At NodeAI, we are transforming Lung Cancer staging with AI-Powered Solution NodeAIPredict and NodeAIDetect, two groundbreaking AI-driven solutions designed to enhance the accuracy and efficiency of endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA). Our mission is to empower clinicians with real-time, data-driven insights to improve lung cancer staging, optimize treatment decisions, and ultimately save lives.

Currently available for use in clinical research, NodeAIPredict is our advanced AI-powered malignancy prediction tool, trained on thousands of EBUS-TBNA images to provide real-time insights during procedures. By leveraging a validated AI algorithm built upon 10 years of published research, NodeAIPredict helps clinicians determine the likelihood of lymph node malignancy at the bedside.

NodeAIPredict is currently undergoing rigorous clinical trials to demonstrate its clinical utility and impact, with results showing its potential to significantly improve lung cancer staging accuracy and reduce the need for repeat biopsies.

NodeAIDetect, our next-generation solution currently under development, is designed to address another critical challenge in EBUS-TBNA: accurately locating and identifying specific lymph node stations. While current procedures heavily rely on operator expertise, NodeAIDetect aims to democratize access to high-quality staging by providing real-time AI guidance. 

Our vision is to combine NodeAIPredict and NodeAIDetect into a single, comprehensive AI-powered platform that provides an end-to-end solution for EBUS-TBNA procedures. By integrating malignancy prediction with real-time node detection and station identification, our unified solution will:

• Maximize procedural accuracy: Ensuring clinicians sample the right lymph nodes with the highest risk of malignancy.
• Standardize lung cancer staging: Reducing variability across operators and institutions.
• Optimize workflow efficiency: Delivering automated reporting and decision support to streamline the diagnostic process.
• Improve patient outcomes: Enabling faster, more accurate diagnoses and treatment planning.

This unified approach represents the future of AI-assisted lung cancer staging—one that empowers clinicians at all experience levels to make confident, informed decisions.