Libby Echo:Prio
K220956Dyad Medical, Inc · cleared 2022-07-20 · product code QIH · Radiology
Premarket evidence — what FDA accepted
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.”
source quote (p.5)
“Machine learning based view classification and border segmentation form the basis for this automated analysis.”
source quote (p.7)
“in addition to the FDA Guidance documents, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices.””
Validation studies (1)
Retrospective clinical
sample size not stated
endpoints: view classification accuracy; F1 value; sensitivity; specificity; HR output estimate; ED/ES identification; Ejection Fraction (EF) prediction
standards: 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, 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 (4 observations)
source quote (p.7)
“The testing demonstrated view classification accuracy of 97% with an average F1 value of >96.6%, average sensitivity (Sn) of 96.8% and average Specificity (Sp) of 98.5%.”
source quote (p.7)
“The testing demonstrated view classification accuracy of 97% with an average F1 value of >96.6%, average sensitivity (Sn) of 96.8% and average Specificity (Sp) of 98.5%.”
source quote (p.7)
“The testing demonstrated view classification accuracy of 97% with an average F1 value of >96.6%, average sensitivity (Sn) of 96.8% and average Specificity (Sp) of 98.5%.”
source quote (p.7)
“The testing demonstrated view classification accuracy of 97% with an average F1 value of >96.6%, average sensitivity (Sn) of 96.8% and average Specificity (Sp) of 98.5%.”
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
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).