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Prevalence of Machine Learning in Craniofacial Surgery
Martin L Mak1, Sultan Z Al-Shaqsi2, John Phillips2
1Hospital for Sick Children.
The Journal of Craniofacial Surgery
|March 14, 2020
Summary
This study reviews machine learning (ML) applications in craniofacial surgery. It highlights the need for more research in this specialized surgical field.
Area of Science:
- Medical Informatics
- Computer Science
- Surgical Technology
Background:
- Machine learning (ML) involves training computer programs with data to perform tasks.
- Healthcare offers numerous opportunities for ML integration due to complex tasks.
- Existing reviews cover ML in surgery broadly, but not specifically craniofacial surgery.
Purpose of the Study:
- To conduct a detailed scoping review of ML applications in craniofacial surgery.
- To identify current trends and gaps in ML research within this surgical subspecialty.
- To provide a comprehensive overview for future research and clinical implementation.
Main Methods:
- Systematic literature search for studies on ML in craniofacial surgery.
- Data extraction and synthesis of relevant findings.
- Analysis of the scope and impact of ML in the field.
Main Results:
- Limited but growing body of research on ML in craniofacial surgery.
- Identified applications in areas such as surgical planning and outcome prediction.
- Highlighted a need for more extensive studies and validation.
Conclusions:
- ML holds significant potential to advance craniofacial surgery.
- Further research is crucial to fully realize ML's benefits in this specialty.
- This review provides a foundation for understanding ML's current role and future directions.
