Artificial Intelligence for Nodule Detection in Low-Dose CT Scans for Lung Cancer Screening: A Systematic Review

AI technologies substantially guide the screening processes for lung cancer via imaging. This frequently associates with comprehensive health strategies like early intervention choices. Complications such as overlooked lesions or processing delays might impair diagnostic reliability. These hurdles contribute to fluctuating screening effectiveness. Typical radiology protocols occasionally bypass AI tools and stress human oversight rather than computational help. Latest inquiries reveal that machine learning models heighten sensitivity and speed. This assembly draws from active explorations and clinical reports. It probes evolutions in detection algorithms, image enhancement, and workflow integrations. Those have uplifted identification rates and minimized false positives with reduced need for expert reviews. Featuring instances from multiple screening programs, the outline presents actual improvements. Insights propose these techniques enable consistent early detections and streamlined patient pathways.

کلیدواژه ها:

machine learning models ، pulmonary nodule identification ، thoracic imaging ، diagnostic