Comparative Analysis of Diagnostic Performance Between Elastography and AI-based S-detect for Thyroid Nodule Detection

Objectives: This study aimed to evaluate the diagnostic performance and clinical utility of elastography and S-detect in distinguishing benign from malignant thyroid nodules. Elastography is a non-invasive imaging technique assesses tissue stiffness and elasticity. S-detect is a deep learning-based computer-aided diagnosis (DL-CAD) software that analyzes grayscale ultrasound 2D images to evaluate the morphological characteristics of thyroid nodules, providing a visual guide to the likelihood of malignancy. Method: This retrospective study included 159 patients who underwent thyroid ultrasonography between January 2023 and June 2024. All patients underwent elastography, S-detect analysis, and fine needle aspiration cytology (FNAC). Malignancy status was determined based on the FNAC findings and the diagnostic performance of elasticity contrast index (ECI), S-detect, and evaluations by a radiologist were assessed. Based on FNAC results, 101 patients (63.5%) had benign nodules and 58 patients (36.5%) had malignant nodules. Results: Radiologist interpretation demonstrated the highest diagnostic accuracy (area under the curve 89%), with a sensitivity of 98.28%, specificity of 79.21%, positive predictive value (PPV) of 73.1%, and negative predictive value (NPV) of 98.8%. The elasticity contrast index showed an accuracy of 85%, sensitivity of 87.93%, specificity of 81.19%, PPV of 72.9%, and NPV of 92.1%. S-detect yield the lowest accuracy at 78%, with a sensitivity of 87.93%, specificity of 68.32%, PPV of 61.4%, and NPV of 90.8%. Conclusions: Although limited by its single-center design and sample size, potentially limiting the generalization of the results, the controlled environment ensured consistency and minimized confounding variables. These findings offer valuable insights into the comparative diagnostic utility of Elastography and AI-based S-detect for thyroid nodules in clinical practice.