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Related Concept Videos

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Utilizing augmented artificial intelligence for aminoacidopathies using collaborative laboratory integrated

Zaib Un Nisa Khan1, Lena Jafri2, Patricia L Hall3

  • 1Resident Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Pakistan.

Annals of Medicine and Surgery (2012)
|October 21, 2022
PubMed
Summary
This summary is machine-generated.

Plasma amino acid profiling aids in diagnosing aminoacidopathies. This study analyzed PAA profiles in Pakistan, comparing reference intervals with global data, and identified Phenylketonuria and Maple Syrup Urine Disorder.

Keywords:
Amino acidsArtificial intelligenceMaple syrup urine diseasePhenylketonuriainherited Metabolic disorders

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Area of Science:

  • Clinical Chemistry
  • Metabolomics
  • Biochemistry

Background:

  • Plasma amino acid (PAA) profiling is crucial for diagnosing aminoacidopathies.
  • Establishing accurate reference intervals (RI) is essential for reliable PAA analysis.

Purpose of the Study:

  • To analyze and report PAA metabolic profiles from Pakistan.
  • To compare PAA-RI from Pakistan with international data using Clinical Laboratory Integrated Reports (CLIR).

Main Methods:

  • A prospective, single-center study analyzed 22 amino acids in plasma samples over one year.
  • Ion-exchange high-performance chromatography was used for PAA analysis.
  • The CLIR application (AAQP) facilitated statistical analysis and interpretation.

Main Results:

  • 92% of PAA profiles were within age-specific RI, indicating non-diagnostic results.
  • 168 out of 2081 tests showed abnormal PAA levels.
  • Phenylketonuria and Maple Syrup Urine Disorder were the primary identified aminoacidopathies.

Conclusions:

  • High agreement (>95%) was found between manual and CLIR-based reporting.
  • CLIR, with augmented AI, can enhance the accuracy of rare aminoacidopathy reporting in developing countries.