Abstract
Resuscitative endovascular balloon occlusion of the aorta (REBOA) is an endovascular procedure for hemorrhage control. In REBOA, the balloon must be placed in the precise place, but it may be performed without X-ray fluoroscopy. This study aimed to estimate the REBOA zones from the body surface using deep learning for safe balloon placement. A total of 198 abdominal computed tomography (CT) datasets containing the regions of the REBOA zones were collected from open data libraries. Then, depth images of the body surface generated from the CT datasets and the images corresponding to the zones were labeled for deep learning training and validation. DeepLabV3+, a deep learning semantic segmentation model, was employed to estimate the zones. We used 176 depth images as training data and 22 images as validation data. A nine-fold cross-validation was performed to generalize the performance of the network. The median Dice coefficients for Zones 1-3 were 0.94 (inter-quarter range: 0.90–0.96), 0.77 (0.60–0.86), and 0.83 (0.74–0.89), respectively. The median displacements of the zone boundaries were 11.34 mm (5.90–19.45), 11.40 mm (4.88–20.23), and 14.17 mm (6.89–23.70) for the boundary between Zones 1 and 2, between Zones 2 and 3, and between Zone 3 and out of zone, respectively. This study examined the feasibility of REBOA zone estimation from the body surface only using deep learning-based segmentation without aortography.
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This work was partly supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI Grant (No. 22K18217).
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Takeshi Takata, Kentaro Yamada, Masayoshi Yamamoto, and Hiroshi Kondo conceived the idea of the study. Takeshi Takata performed material preparation, data collection, and analysis. Takeshi Takata and Kentaro Yamada wrote the first draft of the main manuscript text. Masayoshi Yamamoto and Hiroshi Kondo commented on the draft. All authors reviewed the manuscript.
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Takata, T., Yamada, K., Yamamoto, M. et al. REBOA Zone Estimation from the Body Surface Using Semantic Segmentation. J Med Syst 47, 42 (2023). https://doi.org/10.1007/s10916-023-01938-z
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DOI: https://doi.org/10.1007/s10916-023-01938-z