ReviewFrontiers in oncology2022
Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges.
Review in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.PloS one · 2026Pooled it
- A Calibrated Deep Learning Framework Integrating Spatial Annotations and Clinical Metadata for Safe Three-Class Bone Lesion Classification on Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Spine surgery for metastatic spine cancer in the era of advanced radiation therapy.Asian spine journal · 2026Article
- Artificial Intelligence and Machine Learning in Bone Metastasis Management: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Preoperative differentiation of primary vs. metastatic lumbar spine tumors: development and external validation of a multiparametric MRI-based radiomics nomogram.Frontiers in oncology · 2026Article
- Multimodal deep learning for bone tumor diagnosis with clinical imaging, pathology, and blood biomarkers.Journal of bone oncology · 2025Article
- Dual-center study on AI-driven multi-label deep learning for X-ray screening of knee abnormalities.Scientific reports · 2025Article
- Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.Journal of the Korean Society of Radiology · 2025Review
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Review
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- A novel perspective on bone tumors: advances in organoid research.Frontiers in pharmacology · 2025Review
- Deep learning-based automated tongue analysis system for assisted Chinese medicine diagnosis.Frontiers in physiology · 2025Article
- YOLOv8-Seg: a deep learning approach for accurate classification of osteoporotic vertebral fractures.Frontiers in radiology · 2025Article
- Deep bone oncology Diagnostics: Computed tomography based Machine learning for detection of bone tumors from breast cancer metastasis.Journal of bone oncology · 2024Article
- Advances in imaging modalities for spinal tumors.Neuro-oncology advances · 2024Review
- Semi-supervised recognition for artificial intelligence assisted pathology image diagnosis.Scientific reports · 2024Article
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown 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 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 for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods 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 the implementation of the deep learning method and future perspectives in this field were extensively discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.