ArticleJournal of clinical medicine2023
Deep Learning-Based Detection of Bone Tumors around the Knee in X-rays of Children.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 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
- Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring.Advanced healthcare materials · 2026Review
- SHIELD: An AI Framework for Skeletal Health Intelligence and Early Lesion Detection to Improve Orthopedic Referrals.Journal of medical systems · 2026Article
- Hybrid EfficientNet B4 and SVM framework for rapid and accurate bone cancer diagnosis from X-rays.Scientific reports · 2026Article
- Barriers to implementing AI in pediatric cancer imaging.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Bone-CNN: A Lightweight Deep Learning Architecture for Multi-Class Classification of Primary Bone Tumours in Radiographs.Biomedicines · 2026Article
- Natural Products Targeting Key Molecular Hallmarks in Gastric Cancer: Focus on Apoptosis, Inflammation, and Chemoresistance.International journal of molecular sciences · 2026Review
- Dual-center study on AI-driven multi-label deep learning for X-ray screening of knee abnormalities.Scientific reports · 2025Article
- Generative artificial intelligence, large language models and ChatGPT in musculoskeletal Oncology: Current applications and future potential.Journal of clinical orthopaedics and trauma · 2025Article
- Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models.Bone & joint research · 2025Article
- The revolutionary impact of artificial intelligence in orthopedics: comprehensive review of current benefits and challenges.Journal of robotic surgery · 2025Review
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- Review
- A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors.Scientific data · 2025Article
- Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review.Cancer medicine · 2025Article
- Pediatric Bone Tumors: Location and Age Distribution of 420 Cases.Diagnostics (Basel, Switzerland) · 2024Article
- High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.Cancer science · 2024Article
- Impact of metadata in multimodal classification of bone tumours.BMC musculoskeletal disorders · 2024Article
- Deep learning-based detection of primary bone tumors around the knee joint on radiographs: a multicenter study.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study.Insights into imaging · 2024Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Even though tumors in children are rare, they cause the second most deaths under the age of 18 years. More often than in other age groups, underage patients suffer from malignancies of the bones, and these mostly occur in the area around the knee. One problem in the treatment is the early detection of bone tumors, especially on X-rays. The rarity and non-specific clinical symptoms further prolong the time to diagnosis. Nevertheless, an early diagnosis is crucial and can facilitate the treatment and therefore improve the prognosis of affected children. A new approach to evaluating X-ray images using artificial intelligence may facilitate the detection of suspicious lesions and, hence, accelerate the referral to a specialized center. We implemented a Vision Transformer model for image classification of healthy and pathological X-rays. To tackle the limited amount of data, we used a pretrained model and implemented extensive data augmentation. Discrete parameters were described by incidence and percentage ratio and continuous parameters by median, standard deviation and variance. For the evaluation of the model accuracy, sensitivity and specificity were computed. The two-entity classification of the healthy control group and the pathological group resulted in a cross-validated accuracy of 89.1%, a sensitivity of 82.2% and a specificity of 93.2% for test groups. Grad-CAMs were created to ensure the plausibility of the predictions. The proposed approach, using state-of-the-art deep learning methodology to detect bone tumors on knee X-rays of children has achieved very good results. With further improvement of the algorithm, enlargement of the dataset and removal of potential biases, this could become a useful additional tool, especially to support general practitioners for early, accurate and specific diagnosis of bone lesions in young patients.
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Registered trials
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