Machine learning-based nonlinear regression-adjusted real-time quality control modeling: a multi-center study.

Objectives: Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring the performance of the testing process, has gained increasing attention in recent years. It has been questioned for its generalizability among analytes, instruments, laboratories, and hospitals in real-world settings. Our purpose was to build a machine learning, nonlinear regression-adjusted, patient-based real-time quality control (mNL-PBRTQC) with wide application.

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.

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.

Risk Factors of Difficult Pharyngeal Accidental Fishbones Ingestion.

Objective: Accidental pharyngeal fishbone ingestion is a common complaint in ear, nose, and throat clinics. Approximately two-thirds of the accidentally ingested fishbones can be removed using tongue depressors and indirect laryngoscopy. However, the remaining third is challenging to identify and remove using these methods.

Establishment and validation of serum free light chain reference intervals in an ethnic Chinese population.

Background: Quantification of serum free light chains (FLC) and calculation of a kappa/lambda ratio using polyclonal antisera based immunoassays provide laboratories with a sensitive alternative to urine protein electrophoresis (UPE). However, the published 0.26 - 1.

Construction of a multiple myeloma diagnostic model by magnetic bead-based MALDI-TOF mass spectrometry of serum and pattern recognition software.

A diagnosis of multiple myeloma (MM) is difficult to make on the basis of any single laboratory test result. Accurate diagnosis of MM generally results from a number of costly and invasive laboratory tests and medical procedures. The aim of this work is to find a new, highly specific and sensitive method for MM diagnosis.