Table 4.
Postoperative ICL Vault Prediction Performance of Machine Learning and Conventional Models in the External Validation Dataset from the Japanese Patients (External Validation)
| Mean Vault ± SD (µm) | MAE ± SD (µm) | MedAE (µm) | RMSE (µm) | P Value for MAE | ||
|---|---|---|---|---|---|---|
| Achieved ICL vault (target value) | 476.56 ± 249.06 | – | – | – | – | |
| Predicted ICL vault | Stacking ensemble (XGBoost + LightGBM) | 473.04 ± 164.94 | 143.69 ± 118.76 | 118.68 | 186.29 | Reference |
| Average ensemble (XGBoost + LightGBM) | 473.57 ± 162.70 | 144.07 ± 138.89 | 105.89 | 199.95 | 0.927 | |
| XGBoost (single model) | 468.22 ± 143.11 | 144.11 ± 141.72 | 100.01 | 201.94 | 0.923 | |
| Random forest | 473.41 ± 144.97 | 145.22 ± 141.38 | 108.35 | 202.51 | 0.723 | |
| Support vector machine | 474.56 ± 107.32 | 166.15 ± 163.48 | 134.49 | 232.90 | 0.002 | |
| Linear regression | 476.38 ± 139.56 | 146.58 ± 138.91 | 108.74 | 201.78 | 0.500 | |
| Manufacturer's nomogram (WTW + ACD) | 522.97 ± 93.06 | 179.36 ± 150.69 | 156.28 | 234.09 | <0.001 | |
| NK formula22 (ACW + CLR) | 456.12 ± 83.09 | 167.45 ± 169.90 | 127.45 | 238.35 | 0.002 | |
ACD, anterior chamber depth; ACW, anterior chamber width; CLR, crystalline lens rise; ICL, implantable collamer lens; LightGBM, light gradient boosting machine; MAE, mean absolute prediction error; MedAE, median absolute prediction error; RMSE, root mean square error; SD, standard deviation; WTW, white-to-white.