Advancing thyroid diagnosis: integrating AI-driven CAD framework with numerical data and ultrasound images

This study proposes an advanced computer-aided diagnosis (CAD) framework for thyroid disease diagnosis that integrates numerical patient data and ultrasound images. The framework uses cutting edge technologies, including Vision Transformers (ViTs) and SHapley Additive exPlanations (SHAPs), to increase diagnostic accuracy, interpretability, and clinical applicability. The proposed CAD framework employs the sparse search algorithm (SSA) for optimized feature selection from numerical data and the tree-structured Parzen estimator for tuning the hyperparameters. ViTs are utilized for analyzing thyroid ultrasound images, whereas SHAP provides explainable AI insights into model predictions. Extensive experiments were conducted on two datasets: the thyroid disease patient dataset and the DDTI: Thyroid Ultrasound Images dataset. Performance was evaluated via five-fold and ten-fold cross-validation utilizing metrics including accuracy, precision, and recall. The framework achieved promising performance, with models trained without data augmentation consistently outperforming their augmented counterparts. For the thyroid disease patient dataset, the best-performing model reported an accuracy of 99.71%, precision of 97.05%, recall of 99.29%, and F1-score of 98.16%. For the DDTI dataset, ViTs achieved an accuracy of 95.06% without augmentation, surpassing existing methodologies. Key features such as thyroxine, thyroid surgery, and thyroid-stimulating hormone (TSH) were identified as critical predictors of thyroid conditions. This study underscores the potentiality of AI-driven approaches in healthcare, paving the way for improved diagnostic outcomes and personalized treatment strategies.