Radiological images and machine learning: Trends, perspectives, and prospects

Area of Science:

• Radiology
• Medical Imaging
• Machine Learning
• Artificial Intelligence

Background:

• Machine learning (ML) is rapidly advancing, enabling complex pattern recognition in diverse radiological imaging modalities.
• ML systems demonstrate performance comparable to human experts in various diagnostic tasks.
• ML applications are poised to become integral to future clinical decision support and patient monitoring systems.

Purpose of the Study:

• To review fundamental machine learning techniques.
• To explore ML applications across key radiological imaging areas.
• To discuss current challenges and future directions for ML in radiology.

Main Methods:

• Review of machine learning concepts and algorithms.
• Analysis of ML applications in medical image segmentation, neurological studies, and computer-aided diagnosis.
• Examination of ML in image registration and content-based image retrieval.

Main Results:

• ML effectively recognizes and classifies complex patterns in radiological images.
• ML-based systems achieve performance levels rivaling human decision-making.
• Identified key application areas including segmentation, diagnosis, and image retrieval.

Conclusions:

• Machine learning offers significant potential to enhance disease prevention and diagnosis accuracy and efficiency.
• Clinicians can leverage ML applications for improved patient care outcomes.
• Continued research and development are crucial for realizing the full potential of ML in radiology.