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Artificial intelligence CT screening model for thyroid-associated ophthalmopathy and tests under clinical conditions

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Thyroid-associated ophthalmopathy (TAO) might lead to blindness and orbital deformity. The early diagnosis and treatment are conducive to control disease progression, but currently, there is no effective screening method. The present study aimed to introduce an artificial intelligence (AI) model for screening and testing the model with TAO patients under clinical conditions.

Methods

A total of 1435 computed tomography (CT) scans were obtained from the hospital. These CT scans were preprocessed by resampling and extracting the region of interest. CT from 193 TAO patients and 715 healthy individuals were adopted for three-dimensional (3D)-ResNet model training, and 49 TAO patients and 178 healthy people were adopted for external verification. Data from 150 TAO patients and 150 healthy people were utilized for application tests under clinical conditions, including non-inferiority experiments and diagnostic tests, respectively.

Results

In the external verification of the model, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.919, indicating a satisfactory classification effect. The accuracy, sensitivity, and specificity were 0.87, 088, and 0.85, respectively. In non-inferiority experiments: the accuracy was 85.67% in the AI group and 84.33% in the resident group. The model passed both non-inferiority experiments (p = 0.001) and diagnostic test (the AI group sensitivity = 0.87 and specificity = 0.84%).

Conclusions

A promising orbital CT-based TAO screening AI model was established and passed application tests under clinical conditions. This may provide a new TAO screening tool with further validation.

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Funding

This study was funded by the National Key R&D Program of China (2018YFC1106100, 2018YFC1106101), Interdisciplinary Program of Shanghai Jiao Tong University (ZH2018QNA07, ZH2018ZDA12), the Science and Technology Commission of Shanghai (17DZ2260100, 19410761100, 19DZ2331400) and National Natural Science Foundation of China (61831015, U1908210).

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Author notes

  1. Xuefei Song, Zijia Liu contributed equally to this work as co-first author.

  2. Guangtao Zhai, Huifang Zhou contributed equally to this work as co-corresponding author.

Authors and Affiliations

  1. Department of Ophthalmology, Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Xuefei Song, Lunhao Li, Xianqun Fan & Huifang Zhou

  2. Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

    Xuefei Song, Lunhao Li, Xianqun Fan & Huifang Zhou

  3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Zijia Liu, Zhongpai Gao & Guangtao Zhai

Authors
  1. Xuefei Song
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  2. Zijia Liu
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Correspondence to Guangtao Zhai or Huifang Zhou.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards, and approved by the local ethics committee (No. SH9H-2019-T8-1).

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Song, X., Liu, Z., Li, L. et al. Artificial intelligence CT screening model for thyroid-associated ophthalmopathy and tests under clinical conditions. Int J CARS 16, 323–330 (2021). https://doi.org/10.1007/s11548-020-02281-1

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  • DOI: https://doi.org/10.1007/s11548-020-02281-1

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