Machine Learning-Assisted Decision Making in Orthopaedic Oncology

Area of Science:

• Computational intelligence and predictive analytics in medicine.

Background:

• Artificial intelligence (AI) mimics human problem-solving, with machine learning (ML) developing algorithms for data-driven predictions.
• Deep learning, a subset of ML, uses layered networks for data generalization.
• ML shows potential in enhancing radiomics for medical image analysis and diagnosis.

Purpose of the Study:

• To explore the application and challenges of machine learning in clinical predictions and radiomics.
• To highlight the development of ML-based calculators for survival prediction in specific cancers.
• To emphasize the need for robust evaluation of ML models.

Main Methods:

• Utilizing machine learning algorithms for clinical prediction and survival analysis.
• Developing and applying ML models to large datasets for diagnostic and prognostic purposes.
• Leveraging deep learning techniques within ML frameworks for enhanced data analysis.

Main Results:

• ML algorithms show promise in improving radiomics and clinical predictions.
• Several ML calculators demonstrate high accuracy in predicting survival for sarcomas and bone metastases.
• Existing ML models require standardized evaluation for robustness.

Conclusions:

• Machine learning offers significant potential in medical imaging and clinical decision support.
• Challenges remain in data diversity, ethical considerations, and model interpretability for widespread AI adoption.
• Balancing computational advancements with these challenges is crucial for the future of AI in healthcare.