ReviewDiagnostics (Basel, Switzerland)2020
Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions.
Review in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
21 citing papers in PubMed, 82 citations in OpenAlex.
- Artificial intelligence algorithm improves radiologists' bone age assessment accuracy.Journal of the Chinese Medical Association : JCMA · 2025Trial
- Article
- Research hotspots and trends of pediatric bone age: A bibliometric and visualization analysis.Lasers in medical science · 2025Article
- Automatic bone age assessment: a Turkish population study.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.Journal of the Korean Society of Radiology · 2025Review
- Automatic skeletal maturity grading from pelvis radiographs by deep learning for adolescent idiopathic scoliosis.Medical & biological engineering & computing · 2025Review
- Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation.International journal of legal medicine · 2025Review
- Automatic Evaluation of Bone Age Using Hand Radiographs and Pancorporal Radiographs in Adolescent Idiopathic Scoliosis.Diagnostics (Basel, Switzerland) · 2025Article
- Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Review
- A new design of wind power prediction method based on multi-interaction optimization informer model.PloS one · 2025Article
- Clinical decision-making in bone cancer care management and forecast of ICU needs based on computed tomography.Journal of bone oncology · 2024Article
- Radiographic imaging and diagnosis of spinal bone tumors: AlexNet and ResNet for the classification of tumor malignancy.Journal of bone oncology · 2024Article
- Deep Learning-Assisted Diffusion Tensor Imaging for Evaluation of the Physis and Metaphysis.Journal of imaging informatics in medicine · 2024Article
- Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children.BMC pediatrics · 2022Article
- Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2022Review
- Deep Learning-Enabled Technologies for Bioimage Analysis.Micromachines · 2022Review
- How Artificial Intelligence and Machine Learning Is Assisting Us to Extract Meaning from Data on Bone Mechanics?Advances in experimental medicine and biology · 2022Review
- A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes.Healthcare (Basel, Switzerland) · 2021Article
- Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning.Diagnostics (Basel, Switzerland) · 2021Article
- Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is a quite useful and proliferating technique of machine learning. Various applications, such as medical images analysis, medical images processing, text understanding, and speech recognition, have been using deep learning, and it has been providing rather promising results. Both supervised and unsupervised approaches are being used to extract and learn features as well as for the multi-level representation of pattern recognition and classification. Hence, the way of prediction, recognition, and diagnosis in various domains of healthcare including the abdomen, lung cancer, brain tumor, skeletal bone age assessment, and so on, have been transformed and improved significantly by deep learning. By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation, prediction, and classification). An enormous number of scientific research publications related to bone age assessment using deep learning are explored, studied, and presented in this survey. Furthermore, the emerging trends of this research domain have been analyzed and discussed. Finally, a critical discussion section on the limitations of deep-learning models has been presented. Open research challenges and future directions in this promising area have been included as well.
Indexed as
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.