Content provided by the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences. Assessment of skeletal maturity is an important tool in managing human's growth problems, especially in assisting physicians decide the best treatment for various skeletal disorders. This task remains challenging when using machine learning method due to limited data and large anatomical variations among different subjects. Recently, researchers from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences and University of Hong Kong introduced an ensemble-based deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left-hand radiographs. The study was published in IEEE Transactions on Systems, Man, and Cybernetics: Systems. The researchers combined the dense connection mechanism with the ensemble model to improve the stability and accuracy of a skeletal maturity assessment system.