HepaFat-AI

K201039

Resonance Health Analysis Services Pty Ltd · cleared 2020-12-07 · product code LNH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
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).
AlgorithmCustom-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.
source quote (p.5)
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.
Adaptive (vs locked)No
source quote (p.5)
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.
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

n=145 patients

endpoints: sensitivity; specificity; negative percent agreement (NPA); positive percent agreement (PPA); repeatability; user testing results

standards: Software as a Medical Device (SaMD): Clinical Evaluation. Final Guidance for Industry and FDA Staff. December 2017, 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

Reported performance (6 observations)

sensitivityas written: “Sensitivity (Grade 0 vs Grades 1-3)97.6CI 93.3% to 99.2%
source quote (p.9)
97.6 (93.3% to 99.2%)
specificityas written: “Specificity (Grade 0 vs Grades 1-3)88.2CI 65.7% to 96.7%
source quote (p.9)
88.2 (65.7% to 96.7%)
sensitivityas written: “Sensitivity (Grades 0 & 1 vs Grades 2 & 3)86.1CI 76.8% to 92.0%
source quote (p.9)
86.1 (76.8% to 92.0%)
specificityas written: “Specificity (Grades 0 & 1 vs Grades 2 & 3)74.8CI 66.6% to 81.5%
source quote (p.9)
74.8 (66.6% to 81.5%)
sensitivityas written: “Sensitivity (Grades 0-2 vs Grade 3)100CI 81.6% to 100.0%
source quote (p.9)
100.0 (81.6% to 100.0%)
specificityas written: “Specificity (Grades 0-2 vs Grade 3)71.4CI 62.4% to 78.1%
source quote (p.9)
71.4 (62.4% to 78.1%)

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). 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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K201039