Using machine learning to identify clotted specimens in coagulation testing

Fang, Kui; Dong, Zheqing; Chen, Xiling; Zhu, Ji; Zhang, Bing; You, Jinbiao; Xiao, Yingjun; Xia, Wenjin

doi:10.1515/cclm-2021-0081

Cited by 17 publications

(10 citation statements)

References 29 publications

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“…Standard and momentum back-propagation neural networks (BPNNs) were trained and validated using training datasets and five-fold cross-validation methods to verify the feasibility of identifying clot specimens through machine learning. Surprisingly, the result confirmed that 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 [ 90 ].…”

Section: The Application Of Clinlabomicsmentioning

confidence: 86%

Clinlabomics: leveraging clinical laboratory data by data mining strategies

et al. 2022

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The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.

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Section: The Application Of Clinlabomicsmentioning

confidence: 86%

Clinlabomics: leveraging clinical laboratory data by data mining strategies

et al. 2022

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“…Each group was trained in a fivefold cross-validation (CV) manner, as described in our previous study. 5 Briefly, the scattergrams were randomly split into five subgroups, and each subgroup served as a validation dataset for assessing the predictive ability of the model, which was trained by using the rest of the subgroups (i.e. in each CV loop, five CNN models were achieved, and all the samples were validated once).…”

Section: Methodsmentioning

confidence: 99%

“…Our previous study demonstrated that a fully connected artificial neural network (FCN) can identify clotted samples by using five parameters from the coagulation test. 5 However, processing image data may require an FCN with a vast number of nodes and weights, which would be untrainable. Deep learning (DL) is a form of artificial intelligence in which computers imitate the working mechanism of the human brain to solve problems.…”

Section: Introductionmentioning

confidence: 99%

Developing and validating a highly sensitive platelet clump detection model for the Sysmex haematology analyser

et al. 2023

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Background: Mainstream haematology analysers (HAs) are reported to have low detection sensitivity for platelet clumps. In this study, a deep learning (DL) algorithm, convolutional neural network (CNN), was implemented to detect platelet clumps. Methods: Adenosine diphosphate (ADP) was used to induce platelet aggregation to mimic platelet clumps detected (PCD) samples. Six types of leukocyte scattergrams were collected from the Sysmex XN-10. Then, multiple CNNs were trained and validated by scattergrams in a fivefold cross-validation (CV) method. Finally, the CNN model with the best CV accuracy was tested with practical routine work samples. Results: A total of 386 samples (190 PCD and 196 negative samples) and 4,253 samples (150 PCD and 4103 negative samples) were eligible for CNN training and practical test, respectively. The CNN with the highest CV accuracy was trained by using scattergrams of side scatter (SSC) vs. forward scatter (FSC) from the white count and nucleated red blood cells (WNR) channel, whose mean area under the curve (AUC), accuracy, specificity, and sensitivity were 0.968, 0.940, 0.937, and 0.942, respectively, in the CV. In the practical test, the AUC, accuracy, specificity, and sensitivity of the CNN were 0.916, 0.961, 0.860, and 0.965, respectively. The dispersed spots presenting around the leucocytes in the WNR channel may be a sign of platelet clumping. Conclusions: This study demonstrates that the CNN algorithms can identify platelet clumps based on optical information from dedicated leukocyte channels and has a higher ability to detect platelet clumps than the XN-10 device’s internal algorithm under practical circumstances.

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“…Since BPNN was trained with five different variables, it could easily detect clots in samples by analyzing the coagulation results from the laboratory database and making the decision-making process easy for the technician. The advantage of BPNN is that it can be embedded directly in an instrument program and used without much difficulty [ 79 ]. Similarly, Kremers et al have developed an ML-based neural network to predict thrombosis in COVID-19 patients.…”

Section: Omicronmentioning

confidence: 99%

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic

et al. 2023

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic’s effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.

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Using machine learning to identify clotted specimens in coagulation testing

Cited by 17 publications

References 29 publications

Clinlabomics: leveraging clinical laboratory data by data mining strategies

Clinlabomics: leveraging clinical laboratory data by data mining strategies

Developing and validating a highly sensitive platelet clump detection model for the Sysmex haematology analyser

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic

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