The aim of this study was to compare the performance of a deep learning (DL)-based method used for diagnosing pulmonary nodules compared with the diagnostic approach of the radiologist in computed tomography (CT) of the chest. The authors included a total of 150 pathologically confirmed pulmonary nodules that were assessed and reported by radiologists. The study found that the DL-based method was able to achieve an accuracy that was comparable with that of the radiologists’ diagnostic approach in clinical practice. Key points Deep learning-based method for diagnosing the pulmonary nodules in computed tomography provides a higher diagnostic certainty. Article: Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest Authors: Ya-Wen Wang, Jian-Wei Wang, Shou-Xin Yang, Lin-Lin Qi, Hao-Liang Lin, Zhen Zhou & Yi-Zhou Yu
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
