Journal Section:
Valvular Heart Disease: Letter

Dear Editor,

This is a response to a published research on “A Deep Learning-Based Method for Rapid 3D Whole-Heart Modeling in Congenital Heart Disease [1].” This work points in a new route for the development of deep learning-based solutions for patients with congenital heart disease, but there are several critical difficulties with the integration of medical imaging data and clinical data that require additional investigation. The diagnosis is excellent; however, the training dataset of 110 patients raises questions about representativeness and variety. Congenital heart disease encompasses a wide spectrum of conditions, each with its own anatomical problems. How can the authors be convinced that their model will apply to various types and complex congenital heart anomalies? A more extensive and diverse dataset, potentially from other schools, might boost the model.

Furthermore, the method for training the deep learning model is not given in sufficient detail. What specific deep learning architecture is being used? Furthermore, the criteria for picking CT and CMR pictures for training and validation remain unclear. Is there a step before processing? How do you normalize the input data? Such nuances are crucial for reproducibility. It could benefit other researchers. They can apply similar strategies in their own work.

The findings indicate that medical specialists greatly value the model’s anatomical accuracy and clarity. However, a quantitative assessment is absent. What specific measurements are utilized to compare the accuracy of 3D printed and virtual reality models to traditional visualization techniques? Validation with Dice similarity coefficient and Hausdorff distance. This would enable a more rigorous evaluation of the model’s effectiveness, with possible uses in preoperative planning and diagnostic support. However, the authors do not predict how these models will affect real-world clinical results. Long-term research studies that assess outcomes such as surgical accuracy and patient recovery time may confirm the therapeutic applicability of the produced models.

Future directions may involve investigating the integration of real-time data collection technologies such as intraoperative imaging. Furthermore, expanding the platform to include machine-learning algorithms to forecast surgical risk based on model analysis could improve decision-making in some circumstances. We’re incorporating feedback from end users like surgeons and cardiologists. Using this approach could result in iterative model upgrades. Overall, this work contributes significantly to the discipline, but only to the extent that the models can effectively meet clinical requirements. However, addressing these critiques and investigating these approaches may assist to improve the use of 3D models in the therapy of congenital cardiac disease.

The authors used AI for language editing and translation of the article.

The authors declare no conflict of interest.

There is no funding.

H.D.: 50% ideas, writing, analyzing, and approval. V.W.: 50% ideas, supervision, and approval.

1.
Huang
H
,
Wu
Y
.
A deep learning-based method for rapid 3D whole-heart modeling in congenital heart disease
.
Cardiology
.
2024
;
11
:
1
–
25
.
Google Scholar
 
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2024