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 an...

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Publicado en:Critical Reviews in Clinical Laboratory Sciences Vol. 61; no. 3; pp. 226 - 241
Autores principales: He He, Lingfeng Wang, Xia Wang, Mei Zhang
Formato: pictorial review tables/charts Journal Article
Publicado: Taylor & Francis Ltd May2024
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Artificial intelligence in serum protein electrophoresis: history, state of the art, and perspective.
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          He He
          Lingfeng Wang
          Xia Wang
          Mei Zhang
        affil: Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
      sug:
        subj:
          Blood Protein Electrophoresis
          Artificial Intelligence Utilization
          Deep Learning
          Diagnosis, Laboratory Standards
          Blood Protein Disorders Diagnosis
          Paraproteinemias Blood
          Blood Proteins Analysis
          Sensitivity and Specificity
          Paraproteinemias Diagnosis
          Validity
          Predictive Value of Tests
      ab: 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.
      pubtype: Academic Journal
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        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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