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Clinical decision support system based on deep learning for evaluating implantable collamer lens size and vault after
Yixuan Yang1, Zhengqin Long2, Bo Lei1
1Department of Ophthalmology, The Third Hospital Affiliated to the Third Military Medical University Department of Ophthalmology, Chongqing, China.
Objectives:
To aid doctors in selecting the optimal preoperative implantable collamer lens (ICL) size and to enhance the safety and surgical outcomes of ICL procedures, a clinical decision support system (CDSS) is proposed in our study.
Design:
A retrospective study of patients after ICL surgery.
Setting:
China Tertiary Myopia Prevention and Control Center.
Participants:
2772 eyes belonging to 1512 patients after ICL surgery. Data were collected between 2018 and 2022.
Outcome Measures:
A CDSS is constructed and used to predict vault at 1 month postoperatively and preoperative ICL dimensions using various artificial intelligence methods. Accuracy metrics as well as area under curve (AUC) parameters are used to determine the CDSS prediction methods.
Results:
Among the ICL size prediction models, conventional neural networks (CNNs) achieve the best prediction accuracy at 91.37% and exhibit the highest AUC of 0.842. Regarding the prediction model for vault values 1 month after surgery, CNN surpasses the other methods with an accuracy of 85.27%, which has the uppermost AUC of 0.815. Thus, we select CNN as the prediction algorithm for the CDSS.
Conclusions:
This study introduces a CDSS to assist doctors in selecting the optimal ICL size for patients while improving the safety and postoperative outcomes of ICL surgery.
