V5 Diagnostic Ultrasound System, H5 Diagnostic Ultrasound System, XV5 Diagnostic Ultrasound System, XH5 Diagnostic Ultrasound System, V4 Diagnostic Ultrasound System, H4 Diagnostic Ultrasound System, XV4 Diagnostic Ultrasound System, XH4 Diagnostic Ultrasound System

K242511

Samsung Medison Co., Ltd. · cleared 2024-12-10 · product code IYN · Radiology

Premarket evidence — what FDA accepted

Device typehardware with ml
source quote (p.6)
“V5 Diagnostic Ultrasound System; H5 Diagnostic Ultrasound System; XV5 Diagnostic Ultrasound System; XH5 Diagnostic Ultrasound System; V4 Diagnostic Ultrasound System; H4 Diagnostic Ultrasound System; XV4 Diagnostic Ultrasound System; XH4 Diagnostic Ultrasound System (*Hereinafter referred to_as_V5/H5/XV5/XH5,V4/H4/XV4/XH4 diagnostic ultrasound system) are a general purpose, mobile, software controlled, diagnostic ultrasound system.”
AlgorithmDeep learning based view recognition, segmentation, and detection algorithms are used for various assist functions (HeartAssist, BiometryAssist, ViewAssist, UterineAssist, NerveTrack).
source quote (p.8)
“A deep learning based view recognition algorithm was validated using 280 fetal heart and 540 adult heart images collected at five hospitals. A deep learning based segmentation algorithm was validated using 320 fetal biometry images collected at two hospitals. A deep learning based view recognition algorithm was validated using 680 fetal ultrasound images and fetal biometry images collected at two hospitals. A deep learning based segmentation algorithm was validated using 450 sagittal uterus images and 150 transverse uterus images collected at three hospitals. A deep learning-based detection algorithm was validated using a total of 3,999 images collected at eight hospitals. A deep learning based segmentation algorithm was validated using 1,675 nerve images collected at ten hospitals.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (12)

Retrospective clinical

n=820 images · 5 site(s)

endpoints: average recognition accuracy

Retrospective clinical

n=820 images · 5 site(s)

endpoints: average dice-score

Retrospective clinical

n=820 images · 5 site(s)

endpoints: error rate of area measured value; error rate of angle measured value; error rate of circumference measured value; error rate of diameter measured value

Retrospective clinical

n=320 images · 2 site(s)

endpoints: average dice-score

Retrospective clinical

n=320 images · 2 site(s)

endpoints: error rate of circumference measured value; error rate of distance measured value; error rate of NT measured value

Retrospective clinical

n=680 images · 2 site(s)

endpoints: average recognition accuracy

Retrospective clinical

n=680 images · 2 site(s)

endpoints: average dice-score

Retrospective clinical

n=600 images · 3 site(s)

endpoints: average dice-score of uterus; average dice-score of endometrium

Retrospective clinical

n=92 images · 3 site(s)

endpoints: errors of uterus feature points; errors of endometrium feature points

Retrospective clinical

n=92 images · 3 site(s)

endpoints: errors of Measurements performance

Retrospective clinical

n=3,999 images · 8 site(s)

endpoints: average accuracy; average speed (fps)

Retrospective clinical

n=1,675 images · 10 site(s)

endpoints: average accuracy; average speed (fps)

Reported performance (12 observations)

accuracyas written: “average recognition accuracy (Fetus)”93.21
source quote (p.8)
“(Fetus) The average recognition accuracy is 93.21% (threshold 89%)”
accuracyas written: “average recognition accuracy (Adult)”98.31
source quote (p.8)
“(Adult) The average recognition accuracy is 98.31% (threshold 84%)”
diceas written: “average dice-score (Fetus)”0.88
source quote (p.8)
“(Fetus) The average dice-score is 0.88 (threshold 0.8)”
diceas written: “average dice-score (Adult)”0.93
source quote (p.8)
“(Adult) The average dice-score is 0.93 (threshold 0.9)”
diceas written: “average dice-score (BiometryAssist Segmentation)”0.919
source quote (p.10)
“The average dice-score is 0.919 (threshold 0.8)”
accuracyas written: “average recognition accuracy (ViewAssist View Recognition)”94.26
source quote (p.12)
“The average recognition accuracy is 94.26% (threshold 89%)”
diceas written: “average dice-score (ViewAssist Anatomy Annotation)”0.885
source quote (p.12)
“The average dice-score is 0.885 (threshold 0.8)”
diceas written: “average dice-score of uterus (UterineAssist Segmentation)”0.96
source quote (p.13)
“The average dice-score of uterus is 96%”
diceas written: “average dice-score of endometrium (UterineAssist Segmentation)”0.92
source quote (p.13)
“The average dice-score of endometrium is 92%”
f1as written: “errors of Measurements performance (UterineAssist Size Measurement)”2
source quote (p.13)
“The errors of Measurements performance are 2.0 mm or less”
accuracyas written: “average accuracy (NerveTrack Detection)”89.91CI 86.51, 93.35
source quote (p.15)
“The average accuracy from 10 image sequence was 89.91% (95% Confidence Interval: 86.51, 93.35)”
accuracyas written: “average accuracy (NerveTrack Segmentation)”98.3CI 95.43, 100
source quote (p.16)
“The average accuracy from nine image sequences is 98.30% (95% Confidence Interval: 95.43, 100)”

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/K242511