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Deep Learning Enhances Thyroid Nodule Diagnosis Accuracy with Motion Correction

Thyroid nodules are a common concern, with ultrasonography being a key tool for their assessment due to its non-invasive nature and cost-effectiveness. However, the accuracy of ultrasound in diagnosing thyroid cancer can be limited, leading to unnecessary biopsies. Novel imaging modalities like contrast-free high-definition microvasculature imaging (HDMI) have emerged to improve diagnostic accuracy.

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One challenge in ultrasound imaging is inter-frame motion artifacts, which can affect the reliability of biomarkers used for classification. These artifacts are particularly prominent near the thyroid due to carotid artery pulsation. To address this issue, a deep learning-based motion correction technique was introduced in this study to enhance the quality of microvasculature images and improve the classification of thyroid nodules.

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The motion correction framework, known as IFMoCoNet, effectively compensated for motion-induced artifacts, resulting in more accurate biomarkers for distinguishing between benign and malignant nodules. By analyzing the quantitative biomarkers derived from the corrected images, significant differences were observed in the characteristics of benign and malignant nodules compared to images with motion artifacts.

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Key morphological features of the microvessels, such as vessel density, number of vessel segments, and bifurcation angles, were found to be more consistent and statistically significant after motion correction. These features play a crucial role in differentiating between benign and malignant nodules. Machine learning algorithms, particularly support vector machines, were employed to classify the nodules based on the quantitative biomarkers extracted from the motion-corrected images.

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The results showed that the sensitivity of the classification model improved significantly for nodules with high motion after applying the motion correction technique. The overall classification performance, including accuracy, sensitivity, and specificity, demonstrated notable enhancements with the corrected images. Furthermore, a comparative analysis with an existing two-stage motion correction method revealed the superiority of the deep learning-based approach in terms of performance and computational efficiency.

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In conclusion, this study underscores the importance of motion correction in ultrasound imaging for accurate classification of thyroid nodules. The deep learning-based framework presented in this research shows promise in enhancing the quality of microvasculature images and improving diagnostic accuracy, particularly in cases with significant motion artifacts.

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