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Smart management of sample dilution using an artificial neural network to achieve streamlined processes and saving resources: the automated nephelometric testing of serum free light chain as case study

  • Cristiano Ialongo EMAIL logo , Massimo Pieri and Sergio Bernardini
Published/Copyright: July 12, 2016
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 55 Issue 2

Abstract

Background:

Saving resources is a paramount issue for the modern laboratory, and new trainable as well as smart technologies can be used to allow the automated instrumentation to manage samples more efficiently in order to achieve streamlined processes. In this regard the serum free light chain (sFLC) testing represents an interesting challenge, as it usually causes using a number of assays before achieving an acceptable result within the analytical range.

Methods:

An artificial neural network based on the multi-layer perceptron (MLP-ANN) was used to infer the starting dilution status of sFLC samples based on the information available through the laboratory information system (LIS). After the learning phase, the MLP-ANN simulation was applied to the nephelometric testing routinely performed in our laboratory on a BN ProSpec® System analyzer (Siemens Helathcare) using the N Latex FLC kit.

Results:

The MLP-ANN reduced the serum kappa free light chain (κ-FLC) and serum lambda free light chain (λ-FLC) wasted tests by 69.4% and 70.8% with respect to the naïve stepwise dilution scheme used by the automated analyzer, and by 64.9% and 66.9% compared to a “rational” dilution scheme based on a 4-step dilution.

Conclusions:

Although it was restricted to follow-up samples, the MLP-ANN showed good predictive performance, which alongside the possibility to implement it in any automated system, made it a suitable solution for achieving streamlined laboratory processes and saving resources.

Keywords: artificial neural network; clinical chemistry; clinical laboratory information system; free light chains; laboratory automation
Corresponding author: Dr. med. Cristiano Ialongo, Department of Physiology and Pharmacology, Sapienza University of Rome, Viale Oxford 81, 00135 Rome (RM), Italy, Phone: +3906-2090-2151, Fax: +3906-2090-2357
aCristiano Ialongo and Massimo Pieri contributed equally to this work.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

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Supplemental Material:

The online version of this article (DOI: 10.1515/cclm-2016-0263) offers supplementary material, available to authorized users.

Received: 2016-4-1
Accepted: 2016-6-17
Published Online: 2016-7-12
Published in Print: 2017-2-1

©2017 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2016-0263
Keywords for this article
artificial neural network; clinical chemistry; clinical laboratory information system; free light chains; laboratory automation

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Commutable samples with assigned target concentrations may help us harmonise general chemistry results
  4. Reviews
  5. Performance of point-of-care HbA1c test devices: implications for use in clinical practice – a systematic review and meta-analysis
  6. Cardiac troponins and mortality in type 1 and 2 myocardial infarction
  7. Opinion Paper
  8. Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM Strategic Conference
  9. Genetics and Molecular Diagnostics
  10. External quality assessment for human papillomavirus 16/18 DNA detection and genotyping in Shanghai, China
  11. General Clinical Chemistry and Laboratory Medicine
  12. Analytical performance of 17 general chemistry analytes across countries and across manufacturers in the INPUtS project of EQA organizers in Italy, the Netherlands, Portugal, United Kingdom and Spain
  13. Commutability of proficiency testing material containing tobramycin: a study within the framework of the Dutch Calibration 2.000 project
  14. Optimization and validation of moving average quality control procedures using bias detection curves and moving average validation charts
  15. Extending laboratory automation to the wards: effect of an innovative pneumatic tube system on diagnostic samples and transport time
  16. Smart management of sample dilution using an artificial neural network to achieve streamlined processes and saving resources: the automated nephelometric testing of serum free light chain as case study
  17. An integrated proteomic and peptidomic assessment of the normal human urinome
  18. An alternative inhibition method for determining cross-reactive allergens
  19. Validation of a new assay for α-synuclein detection in cerebrospinal fluid
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  26. Association between apolipoprotein E polymorphisms and premature coronary artery disease: a meta-analysis
  27. Urinary orosomucoid: a novel, early biomarker of sepsis with promising diagnostic performance
  28. Letters to the Editor
  29. CT or MRI
  30. Reply to: CT or MRI in the diagnosis of right lower quadrant abdominal pain?
  31. Quantification of daratumumab in the serum protein electrophoresis
  32. Response to: Interference of daratumumab on the serum protein electrophoresis
  33. Glycated albumin: correlation to HbA1c and preliminary reference interval evaluation
  34. Using “big data” to describe the effect of seasonal variation in thyroid-stimulating hormone
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  36. Therapeutic decision-making process in the intensive care unit: role of biological point-of-care testing
  37. How can we evaluate differences between serial measurements on the same sample? A new approach based on within-subject biological variation
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