Research Progress and Prospect of Machine Learning in Bone Age Assessment.

Area of Science:

• Forensic Science
• Medical Imaging
• Computer Vision

Background:

• Bone age assessment is crucial but challenging in forensic science.
• Machine learning (ML) has shown significant promise in various medical fields, including image recognition.
• The computer vision nature of medical imaging makes it suitable for ML applications.

Purpose of the Study:

• To review recent progress in applying machine learning to bone age assessment.
• To explore the development of ML-based recognition models for forensic skeletal images.

Main Methods:

• Reviewing scholarly research on machine learning applications in bone age assessment.
• Analyzing the integration of ML techniques with forensic skeletal image analysis.

Main Results:

• Machine learning offers unique advantages for image recognition tasks.
• ML has demonstrated breakthroughs in medical image analysis, applicable to skeletal imaging.
• Recent research focuses on constructing ML models for forensic bone age assessment.

Conclusions:

• Machine learning is a powerful tool for improving bone age assessment in forensic science.
• The integration of ML with skeletal image analysis is a rapidly advancing area.
• Further development of ML models is expected to enhance forensic identification capabilities.