Evidence map›Paper›PMID 36268324›Full record

ArticleAnnals of medicine and surgery (2012)2022

Utilizing augmented artificial intelligence for aminoacidopathies using collaborative laboratory integrated reporting- A cross-sectional study.

Zaib Un Nisa Khan, Lena Jafri, Patricia L Hall, Matthew J Schultz, Sibtain Ahmed, Aysha Habib Khan, Hafsa Majid

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Article in Annals of medicine and surgery (2012), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Zaib Un Nisa KhanResident Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Pakistan.
Lena JafriDepartment of Pathology and Laboratory Medicine, Aga Khan University, Pakistan.
Patricia L HallMayo Clinic Department of Laboratory Medicine, and Pathology, Rochester, Minnesota, USA.
Matthew J SchultzMayo Clinic Department of Laboratory Medicine, and Pathology, Rochester, Minnesota, USA.
Sibtain AhmedDepartment of Pathology and Laboratory Medicine, Aga Khan University, Pakistan.
Aysha Habib KhanDepartment of Pathology and Laboratory Medicine, Aga Khan University, Pakistan.
Hafsa MajidDepartment of Pathology and Laboratory Medicine, Aga Khan University, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Plasma amino acids profiling can aid in the screening and diagnosis of aminoacidopathies. The goal of the current study was to analyze and report the metabolic profiles of plasma amino acid (PAA) and additionally to compare PAA-reference intervals (RI) from Pakistan with more countries utilizing Clinical Laboratory Integrated Reports (CLIR). Methods: This was a cross sectional prospective single center study. Twenty-two amino acids were analyzed in each sample received for one year at the clinical laboratory Result: The majority of 92% (n = 1913) of PAA profiles out of the total 2081 tests run were non-diagnostic; the PAA values were within the age-specific RI. The PAA median was in close comparison close to the 50th percentile of reference data available in CLIR software. Out of the total 2081 tests run, one hundred and sixty-eight had abnormal PAA levels; 27.38% were labeled as non-fasting samples, and the main aminoacidopathies identified were Phenylketonuria and Maple Syrup Urine Disorder. Conclusion: An agreement of >95% was observed between the reporting done by the pathologists and technologists' team and then after the application of CLIR. Augmented artificial intelligence using CLIR can improve the accuracy of reporting rare aminoacidopathies in a developing country like ours.

Indexed as

Amino acidsArtificial intelligenceinherited Metabolic disordersMaple syrup urine diseasePhenylketonuria

Identifiers

PMID36268324
PMCPMC9577660

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