In this study, the authors developed a deep learning-based algorithm, which is able to mimic human judgment, in order to help clinicians assess the potential of spine growth based on EOS radiographs. The outcome of the study showed that their deep learning method achieved comparable, and even superior, results compared to those of clinicians, which should have positive applications in assisting busy clinicians. Key points In the clinic, there is no available computer-based method that can automatically assess spine growth potential. We developed a deep learning–based method that could automatically ascertain spine growth potential. Compared with the results of the clinicians, our algorithm got comparable results. Article: Deep learning–based identification of spine growth potential on EOS radiographs Authors: Lin-Zhen Xie, Xin-Yu Dou, Teng-Hui Ge, Xiao-Guang Han, Qi Zhang, Qi-Long Wang, Shuo Chen, Da He & Wei Tian
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
