Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, thyroid segmentation, MRI, hypothyroidism, hyperthyroidism
Motivation: Assess the feasibility of calculating thyroid volumes using T2-weighted MR imaging.
Goal(s): Quantify thyroid volume using deep learning methods in healthy, hypo and hyperthyroid patients.
Approach: We assessed MRIs of 469 healthy, 606 hypothyroid, and 203 hyperthyroid individuals, matched for age, weight, and height.
Results: Findings indicated altered thyroid volumes in healthy (11.02 ml), hyperthyroid (9.42 ml), and hypothyroid (8.35 ml), BMI-normalized volumes also differed: healthy (0.445), hyperthyroid (0.387), and hypothyroid (0.337). There is a moderate association between thyroid volume and weight (0.41, p=3.2e-25) and a weaker link with height (0.17, p=3.9e-05).
Impact: MRI-based analysis of thyroid volumes in healthy, hyper and hypothyroid patients using deep learning, revealing varied absolute and BMI-based normalized volumes.
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