PDF(Pubmed)
Abstract：
The primary objective of this study was to use artificial neural network (ANN) to predict the post operative hypocalcemia and severity of hypocalcemia following total thyroidectomy. The secondary objective was to determine the weightage for the factors predicting the hypocalcemia with the ANN. A single center, retrospective case series included treatment-naive patients undergoing total thyroidectomy for benign or malignant thyroid nodules from January 2020 to December 2022. Artificial neural network (ANN) - Multilayer Perceptron (MLP) used to predict post-operative hypocalcemia in ANN. Multivariate analysis was used construct validity. The data of 196 total thyroidectomy cases was used for training and testing. The mean incorrect prediction during training and testing was 3.18% (± σ = 0.65%) and 3.66% (± σ = 1.88%) for hypocalcemia. The cumulative Root-Mean-Square-Error (RMSE) for MLP model was 0.29 (± σ = 0.02) and 0.32 (± σ = 0.04) for training and testing, respectively. Area under ROC was 0.98 for predicting hypocalcemia 0.942 for predicting the severity of hypocalcemia. Multivariate analysis showed lower levels of post operative parathormone levels to be predictor of hypocalcemia (p < 0.01). The maximum weightage given to iPTH (100%) > Need for sternotomy (28.55%). Our MLP NN model predicted the post-operative hypocalcemia in 96.8% of training samples and 96.3% of testing samples, and severity in 92.8% of testing sample in 10 generations. however, it must be used with caution and always in conjunction with the expertise of the surgical team. Level of Evidence - 3b.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s12070-024-04608-9.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s12070-024-04608-9.
摘要：
这项研究的主要目的是使用人工神经网络（ANN）来预测全甲状腺切除术后的术后低钙血症和低钙血症的严重程度。次要目标是确定预测ANN低钙血症的因素的权重。一个单一的中心，回顾性病例系列包括2020年1月至2022年12月期间未接受治疗的良性或恶性甲状腺结节全切除术患者.人工神经网络（ANN）-用于预测ANN术后低钙血症的多层感知器（MLP）。多因素分析采用结构效度。196例甲状腺全切除术的数据用于训练和测试。对于低钙血症，训练和测试期间的平均错误预测为3.18％（±σ=0.65％）和3.66％（±σ=1.88％）。MLP模型的累积均方根误差（RMSE）为0.29（±σ=0.02）和0.32（±σ=0.04），用于训练和测试。分别。预测低钙血症的ROC下面积为0.98，预测低钙血症的严重程度为0.942。多因素分析显示，术后副激素水平降低是低钙血症的预测因子（p<0.01）。给予iPTH的最大权重(100%)>需要胸骨切开术(28.55%)。我们的MLPNN模型预测了96.8%的训练样本和96.3%的测试样本的术后低钙血症,10代中92.8%的测试样品的严重程度。然而,必须谨慎使用，并始终与手术团队的专业知识结合使用。证据水平-3b
■在线版本包含补充材料，可在10.1007/s12070-024-04608-9获得。
■在线版本包含补充材料，可在10.1007/s12070-024-04608-9获得。
