Scholarlink

<p>The predictive modeling of postoperative mechanical complications, such as the early micro-rotation of premium toric intraocular lenses (IOLs), is severely hindered by the extreme imbalance of clinical datasets. Traditional statistical methods often fail to capture complex biomechanical interactions in rare-event scenarios. This exploratory pilot study introduces a machine learning framework designed as a hypothesis-generating tool to handle extremely imbalanced ophthalmic data and identify potential preoperative biometric features associated with toric IOL micro-rotation. A prospective cohort of 35 eyes implanted with the Clareon PanOptix® Toric IOL was analyzed, quantifying true rotational stability via high-resolution photographic registration. Given the exceedingly low incidence of &gt; 1-degree micro-rotation, a strict, leak-proof fivefold cross-validation pipeline was established. The Synthetic Minority Over-sampling Technique (SMOTE) was applied exclusively within the training folds, and an interpretable Linear Support Vector Machine (Linear SVM) was deployed to extract robust feature weights for biomechanical interpretation. Our findings highlight the “accuracy paradox” in small clinical datasets: complex ensemble models exhibited severe majority-class bias, failing to detect rare micro-rotations. Conversely, the SMOTE-enhanced Linear SVM achieved a Precision-Recall Area Under the Curve (PR-AUC) of 0.463, outperforming a random baseline by nearly a factor of three. The algorithmic feature weights successfully isolated Anterior Chamber Depth (ACD) and steep keratometry (Steep K2) as the primary geometric drivers of rotational instability, demonstrating a profound alignment with clinical ocular biomechanics. While strictly constrained by the small sample size (N = 35) and limited event rate, this preliminary pilot framework successfully bridges high-dimensional data augmentation with physical ocular biomechanics, effectively identifying minority risk features and laying the groundwork for future AI-driven surgical navigation systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An exploratory SMOTE-SVM approach for identifying preoperative biomechanical risk factors driving early toric intraocular lens micro-rotation in extremely imbalanced cohorts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

查看全文
  • Kuo-Chi Hung,
  • Pi-Jung Lin,
  • Tzyy-Chang Ho,
  • Jin-Siang Shaw

摘要

The predictive modeling of postoperative mechanical complications, such as the early micro-rotation of premium toric intraocular lenses (IOLs), is severely hindered by the extreme imbalance of clinical datasets. Traditional statistical methods often fail to capture complex biomechanical interactions in rare-event scenarios. This exploratory pilot study introduces a machine learning framework designed as a hypothesis-generating tool to handle extremely imbalanced ophthalmic data and identify potential preoperative biometric features associated with toric IOL micro-rotation. A prospective cohort of 35 eyes implanted with the Clareon PanOptix® Toric IOL was analyzed, quantifying true rotational stability via high-resolution photographic registration. Given the exceedingly low incidence of > 1-degree micro-rotation, a strict, leak-proof fivefold cross-validation pipeline was established. The Synthetic Minority Over-sampling Technique (SMOTE) was applied exclusively within the training folds, and an interpretable Linear Support Vector Machine (Linear SVM) was deployed to extract robust feature weights for biomechanical interpretation. Our findings highlight the “accuracy paradox” in small clinical datasets: complex ensemble models exhibited severe majority-class bias, failing to detect rare micro-rotations. Conversely, the SMOTE-enhanced Linear SVM achieved a Precision-Recall Area Under the Curve (PR-AUC) of 0.463, outperforming a random baseline by nearly a factor of three. The algorithmic feature weights successfully isolated Anterior Chamber Depth (ACD) and steep keratometry (Steep K2) as the primary geometric drivers of rotational instability, demonstrating a profound alignment with clinical ocular biomechanics. While strictly constrained by the small sample size (N = 35) and limited event rate, this preliminary pilot framework successfully bridges high-dimensional data augmentation with physical ocular biomechanics, effectively identifying minority risk features and laying the groundwork for future AI-driven surgical navigation systems.