A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.

OBJECTIVES: Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection of sample mix-ups. The aim of the study was to develop a highly accurate DC method based on designed deep learning to detect sample mix-up. METHODS: A total of 22 routine hematology test items were adopted for the study. The hematology test results, collected from two hospital laboratories, were independently divided into training, validation, and test sets. By selecting six mainstream algorithms, the Deep Belief Network (DBN) was able to learn error-free and artificially (intentionally) mixed sample results. The model's analytical performance was evaluated using training and test sets. The model's clinical validity was evaluated by comparing it with three well-recognized statistical methods. RESULTS: When the accuracy of our model in the training set reached 0.931 at the 22nd epoch, the corresponding accuracy in the validation set was equal to 0.922. The loss values for the training and validation sets showed a similar (change) trend over time. The accuracy in the test set was 0.931 and the area under the receiver operating characteristic curve was 0.977. DBN demonstrated better performance than the three comparator statistical methods. The accuracy of DBN and revised weighted delta check (RwCDI) was 0.931 and 0.909, respectively. DBN performed significantly better than RCV and EDC. Of all test items, the absolute difference of DC yielded higher accuracy than the relative difference for all methods. CONCLUSIONS: The findings indicate that input of a group of hematology test items provides more comprehensive information for the accurate detection of sample mix-up by machine learning (ML) when compared with a single test item input method. The DC method based on DBN demonstrated highly effective sample mix-up identification performance in real-world clinical settings.

A multi-model fusion algorithm as a real-time quality control tool for small shift detection.

BACKGROUND: Patient-based real-time quality control (PBRTQC), a complement to traditional QC, may eliminate matrix effect from QC materials, realize real-time monitoring as well as cut costs. However, the accuracy of PBRTQC has not been satisfactory as physicians expect till now. Our aim is to set up a artificial intelligence-based QC for small error detection in real laboratory settings. Taking tPSA as our unique research subject, data extraction, data stimulation, data partition, model construction and evaluation were designed. METHODS: 84241 deidentified results for tPSA were extracted from Laboratory Information System of Aviation General Hospital. The data set was accumulated by way of data simulation. Independent training and test datasets were separated. After three classification models (RF, SVM and DNN) in ML constructed and weighted by information entropy, a multi-model fusion algorithm was generated. Performance of the fusion model was evaluated by comparing with optimal PBRTQC. RESULTS: For 4 PBRTQC methods, MovSO showed overall better performance for 0.2 µg/L bias and optimal MNPed was equal to 200. For the fusion model, MNPeds were less than 12 for all biases, and ACC surpassed MovSO nearly 100 times. Except for 0.01 µg/L bias, ACC was more than 0.9 for the rest of biases. FPR was apparently lower than MovSO, only 0.2% and 0.1%. CONCLUSION: The fusion model shows outstanding performance and reduces incorrect and omitting error detection, adaptable for the real settings.

[Pathogen spectrum and epidemic status of major human parasites in Chenzhou City, Hunan Province].

OBJECTIVE: To understand the pathogen spectrum and epidemic status of major human parasites in Chenzhou City, Hunan Province, so as to provide the evidence for parasitic diseases control. METHODS: The survey sites were selected by the stratified cluster sampling method. The intestinal helminthic eggs were detected by Kato-Katz technique. The trophozoites or cysts of intestinal protozoa were detected by saline smear and iodine staining methods. The eggs of Enterubius vermicularis of children from 3 to 6 years old were detected by the cellophane anal swab method. The species of hookworm were identified by the filter paper strip culture method. RESULTS: A total of 7 031 people were detected with the intestinal helminthic infective rate of 1.83% (129 cases). The major parasite was hookworm and there was a statistically significant difference of the infection rates among various parasites (χ2 = 107.77, P < 0.01). All the hookworm larvae were Necator americanus. No intestinal protozoon was detected. There were statistically significant differences of the infection rates among the counties (χ2 = 25.77, P < 0.01). The age of the patients was mainly focused on 30 and above years old and the infection rate was increased with the growth of age (χ2 = 26.21, P < 0.01). Farmers were the main population of the patients and there was a statistically significant difference of the infection rates between farmer and others (χ2 = 29.67, P < 0.01). CONCLUSIONS: The infection rates of parasites are low and hook-worm is the main parasite in the pathogen spectrum in Chenzhou City. However, the infection factors still exist, therefore, effective and scientific measures should be taken to consolidate the achievement.