ArticleMedicina (Kaunas, Lithuania)2022
Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms.
Article in Medicina (Kaunas, Lithuania), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- The implementation of artificial intelligence in upper extremity surgery: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Hybrid EfficientNet B4 and SVM framework for rapid and accurate bone cancer diagnosis from X-rays.Scientific reports · 2026Article
- Deep learning ensemble models for CT-based differentiation of malignant and benign sacral bone tumors: development and evaluation.Insights into imaging · 2026Article
- Multimodal contrastive learning for non-invasive chondroid bone tumor classification and grading using radiographs.BMC medical imaging · 2026Article
- Assessing an Automated Noncontrast CT-based Pipeline for Sacral Tumor Classification Using a Hip Bone Reference Frame.Radiology. Imaging cancer · 2026Article
- Enhancing bone cancer detection through optimized pre trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset.Scientific reports · 2025Article
- Current concepts in the management of proximal femoral osteochondromas: a narrative review.Journal of orthopaedic surgery and research · 2025Review
- Automated classification of chondroid tumor using 3D U-Net and radiomics with deep features.Scientific reports · 2025Article
- Comparison of Different Fusion Radiomics for Predicting Benign and Malignant Sacral Tumors: A Pilot Study.Journal of imaging informatics in medicine · 2024Article
- Artificial intelligence in diagnosing upper limb musculoskeletal disorders: a systematic review and meta-analysis of diagnostic tests.EFORT open reviews · 2024Article
- A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images.Scientific reports · 2024Article
- From the microspheres to scaffolds: advances in polymer microsphere scaffolds for bone regeneration applications.Biomaterials translational · 2024Review
- Automated Bone Tumor Segmentation and Classification as Benign or Malignant Using Computed Tomographic Imaging.Journal of digital imaging · 2023Article
- Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.Cancers · 2023Review
- An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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Identifiers
What OpenQuestion holds
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.