PowerLook Tomo Detection V2 Software

K182373

iCAD Inc. · cleared 2018-12-06 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“PowerLook® Tomo Detection V2 Software is a computer-assisted detection and diagnosis (CAD) software device intended to be used concurrently by interpreting physicians while reading digital breast tomosynthesis (DBT) exams from compatible DBT systems.”
Algorithmdeep learning technology to process feature computations and uses pattern recognition
source quote (p.5)
“The PowerLook Tomo Detection V2 algorithm uses deep learning technology to process feature computations and uses pattern recognition to identify suspicious breast lesions appearing as soft tissue densities or clusters of calcifications.”
Adaptive (vs locked)No
source quote (p.6)
“Certainty of Finding and Case Scores are not calibrated to the prevalence in the intended use population or to the prevalence in the pivotal reader study outlined in the Assessment of Clinical Performance Data section, and consequently, the Certainty of Finding and Case Scores are in general higher than the actual probability of malignancy in an intended use population with less than 50% prevalence. These scores represent a relative level of concern or level of suspicion because they do not represent an absolute clinical probability of malignancy.”
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (3)

Standalone

n=655 cases

endpoints: Case-Level Sensitivity; Lesion-Level Sensitivity; FP Rate in Non-Cancer Cases; Specificity

Standalone

n=610 cases

endpoints: Case-Level Sensitivity; Lesion-Level Sensitivity; FP Rate in Non-Cancer Cases; Specificity

Reader study (MRMC)

n=260 cases

endpoints: radiologist performance when using CAD with DBT images is non-inferior to radiologist performance when using DBT images without CAD; radiologist reading time when using CAD with DBT images is superior to (shorter than) radiologist reading time when using DBT images without CAD; Superiority of case-level AUC; Non-inferiority (with non-inferiority margin delta = 0.05) of sensitivity at the case level; Superiority of sensitivity at the case level; Non-inferiority (with non-inferiority margin delta = 0.05) of sensitivity at the lesion level; Superiority of sensitivity at the lesion level; Non-inferiority (with non-inferiority margin delta = 0.05) of specificity (case-level); Non-inferiority (with non-inferiority margin delta = 0.05) of recall rate in non-cancers (case-level)

Reported performance (5 observations)

sensitivity0.85
source quote (p.11)
“Average case-level sensitivity was 0.770 without CAD and 0.850 with CAD.”
specificity0.696
source quote (p.11)
“Specificity was 0.627 without CAD and 0.696 with CAD, for an average increase of 0.069 (95% CI: 0.030, 0.108; non-inferiority p < 0.01 for non-inferiority margin delta = 0.05).”
aurocas written: “auc”0.852
source quote (p.11)
“Radiologists had superior per-subject average area under the receiver operating characteristic (ROC) curve (AUC) with CAD, 0.852, versus without CAD, 0.795. The average difference in AUC was 0.057 (95% CI: 0.028, 0.087; non-inferiority p < 0.01 for non-inferiority margin delta = 0.05, and p < 0.01 for test of difference).”
sensitivityas written: “Lesion-level sensitivity (with CAD)”0.853
source quote (p.11)
“Average per-lesion sensitivity across readers increased by 0.084 (95% CI: 0.029, 0.139; non-inferiority p < 0.01 for non-inferiority margin delta = 0.05, and p < 0.01 for test of difference), from 0.769 without CAD to 0.853 with CAD.”
sensitivityas written: “Recall rate in non-cancer cases (with CAD)”0.309
source quote (p.11)
“Average recall rate in non-cancer cases was 0.380 without CAD and 0.309 with CAD, for an average reduction of 0.072 (95% CI: 0.031, 0.112; non-inferiority p < 0.01 for non-inferiority margin delta = 0.05).”

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/K182373