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Generative artificial intelligence for postoperative parameters prediction in implantable collamer lens surgery
Yinglin Zhang1, Ruiling Xi, Lingxi Zeng
1From the Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China (Zhang, Xi, Zeng, Higashita, Liu); Showa Medical University, School of Rehabilitation Sciences, Tokyo, Japan (Kamiya); Tomey Corp., Nagoya, Japan (Higashita, Okamoto); Nagoya Eye Clinic, Aichi, Japan (Nakamura); Daikanyama Eye Clinic, Tokyo, Japan (Igarashi); Yokohama Sky Eye Clinic, Kanagawa, Japan (Hata); Miyata Eye Hospital, Miyazaki, Japan (Miyata).
Purpose:
To predict multiple postoperative parameters after implantable collamer lens (ICL) surgery with generative artificial intelligence, using preoperative anterior segment optical coherence tomography (AS-OCT) images as the input.
Setting:
Daikanyama Eye Clinic, Tokyo, Japan; Miyata Eye Hospital, Miyazaki, Japan; Nagoya Eye Clinic, Nagoya, Japan; Yokohama Sky Eye Clinic, Kanagawa, Japan.
Design:
Retrospective study.
Methods:
The research involved paired preoperative and postoperative AS-OCT images from 1010 patients (1585 eyes) who underwent horizontal ICL implantation and 86 patients (86 eyes) who received vertical implantation from 4 clinical centers. A implantable collamer lens-generative adversarial network (ICL-GAN) was used to predict postoperative structures based on preoperative AS-OCT slice from each eye. Postoperative parameters, including vault, AOD500, and TIA500, were measured from the predicted postoperative structures. The prediction error was evaluated using the mean absolute error (MAE) and root mean square error. The correlation and agreement between the prediction and the achieved values were also analyzed.
Results:
The vaults measured from the predictions of postoperative anatomical structure have a strong correlation with the achieved values on horizontal data ( r = 0.659, P < .01 for ICL size of 12.1 mm; r = 0.799, P < .01 for 12.6 mm; and r = 0.737, P < .01 for 13.2 mm) and when compared with the NK-formula and KS-formula achieved the minimum prediction errors (MAE are 105 μm, 114 μm, and 111 μm). ICL-GAN also performed best on the vertical implantation data. The AOD500 and TIA500 also showed good correlation and agreement with the achieved values.
Conclusions:
The generative artificial intelligence demonstrated the capability to predict multiple postoperative parameters after ICL surgery.
