A mathematical function to evaluate surgical complexity of cleft lip and palate

Area of Science:

• Medical informatics
• Pattern recognition theory
• Congenital malformations

Background:

• Cleft lip and/or palate are common congenital malformations.
• Accurate patient comparison is crucial for treatment and research.
• Existing methods may not fully capture the complexity of these conditions.

Purpose of the Study:

• To model a novel similarity function tailored for the medical environment.
• To apply this function to compare patients with cleft-primary palate and/or cleft-secondary palate.
• To demonstrate the function's utility in assessing patient condition similarity.

Main Methods:

• Utilized a logical-combinatorial approach from pattern recognition theory.
• Defined a similarity function based on 18 variables, considering type, domain, and representation.
• Incorporated six comparison criteria (fuzzy and absolute difference) and variable importance weights reflecting surgical complexity.

Main Results:

• Developed and validated a similarity function adaptable to medical data.
• Successfully applied the function to compare the condition of three patients with clefts.
• Demonstrated the model's capability to quantify similarity based on defined criteria and weights.

Conclusions:

• The proposed similarity function provides a robust method for comparing patients with cleft lip and/or palate.
• This model can aid in clinical decision-making and research by offering a quantitative measure of patient similarity.
• The logical-combinatorial approach offers a flexible framework for medical data analysis.