Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges - Zhou, Xiaowen; Wang, Hua; Feng, Chengyao; Xu, Ruilin; He, Yu; Li, Lan; Tu, Chao (Frontiers in Oncology, 2022)
Deep learning is a subfield state-of-the-art artificial intelligence (AI) technology and multiple deep learning-based AI models have been applied in musculoskeletal diseases. Deep learning has showed the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated a good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential in diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we firstly summarized the workflows of deep learning method in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in implementation of deep learning method and future perspective in this field were extensively discussed.