Use of Neurosoft expert system improves turnaround time in a laboratory section specialized in protein diagnostics: a two-year experience

Artificial intelligence in serum protein electrophoresis: history, state of the art, and perspective

Serum protein electrophoresis (SPEP) is a valuable laboratory test that separates proteins from the blood based on their electrical charge and size. The test can detect and analyze various protein abnormalities, and the interpretation of graphic SPEP features plays a crucial role in the diagnosis and monitoring of conditions, such as myeloma. Furthermore, the advancement of artificial intelligence (AI) technology presents an opportunity to enhance the organization and optimization of analytical procedures by streamlining the process and reducing the potential for human error in SPEP analysis, thereby making the process more efficient and reliable. For instance, AI can assist in the identification of protein peaks, the calculation of their relative proportions, and the detection of abnormalities or inconsistencies. This review explores the characteristics and limitations of AI in SPEP, and the role of standardization in improving its clinical utility. It also offers guidance on the rational ordering and interpreting of SPEP results in conjunction with AI. Such integration can effectively reduce the time and resources required for manual analysis while improving the accuracy and consistency of the results.

Development and validation of a deep learning-based protein electrophoresis classification algorithm

Background

Protein electrophoresis (PEP) is an important tool in supporting the analytical characterization of protein status in diseases related to monoclonal components, inflammation, and antibody deficiency. Here, we developed a deep learning-based PEP classification algorithm to supplement the labor-intensive PEP interpretation and enhance inter-observer reliability.

Methods

A total of 2,578 gel images and densitogram PEP images from January 2018 to July 2019 were split into training (80%), validation (10%), and test (10.0%) sets. The PEP images were assessed based on six major findings (acute-phase protein, monoclonal gammopathy, polyclonal gammopathy, hypoproteinemia, nephrotic syndrome, and normal). The images underwent processing, including color-to-grayscale and histogram equalization, and were input into neural networks.

Results

Using densitogram PEP images, the area under the receiver operating characteristic curve (AUROC) for each diagnosis ranged from 0.873 to 0.989, and the accuracy for classifying all the findings ranged from 85.2% to 96.9%. For gel images, the AUROC ranged from 0.763 to 0.965, and the accuracy ranged from 82.0% to 94.5%.

Conclusions

The deep learning algorithm demonstrated good performance in classifying PEP images. It is expected to be useful as an auxiliary tool for screening the results and helpful in environments where specialists are scarce.

Lightweight, open source, easy-use algorithm and web service for paraprotein screening using spatial frequency domain analysis of electrophoresis studies

Introduction

Methods

Results

Conclusions