Predicting the correlation between neurological abnormalities and thyroid dysfunction using artificial neural networks

Dina Falah Noori Al-Sabak; Leila Sadeghi; Gholamreza Dehghan; Dina Falah Noori Al-Sabak; Leila Sadeghi; Gholamreza Dehghan

doi:10.3934/biophy.2024022

AIMS Biophysics

2024, Volume 11, Issue 4: 403-426. doi: 10.3934/biophy.2024022

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Predicting the correlation between neurological abnormalities and thyroid dysfunction using artificial neural networks

1.
Department of Animal Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran, P.O. Box 5166616471, Tabriz, Iran
2.
Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

Received: 31 July 2024 Revised: 03 October 2024 Accepted: 14 October 2024 Published: 25 October 2024

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.
- BiLSTM,
- deep learning network,
- neurological abnormalities,
- thyroid dysfunction,
- blood samples,
- hypothyroidism,
- Parkinson
Citation: Dina Falah Noori Al-Sabak, Leila Sadeghi, Gholamreza Dehghan. Predicting the correlation between neurological abnormalities and thyroid dysfunction using artificial neural networks[J]. AIMS Biophysics, 2024, 11(4): 403-426. doi: 10.3934/biophy.2024022

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Abstract

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.

Acknowledgments

The authors express gratitude to the Head of the Natural Sciences Department, the Medical Superintendent, the Director, and the Ethics Committee of Tabriz Sadra Institute of Medical Sciences, Tabriz, for granting permission to conduct the study at the institute.

Conflict of interest

Participants in this study provided consent, or consent was waived. Approval (110/IEC/TSIM/2023) was obtained from the Institutional Ethics Committee at Tabriz Sadra Institute of Medical Sciences (TSIM), Tabriz. The study did not involve animal subjects or tissue. The authors disclosed no conflicts of interest, stating that no financial support was received for the submitted work. They also confirmed no financial relationships in the past three years with organizations that may have an interest in the work. Additionally, the authors declared no other relationships or activities that could be perceived as influencing the submitted work.

Author contributions

Dina Falah Noori Al-Sabak and Leila Sadeghi conceptualized the study, designed the methodology, conducted data analysis, supervised the project, and contributed to writing and revising the manuscript. Gholamreza Dehghan assisted with the literature review, collected data, and contributed to writing and revising the manuscript.

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