DeepCT

K182875

Deep01 Limited · cleared 2019-07-10 · product code QAS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“DeepCT (Ver. 4.1.4) is a software-only device that uses two components: (1) Image Forwarding Software and (2) Image Processing and Analysis Server.”
Algorithmdeep residual convolutional neural network (aka Residual Network or ResNet for short), deep neural networks, categorical cross-entropy loss with Adam optimizer, data augmentation
source quote (p.6)
“A deep residual convolutional neural network (aka Residual Network or ResNet for short, see Kaiming He et al. https://arxiv.org/abs/1512.03385) was adopted as the core learning model. By repeatedly applying residual connection, the ResNet model can ease the training of networks that are substantially deeper, effectively help the convergence of the model and gain the accuracy with deep neural networks. The model was trained with a categorical cross-entropy loss with Adam optimizer. Data augmentation was introduced to motivate the model to learn the rotated and translated images. Our DeepCT system was trained with PyTorch, an open source deep learning software library (https://pytorch.org).”
Adaptive (vs locked)No
PCCPNo
Cybersecurity addressedNo

Validation studies (1)

Retrospective clinical

n=260 cases · 5 site(s)

endpoints: evaluate the software's performance in identifying non-contrast CT head images containing ICH findings; DeepCT's processing time

Reported performance (2 observations)

sensitivity93.8CI 88.3%-96.8%
source quote (p.9)
“Specifically, sensitivity was observed to be 93.8% (95% CI: 88.3%-96.8%)”
specificity92.3CI 86.4%-95.7%
source quote (p.9)
“specificity was observed to be 92.3% (95% CI: 86.4%-95.7%).”

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/K182875