EdgeFlow UH10

K231677

Edgecare Inc. · cleared 2024-03-06 · product code IYO · Radiology

Premarket evidence — what FDA accepted

Device typehardware with ml

source quote (p.3)

“The EdgeFlow UH10 is an ultrasound device intended to be used for measuring the urine volume in the bladder noninvasively. It is intended for use in professional healthcare facilities, such as hospitals, clinics, by qualified and trained healthcare professionals. The EdgeFlow UH10 supports B-mode and harmonic imaging modes. The deep learning model employed in EdgeFlow UH10 comprises three components: feature extraction, binary classification, and semantic segmentation networks.”

Algorithmdeep learning model comprising feature extraction (MobileNetV2), binary classification (fully connected neural network), and semantic segmentation (DeepLabv3+) networks.

source quote (p.5)

“The deep learning model employed in EdgeFlow UH10 comprises three components: feature extraction, binary classification, and semantic segmentation networks. B-mode ultrasound images undergo classification in the network to determine the presence of the bladder, while the segmentation network is responsible for delineating the bladder area. Live B-mode ultrasound images are acquired once the scan button is pressed to start scanning. The output of the deep learning model manifests as the bladder contours displayed as green lines in the ultrasound images, and the bladder volume is subsequently calculated based on these lines when the scan button is pressed.”

Adaptive (vs locked)No

PCCPFDA source did not state this

Cybersecurity addressedYes

source quote (p.11)

“Cybersecurity Test”

Validation studies (1)

Retrospective clinical

n=3,711 images · 1 site(s)

endpoints: F1 score; PR AUC; Dice Score

standards: ISO 10993-1, ISO 10993-5, ISO 10993-10, ISO 10993-23, IEC 60601-1:2005+A1:2012, IEC 60601-1-2:2014/AMD1:2020, ANSI IEEE C63.27-2017, IEC 62304:2006/A1:2015, FDA Guidance ("Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices"), FDA Guidance ("Off-the-Shelf Software Use in Medical Devices"), AAMI/UL 29001-:2017, IEC 81001-5-1:2021, FDA Guidance ("Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions"), IEC 60601-2-37:2007/AMD1:2015

Reported performance (2 observations)

f1as written: “F1 score”0.979CI 95% CI 0.974–0.984

source quote (p.13)

“The test criteria of classification accuracy and segmentation accuracy were both satisfied with an F1 score of 0.979 (95% CI 0.974–0.984)”

diceas written: “Dice score”0.896CI 95% CI 0.890-0.901

source quote (p.13)

“and a Dice score of 0.896 (95% CI 0.890-0.901).”

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

recalls in product code, 24mo

344

MAUDE reports in code, 12mo

-40%

vs code's own 3-yr baseline

drift signals on this device

recall_reason_pattern
Software/algorithm-related recall in product code IYO (Civco Medical Instruments Co. Inc., initiated 2026-03-02): "There was an error in inspection and programming of the eTRAX needle sensor for Aurora trackers. The result is a potential for the needle tip position to be incorrectly identified " Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:98513

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).