V8 Diagnostic Ultrasound System; cV8 Diagnostic Ultrasound System; V7 Diagnostic Ultrasound System; cV7 Diagnostic Ultrasound System; V6 Diagnostic Ultrasound System; cV6 Diagnostic Ultrasound System; V5 Diagnostic Ultrasound System; cV5 Diagnostic Ultrasound System; V4 Diagnostic Ultrasound System; cV4 Diagnostic Ultrasound System

K250999

Samsung Medison Co., Ltd. · cleared 2025-07-18 · product code IYN · Radiology

Premarket evidence — what FDA accepted

Device typehardware with ml
source quote (p.6)
The V8/cV8, V7/cV7, V6/cV6 Diagnostic Ultrasound Systems are general purpose, mobile, software controlled, diagnostic ultrasound systems. Its function is to acquire ultrasound data and to display the data as 2D mode, Color Doppler mode, Power Doppler (PD) mode, M mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, ElastoScan Mode, Combined modes, Multi-Image mode(Dual, Quad), 3D/4D mode. The V8/cV8, V7/cV7, V6/cV6 also give the operator the ability to measure anatomical structures and offers analysis packages that provide information that is used to make a diagnosis by competent health care professionals. The V8/cV8, V7/cV7, V6/cV6 have real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed. ... The proposed_V8/cV8, V7/cV7,V6/cV6 have updated 'BiometryAssist', 'ViewAssist', and 'HeartAssist', the cleared features in the predicate V8/cV8, V7/cV7,V6/cV6 (K243702). The AI models for these features have been updated.
Algorithmdeep learning based view recognition algorithm; deep learning-based segmentation algorithm
source quote (p.10)
A deep learning based view recognition algorithm was validated using 320 fetal biometry images collected at hospitals (South Korea and United States). ... A deep learning-based segmentation algorithm was validated using 200 median nerve images collected at a hospital.
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (4)

Retrospective clinical

n=320 images · 2 site(s)

endpoints: Segmentation test (average dice-score); Size measurement test (error rate of circumference measured value); Size measurement test (error rate of distance measured value); Size measurement test (error rate of NT measured value)

standards: ISO 14971:2019, NEMA UD 2-2004 (R2009)

Retrospective clinical

n=680 images · 2 site(s)

endpoints: View recognition test (sensitivity, specificity); Segmentation test (average dice-score)

standards: ISO 14971:2019, NEMA UD 2-2004 (R2009)

Retrospective clinical

n=280 images · 2 site(s)

endpoints: View recognition test (sensitivity, specificity); Segmentation test (average dice-score); Size measurement test (error rate of area measured value); Size measurement test (error rate of angle measured value); Size measurement test (error rate of circumference measured value); Size measurement test (error rate of diameter measured value)

standards: ISO 14971:2019, NEMA UD 2-2004 (R2009)

Retrospective clinical

n=200 images · 2 site(s)

endpoints: flattening ratio (FR) error rate; cross-sectional area (CSA) error rate

standards: ISO 14971:2019, NEMA UD 2-2004 (R2009)

Reported performance (5 observations)

sensitivity93.97
source quote (p.11)
The achieved sensitivity is 93.97% and specificity is 99.62% (thresholds: 75.9%, 88.2%, respectively)
specificity99.62
source quote (p.11)
The achieved sensitivity is 93.97% and specificity is 99.62% (thresholds: 75.9%, 88.2%, respectively)
diceas written: “average dice-score0.869
source quote (p.10)
The average dice-score is 0.869 (threshold 0.8)
diceas written: “average dice-score0.863
source quote (p.11)
The average dice-score is 0.863 (threshold 0.8)
diceas written: “average dice-score0.865
source quote (p.13)
The average dice-score is 0.865 (threshold 0.8)

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). 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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K250999