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Objectives A sample with a blood clot may produce an inaccurate outcome in coagulation testing, which may mislead clinicians into making improper clinical decisions. Currently, there is no efficient method to automatically detect clots. This study demonstrates the feasibility of utilizing machine learning (ML) to identify clotted specimens. Methods The results of coagulation testing with 192 clotted samples and 2,889 no-clot-detected (NCD) samples were retrospectively retrieved from a laboratory information system to form the training dataset and testing dataset. Standard and momentum backpropagation neural networks (BPNNs) were trained and validated using the training dataset with a five-fold cross-validation method. The predictive performances of the models were then assessed based on the testing dataset. Results Our results demonstrated that there were intrinsic distinctions between the clotted and NCD specimens regarding differences in the testing results and the separation of the groups (clotted and NCD) in the t-SNE analysis. The standard and momentum BPNNs could identify the sample status (clotted and NCD) with areas under the ROC curves of 0.966 (95% CI, 0.958–0.974) and 0.971 (95% CI, 0.9641–0.9784), respectively. Conclusions Here, we have described the application of ML algorithms in identifying the sample status based on the results of coagulation testing. This approach provides a proof-of-concept application of ML algorithms to evaluate the sample quality, and it has the potential to facilitate clinical laboratory automation.

Total anomalous pulmonary venous connection (TAPVC) is a rare but mortal congenital heart disease in children and can be repaired by surgical operations. However, some patients may suffer from pulmonary venous obstruction (PVO) after surgery with insufficient blood supply, necessitating special follow-up strategy and treatment. Therefore, it is a clinically important yet challenging problem to predict such patients before surgery. In this paper, we address this issue and propose a computational framework to determine the risk factors for postoperative PVO (PPVO) from computed tomography angiography (CTA) images and build the PPVO risk prediction model. From clinical experiences, such risk factors are likely from the left atrium (LA) and pulmonary vein (PV) of the patient. Thus, 3D models of LA and PV are first reconstructed from low-dose CTA images. Then, a feature pool is built by computing different morphological features from 3D models of LA and PV, and the coupling spatial features of LA and PV. Finally, four risk factors are identified from the feature pool using the machine learning techniques, followed by a risk prediction model. As a result, not only PPVO patients can be effectively predicted but also qualitative risk factors reported in the literature can now be quantified. Finally, the risk prediction model is evaluated on two independent clinical datasets from two hospitals. The model can achieve the AUC values of 0.88 and 0.87 respectively, demonstrating its effectiveness in risk prediction.

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