Szugye Nicholas A, Mahalingam Neeraja, Somasundaram Elanchezhian, Villa Chet, Segala Jim, Segala Michael, Zafar Farhan, Morales David L S, Moore Ryan A
Cleveland Clinic Foundation, Pediatric Cardiology, Cleveland, OH, USA.
Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
Pediatr Cardiol. 2025 Mar;46(3):590-598. doi: 10.1007/s00246-024-03470-4. Epub 2024 Apr 3.
Total Cardiac Volume (TCV)-based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a DenseNet architecture in combination with residual blocks of ResNet architecture. The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). A deep learning-based 3D-CNN model can provide accurate automatic measurement of TCV from CT images. This initial study is limited as a single-center study, though future multicenter studies may enable generalizable and more accurate TCV measurement by inclusion of more diverse cardiac pathology and increasing the training data.
基于计算机断层扫描（CT）的全心容积（TCV）大小匹配是一种用于小儿心脏移植中比较供体和受体心脏大小的新技术，这可能会提高可用移植物的总体利用率。TCV需要手动分割，由于分割所需的时间、专业软件和培训，限制了其广泛应用。本研究旨在确定使用三维卷积神经网络（3D-CNN）的深度学习（DL）方法计算TCV的准确性，其临床目标是使所有移植中心都能快速准确地使用TCV。通过回顾性识别，在0至30岁受试者的CT扫描上分割出真实的TCV。使用真实的分割掩码来训练和测试一个定制的3D-CNN模型，该模型由DenseNet架构与ResNet架构的残差块相结合组成。该模型在270名受试者的队列和44名受试者的验证队列（36名正常受试者，8名患有心脏病的受试者保留用于模型测试）上进行训练。验证队列的平均骰子相似系数为0.94±0.03（范围0.84-0.97）。TCV估计的平均绝对百分比误差为5.5%。模型准确性与受试者年龄、体重或身高之间无显著关联。DL-TCV对正常心脏的平均准确性高于移植名单上的心脏（平均绝对百分比误差4.5±3.9 vs. 10.5±8.5，p = 0.08）。基于深度学习的3D-CNN模型可以从CT图像中提供准确的TCV自动测量。作为一项单中心研究，这项初步研究存在局限性，不过未来的多中心研究可能通过纳入更多样化的心脏病理情况并增加训练数据，实现可推广且更准确的TCV测量。
