Locating small lung tumors located deep within the lungs remains challenging, as they need to be navigated through a complex network of tiny airways. Existing AI systems trained on incomplete human annotations may also fail to identify peripheral airways that were omitted from the training labels. Researchers from Pusan National University developed a novel AI framework, ASTRA-Net, which helps recover previously overlooked peripheral airways—potentially enabling more accurate bronchoscopy maps for better lung cancer diagnosis.