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Simple Summary: Accurate preoperative diagnosis of the nature of thyroid nodules remains a challenge and generates a constant demand for improving the diagnostic tools. The aim of the study was to create a more efficient and yet economic diagnostic tool by combining multiple diagnostic techniques prone to daily use. In our research, we combined ultrasound findings, BRAF mutations in fine needle aspiration cytology (FNAC) and functional imaging with a semi-quantitative technique: 99mTc-methoxy-isobutyl-isonitrile ([99mTc]MIBI) wash-out. Preoperative findings were correlated with a histological report from thyroidectomy, which wasable to assess a diagnostic pattern with the help of an artificial intelligence (AI) model for our small cohort. Background: Technology allows us to predict a histopathological diagnosis, but the high costs prevent the large-scale use of these possibilities. The current liberal indication for surgery in benign thyroid conditions led to a rising frequency of incidental thyroid carcinoma, especially low-risk papillary micro-carcinomas. Methods: We selected a cohort of 148 patients with thyroid nodules by ultrasound characteristics and investigated them by fine needle aspiration cytology (FNAC)and prospective BRAF collection for 70 patients. Also, we selected 44 patients with thyroid nodules using semi-quantitative functional imaging with an oncological, 99mTc-methoxy-isobutyl-isonitrile (99mTc-MIBI) radiotracer. Results: Following a correlation with final histopathological reports in patients who underwent thyroidectomy, we introduced the results in a machine learning program (AI) in order to obtain a pattern. For semi-quantitative functional visual pattern imaging, we found a sensitivity of 33%, a specificity of 66.67%, an accuracy of 60% and a negative predicting value (NPV) of 88.6%. For the wash-out index (WOind), we found a sensitivity of 57.14%, a specificity of 50%, an accuracy of 70% and an NPV of 90.06%.The results of BRAF in FNAC included 87.50% sensitivity, 75.00% specificity, 83.33% accuracy, 75.00% NPV and 87.50% PPV. The prevalence of malignancy in our small cohort was 11.4%. Conclusions: We intend to continue combining preoperative investigations such as molecular detection in FNAC, 99mTc-MIBI scanning and AI training with the obtained results on a larger cohort. The combination of these investigations may generate an efficient and cost-effective diagnostic tool, but confirmation of the results on a larger scale is necessary. [ABSTRACT FROM AUTHOR]

We present in this paper some of the existing machine learning and sound processing techniques involved in the medical process and show how they can be applied in the context of a vocal disease detection task. The machine learning techniques are the usual, ranging from decision trees to neural networks, and when it comes to the sound processing approaches some scientists probably are familiar with them from the speech processing or music analysis areas. However, the techniques are adapted to the particularities of the medical field - data collection, verification, etc. Through the concrete example of the vocal disease detection task we worked on, we made some interesting observations, both on the classification ability of the model and on the influence of the data. [ABSTRACT FROM AUTHOR]