Artificial neural network to predict post-operative hypocalcemia following total thyroidectomy.

UNLABELLED

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 ( < 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.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12070-024-04608-9.