Withings ECG App

K240795

Withings · cleared 2025-06-15 · product code QDA · Cardiovascular

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“The Withings ECG App is a software only mobile medical application that has two components: Withings ECG Watch App Withings ECG Phone App”
Algorithmmachine learning techniques for ECG rhythm classification; AFib detection algorithm
source quote (p.6)
“Withings ECG Watch App uses machine learning techniques for ECG rhythm classification. Both are software only devices including an AFib detection algorithm. The algorithm then processes and classifies the signal and displays the classification to the user.”
Adaptive (vs locked)No
source quote (p.12)
“A fourth dataset, WEFA HWA09 part 2, was used as a second layer of validation after the software freeze (“locked algorithm”) to verify that the hyperparameters tuning were not overfit.”
PCCPNo
Cybersecurity addressedNo

Validation studies (2)

Bench

sample size not stated

standards: IEC 60601-2-47:2012 Medical Electrical Equipment - Ambulatory ECG Systems, IEC 62368-1:2014, ANSI/AAMI ES60601-1:2005/(R) 2012 and A1:2012, C1:2009/(R) 2012 and A2:2010/(R)2012 Medical Electrical Equipment – Part 1: General Requirements For Basic Safety And Essential Performance,, IEC 60601-1-2:2014 Medical Electrical Equipment Part 1-2: General Requirements For Basic Safety And Essential Performance – Collateral Standard: Electromagnetic Compatibility Requirements And Tests, and, IEC 60601-1-11: Medical electrical equipment – Part 1-11: General requirements for basic safety and essential performance - Collateral Standard: Requirements for medical electrical equipment and medical electrical systems used in the home healthcare environment., IEC 62304:2006/Amd 1:2015 - Medical device software, AAMI/ANSI/IEC 62366-1 : Medical Devices Part 1: Application of Usability Engineering to Medical Devices : 2015, ISO 14971: Medical Devices Application of Risk Management to Medical Devices, AAMI TIR69:2017/(R2020) - Wireless coexistence, ANSI IEEE C63.27-2017 - Wireless coexistence, FCC testing per part 15

Prospective clinical

n=626 patients

endpoints: classify an ECG recording into AFib and sinus rhythm

Reported performance (8 observations)

sensitivity99.7
source quote (p.13)
“The Withings ECG App demonstrated 99.7% sensitivity in classifying AFib (HR 50-150 bpm) and 99.8% specificity in classifying sinus rhythm (HR 50-150 bpm) in classifiable recordings.”
specificity99.8
source quote (p.13)
“The Withings ECG App demonstrated 99.7% sensitivity in classifying AFib (HR 50-150 bpm) and 99.8% specificity in classifying sinus rhythm (HR 50-150 bpm) in classifiable recordings.”
sensitivityas written: “Sensitivity across all age groups”stated without valueCI 99.0% - 100%
source quote (p.13)
“Subgroup analysis indicated sensitivity ranged from 99.0% - 100% across all age groups, and specificity ranged from 98.2% - 100%.”
specificityas written: “Specificity across all age groups”stated without valueCI 98.2% - 100%
source quote (p.13)
“Subgroup analysis indicated sensitivity ranged from 99.0% - 100% across all age groups, and specificity ranged from 98.2% - 100%.”
sensitivityas written: “Sensitivity for females”100
source quote (p.14)
“Specificity and sensitivity estimates were similar for females (100% and 99.6% respectively) and for males (99.5% and 100% respectively).”
specificityas written: “Specificity for females”99.6
source quote (p.14)
“Specificity and sensitivity estimates were similar for females (100% and 99.6% respectively) and for males (99.5% and 100% respectively).”
sensitivityas written: “Sensitivity for males”99.5
source quote (p.14)
“Specificity and sensitivity estimates were similar for females (100% and 99.6% respectively) and for males (99.5% and 100% respectively).”
specificityas written: “Specificity for males”100
source quote (p.14)
“Specificity and sensitivity estimates were similar for females (100% and 99.6% respectively) and for males (99.5% and 100% respectively).”

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
8
MAUDE reports in code, 12mo
+14%
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); 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 Cardiovascular 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/K240795