Optimizing Screw Fixation in Total Hip Arthroplasty: A Deep Learning and Finite Element Analysis Approach

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Reviewer’s decision: Substantial revision is required

Reviewer comments：

1. Although FEA (Finite Element Analysis) and DL-FEA (Deep learning + finite element analysis) methods are introduced, the advantages of DL-FEA compared with traditional FEA are not clearly compared, and it is suggested to increase the defect analysis of traditional FEA methods, such as high calculation cost and cumbersome optimization process, and explain how DL-FEA can solve these problems.
2. Although boundary conditions, mesh division and material selection are mentioned, more detailed descriptions are lacking, such as mesh convergence testing and material parameter setting, etc. It is suggested to add descriptions of finite element mesh refinement and convergence testing to provide material properties (such as elastic modulus, Poisson's ratio, etc.) and the basis for selecting parameters of each group of models.
3. The description of the deep learning model is rough, only briefly mentions the input, hidden layer and output of the neural network, but does not provide complete architecture information, such as the number of hidden layers, activation function, loss function, optimization algorithm, etc. It is recommended to describe the deep learning model architecture in detail. Including the number of network layers, activation function (ReLU, LeakyReLU, etc.), loss function (mean square error MSE), optimization algorithm (Adam, SGD, etc.) and other key parameters. At the same time, the validation method of the DL-FEA model is not explained in detail, such as whether it is compared with actual biomechanical data, how to evaluate the generalization ability of the model, etc., the validation process of the deep learning model is added, and how to evaluate the prediction ability of the DL-FEA model, such as whether to use cross-validation, whether to compare with experimental or clinical data. To provide more effective evidence, the authors may consider referring to the following relevant studies: (Accurately and effectively predict the ACL force: Utilizing biomechanical landing pattern before and after fatigue, https://doi.org/10.1016/j.cmpb.2023.107761)
4. In this study, deep learning optimized finite element analysis (DL-FEA) was used to predict the stress distribution of screw fixation. However, the prediction ability of the model was mainly based on static or limited motion conditions, lacking the verification of complex biomechanical models, and failing to consider the influence of dynamic motion state on the stability of screw fixation.
5. The paper showed the deformation distribution map and stress-strain model, but did not fully analyze the specific effects of different screw configurations on the stability of fixation. It is suggested to increase the in-depth analysis of the data, not only to show the image results, but also to explain how different screw numbers, lengths and angles affect the stability of implants and bone stress distribution.
6. Although it is mentioned that the error of MSE is relatively high (24.06%), the sources of error are not discussed in detail, such as insufficient data set size and possible overfitting or underfitting during training. Please further analyze the sources of error, discuss the deficiencies of the DL model, such as whether the input variables are sufficient, whether the data set is diverse, etc., and provide possible improvement measures. Such as the use of transfer learning or more complex network structures, and concrete future research plans, such as the introduction of patient individual modeling, increased experimental validation (e.g., cadcorpse experiments, clinical imaging data), and the use of more machine learning methods (e.g., ensemble learning, reinforcement learning) to further optimize prediction accuracy.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors 1. How is the size and shape of mesh determined in Fig. 3? Is there a mesh sensitivity analysis conducted to show that results are convergent? 2. How is the loading determined in Fig. 7? Is it a static force or a dynamic force can be simulated? 3. Fig. 11 (b) and (d) don't seem to give valid information. 4. What is the color map in Fig. 12 and 13? 5. It would be critical to have some type of experimental measurements to validate the model. 6. Can the proposed DL model be compared with existing machine learning models on the same data in terms of accuracy and computation efficiency? 7. How can you justify that the findings can be applied to more general cases like different loads or steps?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank the authors for their efforts in improving the quality of their papers. The quality of the article has already improved a bit with the revisions. It is suggested that the conclusions be further condensed to provide some substantive comments and recommendations. Please streamline the discussion and conclusion sections.

Author Response

We sincerely appreciate your thoughtful comments and suggestions to improve the quality of our manuscript. As per your request, we have streamlined both the Discussion and Conclusion sections to be more concise while maintaining substantive insights and recommendations (see details in the attachment). These revisions enhance clarity and ensure a stronger focus on the study’s key findings and implications. Thank you for your valuable feedback.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Comments are addressed properly.

Author Response

Thank you for reviewing our revisions. We appreciate your time and feedback.

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

All comments have been addressed.