preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202409.0633.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 8 September 2024 / Approved: 9 September 2024 / Online: 9 September 2024 (09:21:17 CEST)

The objective of this study was to develop and evaluate artificial intelligence (AI) models for the detection and instance segmentation of vertebrae and spinal metastases in computer tomography (CT) scans. The models were trained on datasets consisting of patients with polytrauma and relatively undamaged spines, as well as patients diagnosed with spinal metastases. Our results indicate that the models achieved high performance in vertebra segmentation, with F-beta scores ranging from 0.88 to 0.96 across all vertebra classes. For spinal metastases, the model attained F-beta scores of 0.68 for lytic type and 0.57 for sclerotic type metastases. Additionally, the models demonstrated the capability to detect isolated metastatic nodes in other bones, highlighting their robustness and potential for broader clinical application. Despite these promising results, the study faced limitations including a relatively small and homogeneous dataset, variability in segmentation mask quality, and the need for real-world clinical validation. Future clinical trials are necessary to evaluate the practical utility and effectiveness of these AI models in improving patient outcomes. Our study emphasizes the potential of AI-assisted detection and segmentation models in enhancing diagnostic accuracy and efficiency in clinical practice.

artificial intelligence; spinal metastases; vertebras segmentation; computer tomography; medical imaging; instance segmentation; radiomics

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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