Faculty of Medicine
Library of the School of Health Sciences

2021-06-29

2021

Fragkopoulos Christos

Ευάγγελος Μισιακός, Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ, Επιβλέπων
Εμμανουήλ Α. Πικουλής, Καθηγητής, Ιατρική Σχολή,ΕΚΠΑ
Ιωάννης Παναγιωτίδης, Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ
Πατάπης Παύλος, Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ
Νάστος Κωνσταντίνος, Επίκουρος Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ
Δελίδης Αλέξανδρος, Επίκουρος Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ
Φούκας Περικλής, Αναπληρωτής Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ

Ο ρόλος διαδικτυακών έμπειρων συστημάτων στην υποβοήθηση της κυτταρολογικής διάγνωσης υλικού FNA ψυχρών όζων του θυρεοειδούς αδένα.

Greek

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Objective: This study investigates the potential of an artificial intelligence (AI) methodology, the Radial Basis Function (RBF) Artificial Neural Network (ANN) in the evaluation of thyroid lesions.
Study design: Was performed on 447 patients that had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid based cytology and the histological result was based on subsequent surgical samples. Each specimen was digitized, on these images nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant.
Results: The RBF ANN obtained in the training set: sensitivity 82.5%, specificity 94.6% and overall accuracy 90.3%, while in the test set these indices were 81.4%, 90.0% and 86.9% respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0% and the specificity 95.5% and no statistically significant difference was observed.
Conclusion: AI techniques and especially ANNs, only the recent years have been studied extensively. The proposed approach is promising, to avoid misdiagnoses and assist, the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.

Health Sciences

Thyroid cytopathology, Liquid based cytology, Artificial neural networks, Radial basis function, Machine learning, Morphology

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https://pergamos.lib.uoa.gr/uoa/dl/object/2947705