Automated Cytopathology of Fine Needle Aspiration for the Detection of Malignancy in Thyroid Cells

Authors

  • Gabrielly Pereira Ribeiro

  • Carlos Musso

  • Dominik Lenz

Keywords:

machine learning; cellprofiler; image cytometry; cytopathology; papillary thyroid carcinoma

Abstract

Cytopathology of thyroid cells is an established method to detect malignancies in the thyroid It is of advantage because an anesthesia and a diagnostic laparotomy is not necessary There are however not yet many studies about automated cytopathology in thyroid cells To this end the aim of the present study was to establish an automated diagnosis of malignancy using image analysis and subsequent machine learning and Artificial intelligence Light microscopy images of 52 patients were analyzed and the results were compared to those of pathology The results of the automated analysis yielded a sensitivity of 0 94 and a specificity of 0 91 when compared to those of the pathologic diagnoses The process of machine learning yielded an under curve area of 0 91 as calculated by a ROC-curve The software used for image analysis machine learning and classification diagnosis are open-source software respectively

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How to Cite

Gabrielly Pereira Ribeiro, Carlos Musso, & Dominik Lenz. (2023). Automated Cytopathology of Fine Needle Aspiration for the Detection of Malignancy in Thyroid Cells. Global Journal of Medical Research, 23(C2), 7–14. Retrieved from https://medicalresearchjournal.org/index.php/GJMR/article/view/102367

Automated Cytopathology of Fine Needle Aspiration for the Detection of Malignancy in Thyroid Cells

Published

2023-06-06