-->PONE-D-26-06489-->-->Development and evaluation of AI model with deep learning for segmentation of extraocular muscles in thyroid eye disease-->-->PLOS One

Dear Dr. Tagami,

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Reviewer #1: Partly

Reviewer #2: Yes

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Reviewer #2: N/A

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Dear Authors,

This is an interesting topic.

However, I have some questions and suggestions:

1. You should mention the MRI parameters and protocols.

2. You should combine STIR images with T1.

3. This afirmation is controverse: This might be attributed to the fact that the boundary between the SRM and the upper levator muscle is frequently ambiguous

4. The signal intensity of the muscles can be informed, and a comparison between normal signal intensity and hyperintensity (suggesting active disease) can be included.

5.INCLUDE THAT

The signal intensity ratio of T1 and T2 images was also promising in indicating disease activity

Kirsch E, Kaim AH, De Oliveira MG, von Arx GG. Correlation of signal intensity ratio on orbital MRI-TIRM and clinical activity score as a possible predictor of therapy response in graves’ orbitopathy-a pilot studyat 1. 5 t. Neuroradiol (2011) 53:S99. doi: 10.1007/s00234-011-0913-8.

Politi LS, Godi C, Cammarata G, Ambrosi A, Iadanza A, Lanzi R, et al. Magnetic resonance imaging with diffusion-weighted imaging in the evaluation of thyroidassociated orbitopathy: getting below the tip of the iceberg. Eur Radiol (2014) 24 (5):1118–26

Luccas R, Riguetto CM, Alves M, Zantut-Wittmann DE, Reis F. Computed tomography and magnetic resonance imaging approaches to Graves' ophthalmopathy: a narrative review. Front Endocrinol (Lausanne). 2024 Jan 8;14:1277961.

These studies pointed out the method used to indicate disease activity in extraocular muscles, helping to determine the best treatment approach, because T1 and T2 images are sequences collected routinely.

Reviewer #2: This is very interesting topic and very relevant unmet need. So far in the literature there are scarce attempts to objectivise MRI imaging in patients with thyroid eye disease . Still there are no broadly accepted method enabling to do so. The role of AI in this area of medicine is well known and should be considered as probably most promising one. Designing the method which enables every clinician, regardless to their experience, would serve and prompt earlier recognition of a patients who need medical attention whether it is in a form of emerging designated drugs and / or surgery.

I would like to thank you for your efforts and congratulate on this straight forward paper.

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Reviewer #1: No

Reviewer #2: No

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Development and evaluation of AI model with deep learning for segmentation of extraocular muscles in thyroid eye disease

PONE-D-26-06489R1

Dear Dr. Tagami,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Atsushi Asakura, Ph.D

Academic Editor

PLOS One

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