Dr. Elizabeth Montero, a researcher at the Universidad Internacional Iberoamericana (International Iberoamerican University, UNIB), is participating in a study that presents an innovative approach based on artificial intelligence for the early diagnosis of thyroid disease, with the aim of reducing the mortality rate of people affected by this disease.
Thyroid disorders encompass various conditions that affect the functioning of this gland located in the neck, responsible for regulating metabolism, energy, growth and development. Among the most common problems are hypothyroidism, which involves insufficient production of thyroid hormones, and hyperthyroidism, characterized by an excess of these hormones. These conditions can cause symptoms such as fatigue, weight changes and emotional or menstrual alterations, and are caused by factors such as autoimmune diseases, iodine deficiency, genetics or certain medications. Their diagnosis combines clinical examinations, blood tests and imaging tests, while treatments include hormone therapy, medication or surgery, depending on the case.
Although the mortality associated with thyroid disorders is low compared to other diseases, thyroid cancer, especially anaplastic carcinoma, represents the greatest risk of death. On the other hand, improper management of hypothyroidism or hyperthyroidism can lead to serious complications.
The diagnosis of these conditions is complex and requires exhaustive testing, but artificial intelligence (AI) is revolutionizing this process. Machine learning algorithms make it possible to analyze large volumes of medical data, identifying patterns that facilitate the early and accurate detection of thyroid disorders. However, traditional diagnostic methods have limitations, such as biases in imbalanced data and a lack of interpretability in current AI models.
In this context, the study used an advanced artificial intelligence-based diagnostic approach, in which two innovative techniques were combined: the synthetic nominal continuous oversampling (SMOTE-NC) method to balance the data and the Light Gradient Boosting Machine (LGBM) machine learning model. To guarantee transparency in the decision-making process, an explainable artificial intelligence (XAI) mechanism based on Shapley additive explanations (SHAP) was integrated. This made it possible to understand how and why the model reaches certain conclusions, strengthening confidence in its clinical use.
The proposed approach achieved 96% accuracy in the diagnosis of thyroid diseases, significantly outperforming traditional methods. It also showed promising results in solving critical problems such as class imbalance in medical data, a common challenge in the analysis of rare or specific diseases. The applications of this approach can function as a decision support tool in clinical settings to offer preliminary diagnostic information and flag potential cases for further evaluation. This advance has the potential to revolutionize the detection and treatment of thyroid disorders, facilitating earlier and more personalized interventions for patients.
In a world where thyroid diseases are increasingly prevalent, this research represents a significant step towards the integration of artificial intelligence in medicine. While there is still a way to go before its widespread implementation, the potential of this technology to save lives and improve quality of life is undeniable. Science and technology, once again, prove to be powerful allies in the quest for improved health for all.
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