ProstatID

K212783

ScanMed, LLC · cleared 2022-07-08 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“ProstatIDTM is a radiological computer assisted detection (CADe) and diagnostic (CADx) software-only device for use in a healthcare facility or hospital...The software is not installed on the user's MRI system, PACS system, workstation, or any device other than the cloud-based servers configured as a Software as a Service (SaaS) model.”
AlgorithmDeep learning and Random Forest algorithms are applied. Algorithms are trained with a large database of biopsy-proven examples of normal, benign, and cancerous tissues. The detection algorithm uses Random Forest.
source quote (p.5)
“Deep learning and Random Forest algorithms are applied to the DICOM image set of MRI Axial Images (T2W, DWI, and ADC) of the prostate for recognition of the prostate gland, its central gland, and recognition and classifying the likelihood of malignancy of any suspicious lesions. Algorithms are trained with a large database of biopsy-proven examples of normal, benign, and cancerous tissues.ProstatID was trained on a database with reference normal tissues and abnormalities with known ground truths; however, the detection algorithm uses Random Forest vs. Neural Nets”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Standalone

n=150 cases

endpoints: Diagnostic Accuracy (Lesion-based ROC Analysis); Standalone Detection Performance (FROC Analysis); Detection Performance (AFROC Analysis): CAD vs. Readers

standards: FDA's Guidance for Industry and FDA Staff, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices," dated May 11, 2005., FDA's Guidance for Industry and FDA Staff, "Computer Assisted Detection Devices Applied to Radiology Images and Radiology Device Data – Premarket Notification [510(k)] Submissions,” dated July 3, 2012.

Reader study (MRMC)

n=150 patients

endpoints: expected difference in the AUC of the ROC curve between the first read (without ProstatID) and the second read (with ProstatID)

Reported performance (7 observations)

sensitivity0.8
source quote (p.10)
“ProstatID demonstrated a detection performance with a sensitivity of 80% at a rate of one false positive per patient, increasing to 98% at the rate of 3 false positives per patient.”
aurocas written: “auc”0.71
source quote (p.10)
“The standalone ROC performance of ProstatID yielded an AUC of 0.710, showing that ProstatID has good performance on its own.”
sensitivityas written: “Standalone Detection Performance Sensitivity at 3 false positives per patient”0.98
source quote (p.10)
“ProstatID demonstrated a detection performance with a sensitivity of 80% at a rate of one false positive per patient, increasing to 98% at the rate of 3 false positives per patient.”
aurocas written: “Reader-averaged AUC without ProstatID (MRMC experiment)”0.629CI [0.549, 0.711]
source quote (p.10)
“The reader-averaged AUC from the MRMC experiment was comparatively 0.629 when not using ProstatID.”
aurocas written: “Reader-averaged AUC with ProstatID (MRMC experiment)”0.671CI [0.590, 0.752]
source quote (p.11)
“AUC2nd Read (with CAD) 0.671 [0.590, 0.752]”
accuracyas written: “Model accuracy for Reduction of Unnecessary Biopsies”0.843
source quote (p.13)
“Results showed that the model accuracy was 84.3%.”
accuracyas written: “Accuracy for Correlation of Age and PSA to PCa”0.607
source quote (p.13)
“The logistic regression model was not a good predictor of outcome (accuracy 60.7%).”

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

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

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/K212783