AIMC Topic: Radiography

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Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels.

Korean journal of radiology
OBJECTIVE: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model.

Bone Age Estimation and Prediction of Final Adult Height Using Deep Learning.

Yonsei medical journal
PURPOSE: The appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to...

Early Experiences of Integrating an Artificial Intelligence-Based Diagnostic Decision Support System into Radiology Settings: A Qualitative Study.

Studies in health technology and informatics
Artificial Intelligence (AI) based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practic...

Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers.

Radiology
Background Clinicians consider both imaging and nonimaging data when diagnosing diseases; however, current machine learning approaches primarily consider data from a single modality. Purpose To develop a neural network architecture capable of integra...

Artificial intelligence in osteoarthritis: repair by knee joint distraction shows association of pain, radiographic and immunological outcomes.

Rheumatology (Oxford, England)
OBJECTIVES: Knee joint distraction (KJD) has been associated with clinical and structural improvement and SF marker changes. The current objective was to analyse radiographic changes after KJD using an automatic artificial intelligence-based measurem...

An automatic cephalometric landmark detection method based on heatmap regression and Monte Carlo dropout.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cephalometric analysis plays an important role in orthodontic diagnosis and treatment planning. It depends on the detection of multiple landmarks, while the process is time-consuming and tedious. Although some deep learning-based automatic landmark d...

Domain Adaptation and Feature Fusion for the Detection of Abnormalities in X-Ray Forearm Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The main challenge in adopting deep learning models is limited data for training, which can lead to poor generalization and a high risk of overfitting, particularly when detecting forearm abnormalities in X-ray images. Transfer learning from ImageNet...

Deep Learning Segmentation of Lower Extremities Radiographs for an Automatic Leg Length Discrepancy Measurement.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Leg length measurement is relevant for the early diagnostic and treatment of discrepancies as they are related with orthopedic and biomechanical changes. Simple radiology constitutes the gold standard on which radiologists perform manual lower limb m...