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An interpretable model predicts visual outcomes of no light perception eyes after open globe injury
  1. Xiangda Meng1,
  2. Qihua Wang1,2,
  3. Song Chen1,
  4. Shijie Zhang3,
  5. Jinguo Yu1,
  6. Haibo Li4,
  7. Xinkang Chen3,
  8. Zhaoyang Wang5,
  9. Wenzhen Yu6,
  10. Zhi Zheng7,
  11. Heding Zhou8,
  12. Jing Luo9,
  13. Zhiliang Wang10,
  14. Haoyu Chen11,
  15. Nan Wu12,
  16. Dan Hu13,
  17. Suihua Chen14,
  18. Yong Wei15,
  19. Haibin Cui16,
  20. Huping Song17,
  21. Huijin Chen18,
  22. Yun Wang19,
  23. Jie Zhong20,
  24. Zhen Chen21,
  25. Haokun Zhang1,
  26. Tiantian Yang1,
  27. Mengxuan Li1,
  28. Yuanyuan Liu1,
  29. Xue Dong1,22,
  30. Mei Du3,22,
  31. Xiaohong Wang3,22,
  32. Xuyang Yao23,
  33. Haotian Lin24,25,26,
  34. Mulin Jun Li3,27,
  35. Hua Yan1,22,28
  1. 1Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, China
  2. 2Department of Ophthalmology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
  3. 3Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
  4. 4Department of Ocular Trauma, Xiamen University Xiamen Eye Center, Xiamen, Fujian, China
  5. 5Department of Ophthalmology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
  6. 6Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  7. 7Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China
  8. 8Department of Ophthalmology, Ningbo Eye Hospital, Ningbo, Zhejiang, China
  9. 9Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, China
  10. 10Department of Ophthalmology, Fudan University Huashan Hospital, Shanghai, China
  11. 11Department of Ocular Trauma, Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China
  12. 12Department of Ophthalmology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
  13. 13Department of Ophthalmology, Eye Institute of Chinese PLA, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
  14. 14Department of Ophthalmology, General Hospital of Eastern Theater Command, Nanjing, Jiangsu, China
  15. 15National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
  16. 16Department of Ocular Trauma, Heilongjiang Province Ophthalmology Hospital, Harbin, Heilongjiang, China
  17. 17Department of Ophthalmology, Xi'an People's Hospital (Xi'an No.4 Hospital), Xi'an, Shaanxi, China
  18. 18Department of Ophthalmology, Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China
  19. 19Department of Ophthalmology, Xining First People's Hospital, Xining, Qinghai, China
  20. 20Department of Ophthalmology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
  21. 21Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
  22. 22Laboratory of Molecular Ophthalmology and Tianjin Key Laboratory of Ocular Trauma, Tianjin Medical University, Tianjin, China
  23. 23Tianjin Medical University Eye Hospital, Eye Institute & School of Optometry and Ophthalmology, Tianjin, China
  24. 24State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
  25. 25Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, Hainan, China
  26. 26Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
  27. 27Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
  28. 28School of Medicine, Nankai University, Tianjin, China
  1. Correspondence to Hua Yan, Department of Ophthalmology, Tianjin Medical University General Hospital, Tianjin, China; zyyyanhua{at}tmu.edu.cn; Haotian Lin, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China; linht5{at}mail.sysu.edu.cn; Mulin Jun Li, Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China; mulin{at}tmu.edu.cn

Abstract

Background The visual outcome of open globe injury (OGI)-no light perception (NLP) eyes is unpredictable traditionally. This study aimed to develop a model to predict the visual outcomes of vitrectomy surgery in OGI-NLP eyes using a machine learning algorithm and to provide an interpretable system for the prediction results.

Methods Clinical data of 459 OGI-NLP eyes were retrospectively collected from 19 medical centres across China to establish a training data set for developing a model, called ‘VisionGo’, which can predict the visual outcome of the patients involved and compare with the Ocular Trauma Score (OTS). Another 72 cases were retrospectively collected and used for human–machine comparison, and an additional 27 cases were prospectively collected for real-world validation of the model. The SHapley Additive exPlanations method was applied to analyse feature contribution to the model. An online platform was built for real-world application.

Results The area under the receiver operating characteristic curve (AUC) of VisionGo was 0.75 and 0.90 in previtrectomy and intravitrectomy application scenarios, which was much higher than the OTS (AUC=0.49). VisionGo showed better performance than ophthalmologists in both previtrectomy and intravitrectomy application scenarios (AUC=0.73 vs 0.57 and 0.87 vs 0.64). In real-world validation, VisionGo achieved an AUC of 0.60 and 0.91 in previtrectomy and intravitrectomy application scenarios. Feature contribution analysis indicated that wound length-related indicators, vitreous status and retina-related indicators contributed highly to visual outcomes.

Conclusions VisionGo has achieved an accurate and reliable prediction in visual outcome after vitrectomy for OGI-NLP eyes.

  • Trauma

Data availability statement

All data generated or analysed during this study are included in this published article and its supplementary document.

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Data availability statement

All data generated or analysed during this study are included in this published article and its supplementary document.

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Footnotes

  • XM and QW are joint first authors.

  • Contributors XM collected and analysed the data, conducted experiments, wrote the manuscript. QW revised the manuscript, polished the English written and improved the experiment. XM and QW contributed equally to this paper. SC revised the manuscript and polished the English written. SZ conducted the model and wrote the manuscript. HY and MJL conceived and supervised the project. HL revised the manuscript. JY, HL, ZW, WY, ZZ, HZ, JL, ZW, HC, NW, DH, SC, YW, HC, HS, HC, YW, JZ, ZC, HZ, TY, ML collected data. YL, XD, MD, XW, XY analysed data. XC processed data. HY is the guarantor.

  • Funding This study was supported by National Key R&D Program of China (grants 2021YFC2401404) and National Natural Science Foundation of China (grants 81830026 and 82020108007).

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  • Competing interests None declared.

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

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