Optellum Virtual Nodule Clinic, Optellum software, Optellum platform
K202300Optellum Ltd · cleared 2021-03-05 · product code POK · Radiology
Premarket evidence — what FDA accepted
source quote (p.3)
“Virtual Nodule Clinic (VNC) is a software device used in the tracking, assessment and characterization of incidentally detected pulmonary nodules.”
source quote (p.11)
“CNN architecture: The LCP-CNN system is based on the Deep 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.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)
Standalone
n=300 cases
endpoints: measure performance of VNC's LCP-CNN model in discriminating between benign and malignant nodules; minimum area under curve (AUC) on a receiver operator characteristic (ROC) plot of 0.8 was selected
standards: 21 CFR §892.2060 special control 1(iv)
Reader study (MRMC)
n=300 patients · 9 site(s)
endpoints: compared the ability of the readers to discriminate between malignant and benign pulmonary nodules from CT images only, with and without the aid of the LCP-CNN; discrimination was measured using the area under the receiver operating characteristic curve (AUC) across all cases; effect size of the LCP-CNN intervention was measured as the difference in AUC before and after consulting the malignancy score provided by the LCP-CNN (i.e. unaided and aided reads); change of Likelihood of Malignancy (LoM) and recommended next management action; consistency of readers and sensitivity and specificity at 5% and 65% risk (ACCP thresholds) for blinded and unblinded evaluations
standards: 21 CFR §892.2060 special controls 1(ii) and 1(iii), ISO 14971:2019 Application of Risk Management to Medical Devices
Reported performance (4 observations)
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)”
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. LCP v1.0.0 0.867 CI=(0.811, 0.916) 150/300”
source quote (p.19)
“The mean effect size of the LCP-CNN intervention across all readers was 6.85 AUC points, 95%CI [4.29, 9.41], indicating a significant improvement in the discriminability of the readers (p < .001).”
source quote (p.20)
“Solo Clinician: AUC=81.9%”
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).