Predicting the correlation between neurological abnormalities and thyroid dysfunction using artificial neural networks - Dina Falah Noori Al-Sabak; Leila Sadeghi; Gholamreza Dehghan (AIMS Biophysics)
In this work, a deep learning model was developed to predict future neurological parameters for patients with hypothyroidism, enabling proactive health management. The model features a sequential architecture, comprising a long short-term memory (LSTM) layer, a bidirectional LSTM layer, and several fully connected layers. The study assessed the interplay between serum cortisol, dopamine, and GABA levels in hypothyroid individuals, aiming to illuminate how these hormonal fluctuations influence the condition's symptoms and progression, especially in relation to Parkinson's disease. Conducted at the Tabriz Sadra Institute of Medical Sciences in Iran, the observational study involved 80 hypothyroid patients and 80 age-matched healthy controls. The findings showed a correlation between cortisol levels and TSH and an inverse relationship with T3 and T4 levels among hypothyroid patients. Dopamine levels also correlated with TSH, T3, and T4, highlighting their potential impact on Parkinson's disease. Notably, hypothyroid patients aged 54–71 years old experiencing visual hallucinations had reduced occipital GABA levels correlating with hormone levels. The results indicated significant relationships among cortisol, dopamine, and GABA levels, providing insights into their roles in the pathophysiology of hypothyroidism and its association with neurological disorders. The BiLSTM model achieved the highest accuracy at 92.79% for predicting Parkinson's disease likelihood in adult hypothyroid patients, while the traditional LSTM model reached 84.48%. This research suggests promising avenues for future studies and has important implications for clinical management and treatment strategies.