Evidence map›Paper›PMID 38526794›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring.

Yu-Fen Wang, Jeng-Lin Li, Chi-Chun Lee, Paul K Wallace, Bor-Sheng Ko

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
3.0field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Yu-Fen WangAHEAD Medicine Corporation, San Jose, CA, USA. andrea.wang@aheadmedicine.com.
Jeng-Lin LiDepartment of Electrical Engineering, National Tsing Hua University, Hsin-Chu, Taiwan.
Chi-Chun LeeDepartment of Electrical Engineering, National Tsing Hua University, Hsin-Chu, Taiwan.
Paul K WallaceRoswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
Bor-Sheng KoAHEAD Medicine Corporation, San Jose, CA, USA.
National Tsing Hua University · TWNational Taiwan University Hospital · TWRoswell Park Comprehensive Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current analytic software and methods rely on experienced analysts to perform labor-intensive manual inspection and interpretation on a series of 2-dimensional plots via a complex, sequential gating process. With an aggravating shortage of professionals and growing demands, it is very challenging to provide the FC analysis results in a fast, accurate, and reproducible way. Artificial intelligence has been widely used in many sectors to develop automated detection or classification tools. Here we describe a type of machine learning method for developing automated disease classification and residual disease monitoring on clinical flow datasets.

Indexed as

Artificial IntelligenceMachine LearningFlow CytometrySoftwareTechnologyAcute myeloid leukemiaArtificial intelligenceAutomated classificationFlow cytometryMachine learning

Identifiers

PMID38526794
OpenAlexW4393151870

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.