Vuno has signed a clinical research contract with Harvard Medical School Massachusetts General Hospital (MGH) to evaluate the performance of VUNO Med-LungCT AI, a computed tomography (CT) image reading assistance solution based on the artificial intelligence.
The device is a solution that automatically detects lung nodules in AI-based chest CT images and provides information, such as type, location, diameter and volume, important for clinical judgment.
After being proven in multi-center clinical trials, the device obtained approval from the Ministry of Food and Drug Safety in April 2020, and the number of cases introduced by foreign institutions is steadily increasing based on the CE of the EU and Japan Pharmaceuticals and Medical Devices Agency (PMDA).
VUNO will establish a system of cooperation with MGH and use foreign data to prove the overseas clinical efficacy of VUNO Med-LungCT AI.
The company also plans to lay the foundation for clinical trials for overseas licensing by advancing and optimizing products based on feedback from international healthcare professionals and performance evaluation results.
VUNO said the primary objective of the study was to verify VUNO Med-LungCT AI’s lung nodule detection system and malignancy determination system, a new feature for the platform.
The company plans to conduct more rigorous and systematic clinical verification through data and medical workers from US hospitals and upgrade it to a solution optimized for local clinical needs based on the results.
“Through collaborative research with MGH, we plan to accumulate evidence for the clinical effectiveness of VUNO Med-LungCT AI based on US data and local medical personnel, and lay the foundation for market expansion. in North America in addition to Europe, Japan and Asia,” said VUNO CTO Jung Kyu-hwan.
Jung added that the company will provide optimized solutions for the clinical environment of more radiologists around the world based on product advancement by providing malignancy assessment information and optimizing the model. detection of pulmonary nodules.