Data for "Prediction of Phakic Intraocular Lens Vault Using Machine Learning of Anterior Segment Optical Coherence Tomography Metrics"

Prediction of Phakic Intraocular Lens Vault Using Machine Learning of Anterior Segment Optical Coherence Tomography Metrics. Authors: Kazutaka Kamiya, MD, PhD, Ik Hee Ryu, MD, MS, Tae Keun Yoo, MD, Jung Sub Kim MD, In Sik Lee, MD, PhD, Jin Kook Kim MD, Wakako Ando CO, Nobuyuki Shoji, MD, PhD, Tomofusa, Yamauchi, MD, PhD, Hitoshi Tabuchi, MD, PhD. We hypothesize that machine learning of preoperative biometric data obtained by the As-OCT may be clinically beneficial for predicting the actual ICL vault. Therefore, we built the machine learning model using Random Forest to predict ICL vault after surgery. This multicenter study comprised one thousand seven hundred forty-five eyes of 1745 consecutive patients (656 men and 1089 women), who underwent EVO ICL implantation (V4c and V5 Visian ICL with KS-AquaPORT) for the correction of moderate to high myopia and myopic astigmatism, and who completed at least a 1-month follow-up, at Kitasato University Hospital (Kanagawa, Japan), or at B&VIIT Eye Center (Seoul, Korea). This data file (RFR_model(feature=12).mat) is the final trained random forest model for MATLAB 2020a. Python version: *************************************************************** from sklearn.model_selection import train_test_split import pandas as pd import numpy as np from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor # connect data in your google drive from google.colab import auth auth.authenticate_user() from google.colab import drive drive.mount('/content/gdrive') # Change the path for the custom data # In this case, we used ICL vault prediction using preop measurement dataset = pd.read_csv('gdrive/My Drive/ICL/data_icl.csv') dataset.head() #optimal features (sorted by importance) : # 1. ICL size 2. ICL power 3. LV 4. CLR 5. ACD 6. ATA # 7. MSE 8.Age 9. Pupil size 10. WTW 11. CCT 12. ACW y = dataset['Vault_1M'] X = dataset.drop(['Vault_1M'], axis = 1) # Split the dataset to train and test data, if necessary. # For example, we can split data to 8:2 as a simple validation test train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=0) # In our study, we already defined the training (B&VIIT Eye Center, n=1455) and test (Kitasato University, n=290) dataset, this code was not necessary to perform our analysis. # Optimal parameter search could be performed in this section parameters = {'bootstrap': True, 'min_samples_leaf': 3, 'n_estimators': 500, 'criterion': 'mae' 'min_samples_split': 10, 'max_features': 'sqrt', 'max_depth': 6, 'max_leaf_nodes': None} RF_model = RandomForestRegressor(**parameters) RF_model.fit(train_X, train_y) RF_predictions = RF_model.predict(test_X) importance = RF_model.feature_importances_

基于前节光学相干断层扫描（Anterior Segment Optical Coherence Tomography，As-OCT）指标结合机器学习预测有晶状体眼人工晶状体拱顶高度。作者：神谷和典（Kazutaka Kamiya），医学博士、哲学博士；柳翊熙（Ik Hee Ryu），医学硕士、医学博士；柳泰根（Tae Keun Yoo），医学博士；金正燮（Jung Sub Kim），医学博士；李仁植（In Sik Lee），医学博士、哲学博士；金镇国（Jin Kook Kim），医学博士；安藤和子（Wakako Ando），CO；庄司伸之（Nobuyuki Shoji），医学博士、哲学博士；山智房（Tomofusa Yamauchi），医学博士、哲学博士；田渊仁（Hitoshi Tabuchi），医学博士、哲学博士。本研究提出假说，即通过前节光学相干断层扫描（As-OCT）获取的术前生物计量数据结合机器学习技术，在预测人工晶状体植入后的实际拱顶高度方面具有临床应用价值。据此，本研究采用随机森林（Random Forest）算法构建机器学习模型，以预测术后ICL拱顶高度。本多中心研究共纳入1745名连续患者的1745只患眼（男性656例，女性1089例），所有受试者均因矫正中高度近视及近视散光，在北里大学医院（日本神奈川县）或韩国首尔B&VIIT眼科中心接受了EVO ICL（V4c型及V5型Visian ICL联合KS-AquaPORT）植入手术，并完成了至少1个月的术后随访。本数据集配套的模型文件（RFR_model(feature=12).mat）为适用于MATLAB 2020a的最终训练完成的随机森林模型。Python版本实现代码及注释如下：
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
# 连接谷歌云端硬盘中的数据集
from google.colab import auth
auth.authenticate_user()
# 将谷歌云端硬盘挂载至/content/gdrive路径
from google.colab import drive
drive.mount('/content/gdrive')
# 自定义数据集路径
# 本案例中，我们使用术前测量数据进行ICL拱顶高度预测
dataset = pd.read_csv('gdrive/My Drive/ICL/data_icl.csv')
dataset.head()
# 按重要性排序的最优特征：
# 1. ICL尺寸 2. ICL屈光度 3. LV 4. CLR 5. 前房深度（ACD） 6. ATA 7. 等效球镜度（MSE） 8. 年龄 9. 瞳孔直径 10. 白对白径（WTW） 11. 中央角膜厚度（CCT） 12. 前房宽度（ACW）
y = dataset['Vault_1M']
X = dataset.drop(['Vault_1M'], axis = 1)
# 如需拆分训练集与测试集，可执行如下操作
# 例如，我们可按8:2的比例拆分数据以进行简单的验证测试
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=0)
# 本研究已预先定义训练集（B&VIIT眼科中心，n=1455）与测试集（北里大学医院，n=290），因此本拆分代码无需用于本研究的分析流程
# 本部分可用于最优参数搜索
parameters = {'bootstrap': True, 'min_samples_leaf': 3, 'n_estimators': 500, 'criterion': 'mae', 'min_samples_split': 10, 'max_features': 'sqrt', 'max_depth': 6, 'max_leaf_nodes': None}
RF_model = RandomForestRegressor(**parameters)
RF_model.fit(train_X, train_y)
RF_predictions = RF_model.predict(test_X)
importance = RF_model.feature_importances_