Optellum Virtual Nodule Clinic, Optellum software, Optellum platform

K202300

Optellum Ltd · cleared 2021-03-05 · product code POK · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“VNC is a software only device which consists of two main components: a web application accessed via standard desktop web browsers and the LCP-CNN machine learning model.”
AlgorithmDense Convolutional Network, a widely used type of deep learning CNN architecture that was designed for computer vision tasks. LCP-CNN's ensemble of convolutional neural network models ends with a fully connected binary classification layer (malignant or benign).
source quote (p.11)
“The LCP-CNN system is based on the Dense Convolutional Network, a widely used type of deep learning CNN architecture that was designed for computer vision tasks. LCP-CNN's ensemble of convolutional neural network models ends with a fully connected binary classification layer (malignant or benign).”
Adaptive (vs locked)No
source quote (p.11)
“LCP-CNN's output is a continuous value between 0 and 1; this is mapped to an integer score between 1 and 10. This mapping was constructed by computing the raw LCP score on a dataset consisting of malignant (10%) and benign nodules (90%) but which were not used during the model training.”
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.14)
“Cybersecurity activities were performed using FDA's Guidance for Content of Premarket Submissions for Management of Cybersecurity in Medical Devices (2014).”

Validation studies (2)

Bench

n=300 cases

standards: 21 CFR §892.2060 special control 1(iv)

Reader study (MRMC)

n=300 patients · 9 site(s)

endpoints: ability of the readers to discriminate between malignant and benign pulmonary nodules; area under the receiver operating characteristic curve (AUC); net reclassification improvement (NRI); sensitivity; specificity; change of Likelihood of Malignancy (LoM); recommended next management action; consistency of readers

standards: 21 CFR §892.2060 special controls 1(ii) and 1(iii)

Reported performance (2 observations)

aurocas written: “auc”0.888
source quote (p.21)
“Concurrent use of the LCP-CNN feature in Optellum Virtual Nodule Clinic software to read CT exams improves radiologists' and pulmonologists' accuracy for the diagnosis of pulmonary nodules by an average of 6.85 AUC points (p < .001) (from 81.9 to 88.8 AUC)”
aurocas written: “AUC (standalone testing)”0.867CI (0.811, 0.916)
source quote (p.14)
“The LCP-CNN model achieved an AUC of 0.867, meaning that the LCP-CNN model is performing as expected and therefore accepted as the model to be incorporated into the device for further testing. See Figure 3.”

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/K202300