FDA 510(k) Search - By Innolitics (SaMD and AI Experts)

VNC includes a computer-aided diagnosis (CADx) function, available only to pulmonologists and radiologists. This automatically analyzes user-selected regions of interest (RO) within lung CT data to provide volumetric and computer analysis based on morphological characteristics. Using only imaging features extracted from the CT image data, an artificial intelligence algorithm calculates a single value, the LCP-CNN score, which is displayed to the user. The LCP-CNN score is analyzed relative to LCP-CNN scores generated on a database of cases with known ground-truth using a histogram display format. The LCP-CNN score may be useful in the characterization of pulmonary nodules during image interpretation and may be used as one input to clinical decision making when following published clinical guidelines.

VNC's LCP-CNN score is indicated for the evaluation of incidentally detected solid and semi-solid pulmonary nodules of diameter 5-30mm in patients aged 35 years or above. In cases where multiple abnormalities are present, VNC's LCP-CNN score can be used to assess each abnormality independently.

Note that LCP-CNN is not indicated for lung cancer screening nor is it indicated for nodules of pure ground glass opacity. In addition, high contrast CT images were not used in clinical validation (as measured as >300HU median attenuation in the aortic arch) and the validation data also excluded CT images with only calcified nodules (since these are typically considered to be benign), with implants, motion artifacts, or cases with greater than 5 nodules. Finally, the validation data excluded patients with history of cancer of less than 5 years to avoid the presence of metastatic lesions.

Users other than radiologists and pulmonologists, e.g. clinicians, nurse practitioners and navigators, may use VNC to view CT images and reports, organize patient management workflow, track patients, record management decisions and organize nodule clinics. For these users, the LCP-CNN score is unavailable.

Device Description

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.

Virtual Nodule Clinic (VNC) is a software application designed for trained medical professionals in the clinical management of patients with pulmonary nodules. VNC includes a computer-aided diagnosis (CADx) function to assist pulmonologists and radiologists in the assessment and characterization of incidentally detected pulmonary nodules using CT image data.

VNC has two main functions:

A CADx function to assist radiologists and pulmonologists in clinical decision making by providing a score using machine learning (namely the LCP-CNN algorithm, Lung Cancer Prediction Convolutional Network). Also note that the output of the CADx function is referred to synonymously in this document as either "Optellum LCP Score" or "LCP-CNN score", depending on context.
. A management function to allow users to easily track patients that need to be under management follow-up for indeterminate pulmonary nodules (IPNs).

VNC consists of two main software components:

. A web application deployed on a virtual server within a hospital datacenter;
The LCP-CNN module deployed on a GPU-equipped server on hospital premises or in the cloud.

VNC is connected to two other IT systems in the hospital: a DICOM-compatible Picture Archiving and Communication System (PACS) for accessing images and to the Radiology Information System (RIS) or reporting system for accessing the clinical reports.

AI/ML Overview

Acceptance Criteria and Study for Optellum™ Virtual Nodule Clinic

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria Category	Specific Criteria	Reported Device Performance
CADx Standalone Testing	Minimum Area Under Curve (AUC) on a Receiver Operating Characteristic (ROC) plot of 0.8.	AUC of 0.867 (95% CI [0.811, 0.916])
Clinical Performance (MRMC Study)	Primary Endpoint: Significant improvement in reader accuracy (AUC) when aided by LCP-CNN score versus unaided reads.	Mean improvement of 6.85 AUC points (95% CI [4.29, 9.41], p

Summary

Regulation Number and Section

§ 892.2060 Radiological computer-assisted diagnostic software for lesions suspicious of cancer.

(a)
Identification. A radiological computer-assisted diagnostic software for lesions suspicious of cancer is an image processing prescription device intended to aid in the characterization of lesions as suspicious for cancer identified on acquired medical images such as magnetic resonance, mammography, radiography, or computed tomography. The device characterizes lesions based on features or information extracted from the images and provides information about the lesion(s) to the user. Diagnostic and patient management decisions are made by the clinical user.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended.
(iii) Results from performance testing protocols that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Standalone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; and description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity).(2) Labeling must include:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the intended reading protocol.
(iii) A detailed description of the intended user and recommended user training.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations, including situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) Detailed instructions for use.
(viii) A detailed summary of the performance testing, including: Test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders (
e.g., lesion and organ characteristics, disease stages, and imaging equipment).