIt is composed of one (1) convolutional neural network (CNN) performing liver ROI detection with undesired components (artefacts and major blood vessels) considered/removed using a computer vision technique with machine learning technology. Algorithmic for the image quality checking and calculated Alpha conversion into VLFF, PDFF and Steatosis grade. Algorithms for the measurement and calculation of Alpha VLFF, PDFF and Steatosis grade.
source quote (p.5)
“It is composed of one (1) convolutional neural network (CNN) performing liver ROI detection with undesired components (artefacts and major blood vessels) considered/removed using a computer vision technique with machine learning technology. Background noise correction is not considered as there is no or very minimal impact on the analysis outcome. Following the training of the Al assisted device, the system is completely 'locked down' for final validation prior to release in commercial use to ensure reproducibility of the results. In principle, the HepaFatSmart v2.0.0 uses the same MRI data analysis approach as HepaFat-Scan.”
Adaptive (vs locked)No
source quote (p.5)
“Following the training of the Al assisted device, the system is completely 'locked down' for final validation prior to release in commercial use to ensure reproducibility of the results.”
PCCPNo
Cybersecurity addressedNo

Validation studies (2)

Retrospective clinical

n=42 patients

endpoints: device performance; analysis outcome reproducibility

Retrospective clinical

n=300 patients

endpoints: device performance; sensitivities and specificities for predicting HepaFat-Scan VLFF values greater than clinically relevant thresholds

Reported performance (2 observations)

sensitivity100CI 97.3 - 100.0
source quote (p.14)
“100.0 (97.3 - 100.0)”
specificity98.6CI 94.9 - 99.6
source quote (p.14)
“98.6 (94.9 - 99.6)”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

100
recalls in product code, 24mo
510
MAUDE reports in code, 12mo
+5%
vs code's own 3-yr baseline
2
drift signals on this device
  • recall_reason_pattern

    Software/algorithm-related recall in product code LNH (Philips North America, initiated 2026-04-14): "The potential for stiffness value errors when a specific range of image reconstruction parameters is used in combination with Resoundant's algorithm, leading to the reconstruction " Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:98779

  • recall_reason_pattern

    Software/algorithm-related recall in product code LNH (Philips North America, initiated 2025-12-03): "The potential for stiffness value errors when viewing exported MR Elastography (MRE) stiffness maps to viewer Picture Archiving and Communication System (PACS)." Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:98111

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K231459