학술논문
How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images
Document Type
article
Author
Silvia Strolin; Miriam Santoro; Giulia Paolani; Ilario Ammendolia; Alessandra Arcelli; Anna Benini; Silvia Bisello; Raffaele Cardano; Letizia Cavallini; Elisa Deraco; Costanza Maria Donati; Erika Galietta; Andrea Galuppi; Alessandra Guido; Martina Ferioli; Viola Laghi; Federica Medici; Maria Ntreta; Natalya Razganiayeva; Giambattista Siepe; Giorgio Tolento; Daria Vallerossa; Alice Zamagni; Alessio Giuseppe Morganti; Lidia Strigari
Source
Frontiers in Oncology, Vol 13 (2023)
Subject
Language
English
ISSN
2234-943X
Abstract
BackgroundA CE- and FDA-approved cloud-based Deep learning (DL)-tool for automatic organs at risk (OARs) and clinical target volumes segmentation on computer tomography images is available. Before its implementation in the clinical practice, an independent external validation was conducted.MethodsAt least a senior and two in training Radiation Oncologists (ROs) manually contoured the volumes of interest (VOIs) for 6 tumoral sites. The auto-segmented contours were retrieved from the DL-tool and, if needed, manually corrected by ROs. The level of ROs satisfaction and the duration of contouring were registered. Relative volume differences, similarity indices, satisfactory grades, and time saved were analyzed using a semi-automatic tool.ResultsSeven thousand seven hundred sixty-five VOIs were delineated on the CT images of 111 representative patients. The median (range) time for manual VOIs delineation, DL-based segmentation, and subsequent manual corrections were 25.0 (8.0-115.0), 2.3 (1.2-8) and 10.0 minutes (0.3-46.3), respectively. The overall time for VOIs retrieving and modification was statistically significantly lower than for manual contouring (p