FerriSmart Analysis System

K182218

Resonance Health Analysis Services Pty Ltd · cleared 2018-11-30 · product code PCS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
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 (R2) 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 cloud-based 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.
AlgorithmCustom-designed image analysis software performing R2 measurement based on AI (Artificial Intelligence) technology, using convolutional neural networks for image recognition and analysis, and incorporating a calibration curve for Liver Iron Concentration (LIC).
source quote (p.5)
FerriSmart AI Analysis Software: Custom-designed image analysis software performing the R2 measurement based on AI (Artificial Intelligence) technology. Convolutional neural networks for the image analysis. Algorithmic for the images quality checks and R2 conversion into LIC.
Adaptive (vs locked)No
PCCPFDA source did not state this
Cybersecurity addressedNo

Validation studies (3)

Bench

sample size not stated

endpoints: usability; readability of the patient report; Instructions for Use

Bench

n=60 patients

endpoints: precision of FerriSmart

Retrospective clinical

n=971 other

endpoints: Assessing the performance of the FerriSmart IQC module; Assessing the bias and limits of agreement between FerriSmart and FerriScan measurements of LIC on multiple scanners; Assessing the diagnostic performance of FerriSmart for predicting FerriScan LIC results above various clinically relevant LIC thresholds on multiple scanners

standards: EN ISO 14971:2012 – Medical Devices – Application of Risk Management to Medical Devices

Reported performance (2 observations)

sensitivity96CI 94-97
source quote (p.10)
96 (94-97)
specificity80CI 73-87
source quote (p.10)
80 (73-87)

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

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
vs code's own 3-yr baseline
0
drift signals on this device

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/K182218