Revolutionizing cleft lip and palate management through artificial intelligence: a scoping review

Area of Science:

• Medical Informatics
• Computational Biology
• Biomedical Engineering

Background:

• Cleft lip and/or palate (CLP) represents a significant global health concern.
• The integration of Artificial Intelligence (AI) in CLP management remains underexplored.
• A comprehensive understanding of AI applications in CLP is crucial for advancing patient care.

Purpose of the Study:

• To conduct a scoping review of AI applications in cleft lip and/or palate over the last decade.
• To synthesize existing literature on AI's role in CLP prediction, diagnosis, and treatment.
• To identify trends and research gaps in AI-driven CLP approaches.

Main Methods:

• A systematic literature search was conducted across PubMed, Science Direct, Scopus, and LILACS (2014-2024).
• Initial search yielded 649 articles; snowball method identified 3 additional studies.
• After title/abstract screening and full-text review with inclusion/exclusion criteria, 25 studies were selected.

Main Results:

• The reviewed studies predominantly featured observational and experimental designs.
• Geographical distribution showed a concentration of research in the United States.
• AI models, particularly deep learning and machine learning, were applied across 6 subcategories: diagnosis, prediction, treatment, and education.

Conclusions:

• AI technologies hold significant promise for optimizing patient care in cleft lip and/or palate.
• Current AI advancements in CLP are promising but require further research for refinement and expansion.
• AI integration, including assisted algorithms, predictive genetics models, and surgical planning, is advancing CLP management.