Mase Yoko, Matsui Yoshitsugu, Imai Koki, Imamura Kazuya, Irie-Ota Akiko, Chujo Shinichiro, Matsubara Hisashi, Kawanaka Hiroharu, Kondo Mineo
Department of Ophthalmology, Mie University Graduate School of Medicine, Tsu 514-8507, Mie, Japan.
Department of Electrical and Electronic Engineering, Mie University, Tsu 514-8507, Mie, Japan.
J Clin Med. 2024 Aug 15;13(16):4826. doi: 10.3390/jcm13164826.
To develop a machine learning logistic regression algorithm that can classify patients with an idiopathic macular hole (IMH) into those with good or poor vison at 6 months after a vitrectomy. In addition, to determine its accuracy and the contribution of the preoperative OCT characteristics to the algorithm. This was a single-center, cohort study. The classifier was developed using preoperative clinical information and the optical coherence tomographic (OCT) findings of 43 eyes of 43 patients who had undergone a vitrectomy. The explanatory variables were selected using a filtering method based on statistical significance and variance inflation factor (VIF) values, and the objective variable was the best-corrected visual acuity (BCVA) at 6 months postoperation. The discrimination threshold of the BCVA was the 0.15 logarithm of the minimum angle of the resolution (logMAR) units. The performance of the classifier was 0.92 for accuracy, 0.73 for recall, 0.60 for precision, 0.74 for F-score, and 0.84 for the area under the curve (AUC). In logistic regression, the standard regression coefficients were 0.28 for preoperative BCVA, 0.13 for outer nuclear layer defect length (ONL_DL), -0.21 for outer plexiform layer defect length (OPL_DL) - (ONL_DL), and -0.17 for (OPL_DL)/(ONL_DL). In the IMH form, a stenosis pattern with a narrowing from the OPL to the ONL of the MH had a significant effect on the postoperative BCVA at 6 months. Our results indicate that (OPL_DL) - (ONL_DL) had a similar contribution to preoperative visual acuity in predicting the postoperative visual acuity. This model had a strong performance, suggesting that the preoperative visual acuity and MH characteristics in the OCT images were crucial in forecasting the postoperative visual acuity in IMH patients. Thus, it can be used to classify MH patients into groups with good or poor postoperative visual acuity, and the classification was comparable to that of previous studies using deep learning.
开发一种机器学习逻辑回归算法，该算法能够将特发性黄斑裂孔（IMH）患者在玻璃体切除术后6个月时分为视力良好或较差的患者。此外，确定其准确性以及术前光学相干断层扫描（OCT）特征对该算法的贡献。这是一项单中心队列研究。使用43例接受玻璃体切除术患者的43只眼的术前临床信息和光学相干断层扫描（OCT）结果来开发分类器。基于统计显著性和方差膨胀因子（VIF）值，采用过滤方法选择解释变量，目标变量是术后6个月时的最佳矫正视力（BCVA）。BCVA的判别阈值为最小分辨角对数（logMAR）单位的0.15。该分类器的准确率为0.92，召回率为0.73，精确率为0.60，F值为0.74，曲线下面积（AUC）为0.84。在逻辑回归中，术前BCVA的标准回归系数为0.28，外核层缺损长度（ONL_DL）为0.13，外丛状层缺损长度（OPL_DL）-（ONL_DL）为-0.21，（OPL_DL）/（ONL_DL）为-0.17。在IMH形态中，MH从OPL到ONL变窄的狭窄模式对术后6个月的BCVA有显著影响。我们的结果表明，（OPL_DL）-（ONL_DL）在预测术后视力方面对术前视力有类似的贡献。该模型表现出色，表明术前视力和OCT图像中的MH特征对于预测IMH患者的术后视力至关重要。因此，它可用于将MH患者分为术后视力良好或较差的组，且该分类与先前使用深度学习的研究相当。
