Graphical Abstract

Project Overview

Determining the precise, personalized dosage of the thyroid hormone replacement drug, Levothyroxine (LT4), after a total thyroidectomy remains a significant clinical challenge. Standard monotherapy dosing regimens are often imprecise and require lengthy periods of adjustment. This project introduced a novel Fuzzy Logic System (FLS)—an advanced control algorithm—to predict and recommend an appropriate, personalized LT4 dosing regimen in a computational environment (in silico). By leveraging fuzzy logic, the system effectively manages the inherent uncertainties and complexities of human physiological responses to provide a more stable and optimized therapeutic path for thyroidectomized patients.

Major Outcomes

• Personalized Dosing Tool: Developed a novel Fuzzy Logic System (FLS) to calculate precise, personalized Levothyroxine (LT4) doses for post-thyroidectomy patients.
• Superior Regimen: Demonstrated in silico that the FLS-derived dose regimen is dominant and superior to conventional monotherapy methods across nine comparative criteria.
• Improved Stability: The FLS achieved a faster, more stable, and accurate regulation of key thyroid hormone concentrations (TSH, T3, and T4).
• Proof-of-Concept: Provided a strong proof-of-concept for integrating artificial intelligence (fuzzy logic) into personalized endocrine therapy, paving the way for clinical decision support systems.

Paper Source

doi.org/10.1016/j.heliyon.2023.e12797