SugarBug (1.x)

K250264

Bench7, Inc. · cleared 2025-11-07 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“SugarBug is a software as a medical device (SaMD) that uses machine learning to label features that the reader should examine for evidence of decay.”
Algorithmconvolutional neural network to perform a semantic segmentation task
source quote (p.5)
“SugarBug uses convolutional neural network to perform a semantic segmentation task. The algorithm goes through every pixel in an image and assigns a probability value to it for the possibility that it contains decay. A threshold is used to determine which pixels are labeled in the device's output.”
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)

Bench

n=400 images

endpoints: lesion-level sensitivity; mean FPPI; DICE coefficient versus ground truth

Reader study (MRMC)

n=300 images

endpoints: weighted Alternative Free-response Receiver Operating Characteristic (wAFROC) area under the curve (AUC); reader changes in sensitivity; specificity; annotation quality (DICE scores); standalone model performance

Reported performance (5 observations)

sensitivity0.674CI 0.615, 0.728
source quote (p.8)
“Aided readers' lesion-level mean sensitivity was 0.674 (0.615, 0.728) while that of unaided readers was 0.540 (0.445, 0.621).”
aurocas written: “auc”0.725CI 0.683, 0.767
source quote (p.8)
“The mean unaided reader wAFROC-AUC was 0.659 (0.611,0.707) while the mean aided reader wAFROC-AUC was 0.725 (0.683, 0.767).”
diceas written: “Mean DICE scores (aided readings)”0.74CI 0.733, 0.747
source quote (p.8)
“Mean DICE scores (lesion annotation similarity relative to ground truth) were 0.695 (0.688, 0.702) for unaided readings and 0.740 (0.733, 0.747) for aided readings, resulting in a mean difference of 0.045 (0.035,0.055).”
sensitivityas written: “lesion-level sensitivity (standalone)”0.686CI 0.655, 0.717
source quote (p.7)
“SugarBug's lesion-level sensitivity and mean FPPI were 0.686 (0.655, 0.717) and 0.231 (0.111, 0.303), respectively.”
diceas written: “DICE coefficient versus ground truth (standalone)”0.746CI 0.724, 0.768
source quote (p.7)
“The DICE coefficient versus ground truth was 0.746 (0.724, 0.768).”

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
-100%
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/K250264