Deep learning in nuclear medicine: from imaging to therapy.

Area of Science:

• Artificial Intelligence
• Nuclear Medicine
• Medical Imaging

Background:

• Deep learning, a subset of artificial intelligence (AI), shows significant promise for advancing nuclear medicine.
• AI applications are rapidly evolving within the medical field, particularly in diagnostic imaging.

Purpose of the Study:

• To review recent advancements in deep learning applications within nuclear medicine.
• Focus on key areas: nuclear medicine imaging, lesion detection, and radiopharmaceutical therapy.

Main Methods:

• Review of deep learning techniques, including various neural network architectures.
• Integration of deep learning with functional imaging modalities like PET and SPECT.

Main Results:

• Deep learning enhances accuracy in image reconstruction, lesion segmentation, and diagnosis.
• Improved efficiency in disease detection and treatment planning.
• Enabled precise diagnostics and personalized treatment strategies through AI integration.

Conclusions:

• Deep learning is set to drive significant changes in precision healthcare, real-time treatment monitoring, and clinical decision-making in nuclear medicine.
• Future research will address challenges like model interpretability and generalization for improved clinical application.