Deep Learning-Based Sample Misidentification Error Detection in Clinical Chemistry Test

We developed a deep neural network (DNN) model to detect sample misidentification error using previous and current clinical chemistry test results as input and compared the detection performance with conventional methods such as DPC and absDPC. DNN models were developed to detect sample misidentification errors for each of the five tumor markers; AFP, CA19-9, CA125, CEA, and PSA. As a result of 1,000 times of repeated simulations of 1% random sample misidentification test, the DNN model achieved an AUC of ≥0.802. Moreover, the accuracy of DNN model was ≥0.732, which is higher than the DPC (≥0.646) and absDPC (≥0.509).Clinical Relevance: The DNN model more effectively detects sample mix errors in tumor markers than the conventional delta check methods.