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.
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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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.
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