PeekMed web

K240926

Peek Health, S.A. · cleared 2024-12-06 · product code LLZ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Being software it does not interact with any part of the body of the user and/or patient.”
AlgorithmML models for bone segmentation, landmarking, and classification
source quote (p.15)
“ML models incorporated into PeekMed web were also developed, trained, tested, and externally validated for their performance according to the internal procedures.”
Adaptive (vs locked)No
source quote (p.15)
“Specifically, the validation dataset was not a sampling of the development dataset, has never been used for the algorithm training or for tunning the algorithm, and leakage between development and validation data sets did not occur.”
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

n=367 cases

endpoints: DICE; HD-95; STD DICE; Precision; Recall; MRE; STD MRE; Accuracy; F1 score

Reported performance (8 observations)

diceas written: “DICE”stated without value
source quote (p.16)
“Comparison of the efficacy results of the Bone Segmentation ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.”
diceas written: “STD DICE”stated without value
source quote (p.16)
“Comparison of the efficacy results of the Bone Segmentation ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.”
ppvas written: “Precision (Segmentation)”stated without value
source quote (p.16)
“Comparison of the efficacy results of the Bone Segmentation ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.”
sensitivityas written: “Recall (Segmentation)”stated without value
source quote (p.16)
“Comparison of the efficacy results of the Bone Segmentation ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.”
accuracyas written: “Accuracy”stated without value
source quote (p.16)
“Comparison of the efficacy results of the Classification ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.”
ppvas written: “Precision (Classification)”stated without value
source quote (p.16)
“Comparison of the efficacy results of the Classification ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.”
sensitivityas written: “Recall (Classification)”stated without value
source quote (p.16)
“Comparison of the efficacy results of the Classification ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.”
f1as written: “F1 score”stated without value
source quote (p.16)
“Comparison of the efficacy results of the Classification ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.”

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

48
recalls in product code, 24mo
295
MAUDE reports in code, 12mo
+683%
vs code's own 3-yr baseline
30
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K252856 (decision 2025-12-22) from Peek Health, S.A. for a matching device line ("PeekMed web") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K252856

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K252452 (decision 2025-11-12) from Peek Health, S.A. for a matching device line ("PeekMed web") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K252452

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K251096 (decision 2025-07-14) from Peek Health, S.A. for a matching device line ("PeekMed web") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K251096

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K250042 (decision 2025-03-19) from Peek Health, S.A. for a matching device line ("PeekMed web") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K250042

  • adverse_event_inflection

    MAUDE adverse-event reports for product code LLZ: 295 in the 12 months ending 2026-06, vs a 37.7/12mo average over the prior 3 windows (+683%). Code-level count — reports are not attributed to this specific device.

    first seen 2026-07-08 · openFDA /device/event.json count=date_received product_code=LLZ

  • recall_reason_pattern

    Software/algorithm-related recall in product code LLZ (GE Medical Systems SCS, initiated 2026-05-08): "GE HealthCare has become aware of a context synchronization issue in AW Server 3.2 ext. 6.5. When a user selects a patient or exam in the AW Server Web Client worklist and launches" Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:99042

  • …and 24 more.

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/K240926