FETOLY

K251368

Diagnoly · cleared 2025-09-12 · product code IYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Fetoly is a software that aims at helping sonographers, obstetricians, radiologists, maternal-fetal medicine specialists and pediatric cardiologists (designated as healthcare professionals i.e. HCPs) to perform fetal ultrasound examinations of the fetal brain and heart in real-time. Operates as a local software functioning independently from the ultrasound equipment.”
Algorithmdeep learning algorithm, trained by supervised learning, utilizes computer vision algorithms
source quote (p.6)
“The software's frozen deep learning algorithm, which was trained by supervised learning, analyzes images of this ultrasound image stream to detect brain/heart views, quality criteria within those views and to make biometric measurements of the heart and brain. Artificial Intelligence: Utilizes computer vision algorithms to analyze ultrasound images and provides visualization of detected landmarks and views”
Adaptive (vs locked)No
source quote (p.16)
“The PCCP does not include the implementation of adaptive algorithms that will continuously learn in the field.”
PCCPYes
source quote (p.2)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP).”
Cybersecurity addressedYes
source quote (p.16)
“Cybersecurity verification and penetration testing”

Validation studies (2)

Retrospective clinical

n=750 patients · 7 site(s)

endpoints: sensitivity of fetal heart and brain ultrasound views; specificity of fetal heart and brain ultrasound views; sensitivity of quality criteria within views; specificity of quality criteria within views; mean intersection over union (IoU) of bounding boxes of quality criteria

Retrospective clinical

n=441 patients · 6 site(s)

endpoints: sensitivity of fetal heart and brain ultrasound subviews; specificity of fetal heart and brain ultrasound subviews; limits of agreement (LOA) for fetal heart and brain biometric measurements

Reported performance (10 observations)

sensitivity0.96CI (0.954-0.966)
source quote (p.18)
“mean sensitivity of 96% with a Bootstrap CI of (0.954-0.966)”
specificity0.995CI (0.994-0.996)
source quote (p.18)
“mean specificity of 99.5% with a Bootstrap CI of (0.994-0.996).”
sensitivityas written: “mean sensitivity of 52 fetal heart quality criteria”0.946CI (0.939-0.952)
source quote (p.18)
“mean sensitivity of 94.6%% with a Bootstrap CI of (0.939-0.952)”
specificityas written: “mean specificity of 52 fetal heart quality criteria”0.994CI (0.994-0.995)
source quote (p.18)
“mean specificity of 99.4% with a Bootstrap CI of (0.994-0.995)”
iouas written: “mean IoU of 52 fetal heart quality criteria”0.681CI (0.672-0.691)
source quote (p.18)
“mean IoU of 68.1% with a Bootstrap CI of (0.672-0.691).”
sensitivityas written: “mean sensitivity of 43 fetal brain quality criteria”0.913CI (0.904-0.924)
source quote (p.19)
“mean sensitivity of 91.3% with a Bootstrap CI of (0.904-0.924)”
specificityas written: “mean specificity of 43 fetal brain quality criteria”0.994CI (0.994-0.995)
source quote (p.19)
“mean specificity of 99.4% with a Bootstrap CI of (0.994-0.995)”
iouas written: “mean IoU of 43 fetal brain quality criteria”0.6CI (0.594-0.605)
source quote (p.19)
“mean IoU of 60% with a Bootstrap CI of (0.594-0.605).”
sensitivityas written: “mean sensitivity of fetal heart and brain subviews”0.918CI (0.906, 0.931)
source quote (p.21)
“mean sensitivity of 91.8% with a Bootstrap CI of (0.906, 0.931)”
specificityas written: “mean specificity of fetal heart and brain subviews”0.999CI (0.998, 1.000)
source quote (p.21)
“mean specificity of 99.9% with a Bootstrap CI of (0.998, 1.000).”

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

85
recalls in product code, 24mo
554
MAUDE reports in code, 12mo
+85%
vs code's own 3-yr baseline
4
drift signals on this device
  • recall_reason_pattern

    Software/algorithm-related recall in product code IYN (Philips Ultrasound, LLC, initiated 2025-10-31): "Ultrasound system compatibility issues with Apple devices running iOS 18 may cause a failure to perform live imagining." Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:97843

  • recall_reason_pattern

    Software/algorithm-related recall in product code IYN (GE Medical Systems, LLC, initiated 2025-09-18): "The Ultrasound-Guided Attenuation Parameter (UGAP) measurement data may display inaccurate values representing liver steatosis. This could potentially lead to inappropriate clinica" Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:97726

  • recall_reason_pattern

    Software/algorithm-related recall in product code IYN (GE Medical Systems China Co., Ltd. Dev. Zone National Hi-Tech; No., initiated 2025-05-16): "GE HealthCare has become aware that the Estimated Fetal Weight (EFW) measurement data feature on the Versana Premier R3 and LOGIQ F R3 series ultrasound systems can display previou" Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:96992

  • recall_reason_pattern

    Software/algorithm-related recall in product code IYN (Siemens Medical Solutions USA, Inc., initiated 2024-08-15): "If ultrasound systems with software, are changed from factory default to : 1) Milliliters per second (ml/sec, mL/sec) or 2) Milliliters per minute (ml/min, mL/min); then systems wi" Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:95254

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/K251368