A computer-aided system for ocular myasthenia gravis diagnosis
Open accessAbstract
The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis (OMG) is time-consuming and laborious, and it lacks quantitative standards. An aided diagnostic system for OMG is proposed to solve this problem. The values calculated by the system include three clinical indicators: eyelid distance, sclera distance, and palpebra superior fatigability test time. For the first two indicators, the semantic segmentation method was used to extract the pathological features of the patient's eye image and a semantic segmentation model was constructed. The patient eye image was divided into three regions: iris, sclera, and background. The indicators were calculated based on the position of the pixels in the segmentation mask. For the last indicator, a calculation method based on the Eyelid Aspect Ratio (EAR) is proposed; this method can better reflect the change of eyelid distance overtime. The system was evaluated based on the collected patient data. The results show that the segmentation model achieves a mean Intersection-Over-Union (mIoU) value of 86.05%. The paired-sample T-test was used to compare the results obtained by the system and doctors, and the p values were all greater than 0.05. Thus, the system can reduce the cost of clinical diagnosis and has high application value.
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CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis.
“Recently, the CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis [51,52,53,54,55,56,57,58].”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [54]
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References 25 · Cited by 21
Research works printed but not linked: 4
- K. E. Osserman, P. Kornfeld, E. Cohen, G. Genkins, H. Mendelow, H. Goldberg, H. Windsley, and L. I. Kaplan, Studies in myasthenia gravis: Review of two hundred eighty-two cases at the Mount Sinai Hospital, New York City, A.M.A. Archives of Internal Medicine, vol. 102, no. 1, pp. 72-81, 1958.
- O. Ronneberger, P. Fischer, and T. Brox, U-Net: Tsinghua Science and Technology, October 2021, 26(5): 749-758
- Convolutional networks for biomedical image segmentation, presented at the Int. Conf. Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015, pp. 234-241.
- T. Soukupová and J. Čech, Real-time eye blink detection using facial landmarks, in Proc. 21 st Computer Vision Winter Workshop, Rimske Toplice, Slovenia, 2016, pp. 1-8.
- Authors
- Liu, G., Wei, Y., Xie, Y., Li, J., Qiao, L., & Yang, J.-J.
- Affiliations
- Beijing University of Technology (CN)
- Tsinghua University (CN)
- Venue
- Tsinghua Science and Technology
- Publisher
- Tsinghua University Press
- Published
- 2021-10
- Volume
- 26
- Issue
- 5
- Pages
- 749-758
- ISSN
- 1007-0214 (ISSN-L)
- OpenAlex
- W3158357085
- Cited by
- 21
- References
- 25
- Citations by year
- 2025: 4 · 2024: 7 · 2023: 5 · 2022: 4 · 2021: 1
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Tsinghua Science & Technology (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pathology and Forensic Medicine › Ophthalmology and Eye Disorders
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- ocular myasthenia gravis, computer-aided diagnosis, semantic segmentation, eye image analysis, iris segmentation, clinical indicators
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