Harnessing the power of AI to Revolutionize Lung Cancer Detection and Management

BMVision: Lung enhances radiologists’ performance, boosting sensitivity in detecting, classifying, and quantifying lung nodules. This vastly improves interpretation speed for lung CTs, leading to improved clinical outcomes.

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For research use only.

Works with CT Scans

Functions with native and contrast-enhanced CT images

Provides High Sensitivity

Offers up to 91,6% sensitivity in lung nodule detection

Supports Clinical Workflows

Optimised for lung cancer screening and diagnostic imaging procedures

Backed up by science

Reliable – trained from a database of over a 1200 patients

Challenges

Lung cancer is often asymptomatic until advanced stages, making it one of the deadliest cancers.

Lung Cancer Prevalence and Mortality

Lung cancer, on par with breast cancer in prevalence, stands as the foremost cause of cancer-related deaths globally, responsible for approximately 20% of all cancer fatalities.

Inter-Reader Variability and Diagnostic Consistency

Inconsistent interpretations among readers contribute to misdiagnoses and discrepancies in lung cancer detection, posing significant challenges to accurate patient management and treatment planning.

Workforce Challenges in Lung Cancer Screening

Shortages and skill gaps within the workforce dedicated to lung cancer screening impedes the timely and effective implementation of screening programs, hindering efforts to detect lung cancer at its earliest, most treatable stages.

Demand for Early Lung Cancer Detection

There’s a pressing need for robust early detection methods for lung cancer to improve patient outcomes by facilitating timely intervention and treatment initiation, ultimately reducing mortality rates associated with this deadly disease.

Product Features

Empower your diagnosis with precision and speed via Automated Detection, Quantification, Monitoring, and Management of Lung Nodules

Product Feature 1

Automated lesion detection, classification and segmentation

A comprehensive system to detect, segment, and classify lung nodules and masses, including automated 3D lesion segmentation for nodules sized 3+ mm, improving diagnostic precision and patient care.

Product Feature 2

Automated Nodule Quantification

Automatically localises and quantifies lung nodules voxel-wise, generating three types of measurements for each detected nodule: volumetric, diametric in standard orthogonal planes, and mean diameter based on axial measurements, facilitating precise assessment of nodule characteristics.

Product Feature 3

Automated lung nodule tracking and monitoring

Automated nodule tracking across different CT studies. Dynamic monitoring rapidly estimates Nodule Growth and Volume Doubling Time (VDT) in follow-up CT scans.

Product Feature 4

Enables accurate and fast LUNG-RADS score assignment

Identify the most suspicious nodules according to LUNG-RADS 2022 criteria alongside clear, explainable recommendations for their management category, optimising decision-making in lung nodule assessment.