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Automated classification of multiple ophthalmic diseases using ultrasound images by deep learning
  1. Yijie Wang1,
  2. Zihao Xu2,
  3. Ruilong Dan2,
  4. Chunlei Yao1,
  5. Ji Shao1,
  6. Yiming Sun1,
  7. Yaqi Wang3,
  8. Juan Ye1
  1. 1 Department of Ophthalmology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
  2. 2 Microelectronics CAD Center, Hangzhou Dianzi University, Hangzhou, China
  3. 3 College of Media Engineering, Communication University of Zhejiang, Hangzhou, China
  1. Correspondence to Dr. Juan Ye, Department of Ophthalmology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; yejuan{at}zju.edu.cn; Dr. Yaqi Wang, College of Media Engineering, Communication University of Zhejiang, Hangzhou, Zhejiang, China; wangyaqi{at}cuz.edu.cn

Abstract

Background Ultrasound imaging is suitable for detecting and diagnosing ophthalmic abnormalities. However, a shortage of experienced sonographers and ophthalmologists remains a problem. This study aims to develop a multibranch transformer network (MBT-Net) for the automated classification of multiple ophthalmic diseases using B-mode ultrasound images.

Methods Ultrasound images with six clinically confirmed categories, including normal, retinal detachment, vitreous haemorrhage, intraocular tumour, posterior scleral staphyloma and other abnormalities, were used to develop and evaluate the MBT-Net. Images were derived from five different ultrasonic devices operated by different sonographers and divided into training set, validation set, internal testing set and temporal external testing set. Two senior ophthalmologists and two junior ophthalmologists were recruited to compare the model’s performance.

Results A total of 10 184 ultrasound images were collected. The MBT-Net got an accuracy of 87.80% (95% CI 86.26% to 89.18%) in the internal testing set, which was significantly higher than junior ophthalmologists (95% CI 67.37% to 79.16%; both p<0.05) and lower than senior ophthalmologists (95% CI 89.45% to 92.61%; both p<0.05). The micro-average area under the curve of the six-category classification was 0.98. With reference to comprehensive clinical diagnosis, the measurements of agreement were almost perfect in the MBT-Net (kappa=0.85, p<0.05). There was no significant difference in the accuracy of the MBT-Net across five ultrasonic devices (p=0.27). The MBT-Net got an accuracy of 82.21% (95% CI 78.45% to 85.44%) in the temporal external testing set.

Conclusions The MBT-Net showed high accuracy for screening and diagnosing multiple ophthalmic diseases using only ultrasound images across mutioperators and mutidevices.

  • diagnostic tests/investigation
  • imaging

Data availability statement

Data are available on reasonable request.

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Data availability statement

Data are available on reasonable request.

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Footnotes

  • Contributors Conceived and designed the study: JY and YQW. Collected the images: YJW, CLY, JS and YMS. Trained and tested the model: ZHX and RLD. Data analysis and manuscript writing: YJW, CLY, JS, ZHX and RLD. JY is the guarantor.

  • Funding This work was supported by the National Key Research and Development Program of China (2019YFC0118400) and the Clinical Medical Research Center for Eye Diseases of Zhejiang Province (2021E50007).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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