Automated Sonographic Assessment of Hashimoto's Thyroiditis Using Artificial Intelligence

• Autor(a) principal: Oliveira, Luisa Correia Matos de
• Data de Publicação: 2024
• Outros Autores: Oliveira, Gabriela Correia Matos de, Oliveira, Luis Matos de, Figueiredo, Adriana Malta de, Andrade, Luis Jesuino de Oliveira
• Tipo de documento: preprint
• Idioma: por
• Título da fonte: SciELO Preprints
• Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/9472
• topic: Análise ultrassonográfica automatizada da tireoide; Tireoidite de Hashimoto; linguagem de programação C#; Automated Thyroid Ultrasound Analysis; Hashimoto's Thyroiditis; C# programming language

Introduction: Thyroid ultrasound provides valuable insights for thyroid disorders but is hampered by subjectivity. Automated analysis utilizing large datasets holds immense promise for objective and standardized assessment in screening, thyroid nodule classification, and treatment monitoring. However, there remains a significant gap in the development of applications for the automated analysis of Hashimoto's thyroiditis (HT) using ultrasound. Objective: To develop an automated thyroid ultrasound analysis (ATUS) algorithm using the C# programming language to detect and quantify ultrasonographic characteristics associated with HT. Materials and Methods: This study describes the development and evaluation of an ATUS algorithm using C#. The algorithm extracted relevant features (texture, vascularization, echogenicity) from preprocessed ultrasound images and utilizes machine learning techniques to classify them as "normal" or indicative of HT. The model is trained and validated on a comprehensive dataset, with performance assessed through metrics like accuracy, sensitivity, and specificity. The findings highlight the potential for this C#-based ATUS algorithm to offer objective and standardized assessment for HT diagnosis. Results: The program preprocesses images (grayscale conversion, normalization, etc.), segments the thyroid region, extracts features (texture, echogenicity), and utilizes a pre-trained model for classification ("normal" or "suspected Hashimoto's thyroiditis"). Using a sample image, the program successfully preprocessed, segmented, and extracted features. The predicted classification ("suspected HT") with high probability (0.92) aligns with the pre-established diagnosis, suggesting potential for objective HT assessment. Conclusion: C#-based ATUS algorithm successfully detects and quantifies HT features, showcasing the potential of advanced programming in medical image analysis.