ProstatID
K212783ScanMed, LLC · cleared 2022-07-08 · product code QDQ · Radiology
Premarket evidence — what FDA accepted
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 to assist trained radiologists in the detection, assessment, and characterization of lesions suspicious for cancer using MR image data. 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.”
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”
Validation studies (2)
Standalone
n=150 cases
endpoints: detection and diagnosis or classification of the probability of cancer by comparing the algorithm output to the ground truth biopsy data.
Retrospective clinical
n=150 patients
endpoints: the expected difference in the AUC of the ROC curve between the first read (without ProstatID) and the second read (with ProstatID).
Reported performance (3 observations)
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.”
source quote (p.11)
“AUC2nd Read (with CAD) 0.671 [0.590, 0.752]”
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.”
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
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).