Artificial intelligence-based autosegmentation for radiotherapy

Background and Purpose: In the field of medicine, artificial intelligence (AI) is emerging as a promising tool. In this paper, we present our experience with the integration of commercially available AI-based software into our radiotherapy contouring workflow. We also analyzed the accuracy of the automated segmentation system. Methods and Materials: We analyzed contours of 19 anatomical regions from 24 patients. Comparisons between AI-generated and human-generated contours were made based on volume, Dice coefficients, and contour center of mass shifts. Results: The data indicate that there are minimal differences between AI-generated and human-generated contours, such as those of the lungs. The volume differences are relatively minor <1 cm(3) (P > 0.05). Nevertheless, for certain organs, such as the small intestine, there can be considerable discrepancies, as the AI delineates the entire organ, in contrast to the RTT. Variations of volumes (bowels) > 300 cm(3). The AI completes the contouring process in approximately 2 min, whereas human experts take up to 1 h to create the structures for a given region. Conclusion: The workflow can be highly automated and standardised, resulting in significant time savings. A consistent level of quality can be maintained, regardless of RTT experience. The results are comparable to those reported by Doolan et al.</span>