Libby Echo:Prio

K220956

Dyad Medical, Inc · cleared 2022-07-20 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“Libby™ Echo:Prio is software that is used to process previously acquired DICOM-compliant cardiac ultrasound images, and to make measurements on these images in order to provide automated estimation of several cardiac measurements. Machine learning based view classification and border segmentation form the basis for this automated analysis.”
AlgorithmMachine learning based view classification and border segmentation
source quote (p.5)
“Machine learning based view classification and border segmentation form the basis for this automated analysis.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.7)
“Content of Premarket Submission for Management of Cybersecurity in Medical Devices.””

Validation studies (2)

Retrospective clinical

sample size not stated

endpoints: view classification accuracy; F1 value; sensitivity; specificity; HR output estimate; ED/ES identification

standards: IEC 62304:2006/AC: 2008- Medical device software – Software life cycle processes, FDA Guidance documents, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices", Content of Premarket Submission for Management of Cybersecurity in Medical Devices, NEMA PS 3.1 - 3.20 2021e Digital Imaging and Communications in Medicine (DICOM) Set, IEC 62304:2006/A1:2016 Medical device software - Software life cycle processes, ISO 14971:2019 Medical Devices -- Application of Risk Management to Medical Devices, IEC 62366-1 Edition 1.1 2020-06 Medical Devices -- Part 1: Application of Usability Engineering to Medical Devices, 21 CFR 820 Quality System Regulations, ISO 15223-1 Medical devices -- Symbols to be used with medical device labels, labelling and information to be supplied -- Part 1: General requirements

Reader study (MRMC)

sample size not stated

endpoints: EF output

standards: IEC 62304:2006/AC: 2008- Medical device software – Software life cycle processes, FDA Guidance documents, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices", Content of Premarket Submission for Management of Cybersecurity in Medical Devices, NEMA PS 3.1 - 3.20 2021e Digital Imaging and Communications in Medicine (DICOM) Set, IEC 62304:2006/A1:2016 Medical device software - Software life cycle processes, ISO 14971:2019 Medical Devices -- Application of Risk Management to Medical Devices, IEC 62366-1 Edition 1.1 2020-06 Medical Devices -- Part 1: Application of Usability Engineering to Medical Devices, 21 CFR 820 Quality System Regulations, ISO 15223-1 Medical devices -- Symbols to be used with medical device labels, labelling and information to be supplied -- Part 1: General requirements

Reported performance (3 observations)

sensitivity96.8
source quote (p.7)
“average sensitivity (Sn) of 96.8%”
specificity98.5
source quote (p.7)
“average Specificity (Sp) of 98.5%”
f1as written: “F1 value”96.6
source quote (p.7)
“average F1 value of >96.6%”

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

0
recalls in product code, 24mo
3
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); 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/K220956