contextflow develops AI imaging technology for chest CT scans, providing radiologists with tools for nodule detection, quantification, and tracking, as well as lung tissue analysis. This technology enhances diagnostic accuracy and efficiency by reducing false positives and streamlining the reporting process for lung cancer, interstitial lung disease, and chronic obstructive pulmonary disease cases.
Funding
$10.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Radiologists face challenges in accurately and efficiently evaluating chest CT scans for lung cancer, interstitial lung disease (ILD), and chronic obstructive pulmonary disease (COPD). The process can be time-consuming and complex, potentially leading to missed or delayed diagnoses.
Solution
contextflow ADVANCE Chest CT is a computer-aided detection software integrated directly into the PACS viewer, providing radiologists with comprehensive support for suspected lung cancer, ILD, and COPD cases. The software offers quantitative and qualitative insights, including nodule detection and quantification, nodule tracking over time, and lung tissue analysis for key image findings. By reducing false positives and streamlining the reporting process, contextflow enhances diagnostic accuracy and efficiency, enabling radiologists to objectively report on cases and improve patient outcomes.
Target Audience
The primary target audience includes radiologists, PACS administrators, and pharmaceutical companies seeking comprehensive chest CT insights and quantitative profiling of patients.
Features
- Nodule detection and quantification (4-30mm diameter) with nodule classification (solid, part-solid, non-solid)
- Nodule tracking across multiple prior scans, indicating growth percentage and volume doubling time
- Lung tissue analysis with anomaly heatmaps and quantification of total lung volume affected by disease patterns
- Quantification and individual heatmaps for key image findings: consolidation, effusion, emphysema, ground-glass opacity, honeycombing, pneumothorax, and reticular pattern
- 3D image search for qualitative analysis of 19 image patterns with links to differential diagnosis literature and retrieval of similar cases
- Nodule Malignancy Scoring (MSI) compares selected nodules against a clinical database of nodules with known outcomes to assist clinicians’ assessment of patients’ cancer risk