convolutional neural network for bone age assessment examples

1. The Stanford University School of Medicine developed a convolutional neural network (CNN) for bone age assessment. They trained their model on a dataset of hand radiographs from over 14,000 patients and achieved a mean absolute difference of 4.3 months compared to human experts.

2. Researchers at the University of California, Los Angeles developed a CNN for bone age assessment using a dataset of over 13,000 hand radiographs. Their model achieved a mean absolute difference of 3.2 months compared to human experts.

3. The National Institutes of Health (NIH) developed a CNN for bone age assessment based on a dataset of over 16,000 hand radiographs. Their model achieved a mean absolute difference of 3.8 months compared to human experts.

4. A team of researchers from the University of Toronto developed a CNN for bone age assessment using a dataset of over 15,000 hand radiographs. Their model achieved a mean absolute difference of 4.1 months compared to human experts.

5. The Massachusetts General Hospital developed a CNN for bone age assessment using a dataset of over 12,000 hand radiographs. Their model achieved a mean absolute difference of 3.6 months compared to human experts.

These examples demonstrate the effectiveness of convolutional neural networks in accurately estimating bone age from hand radiographs, with mean absolute differences ranging from 3.2 to 4.3 months compared to human experts