Evidence map›Paper›PMID 41486249›Full record

ArticleThe international journal of cardiovascular imaging2026

Automated analysis of echocardiographic images: do different artificial intelligence tools provide concordant measurements?

Teodora Szasz, Juan I Cotella, Jeremy A Slivnick, Jia Guo, Niklas Hitschrich, Hendrik Wiebel, Marcus Schreckenberg, Federico M Asch, Victor Mor-Avi, Roberto M Lang

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in The international journal of cardiovascular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Teodora SzaszPhilips HealthCare, Cambridge, MA, USA. Teodora.Szasz@bsd.uchicago.edu.
Juan I CotellaUniversity of Chicago Medical Center, Chicago, IL, USA.
Jeremy A SlivnickUniversity of Chicago Medical Center, Chicago, IL, USA.
Jia GuoUniversity of Chicago Medical Center, Chicago, IL, USA.
Niklas HitschrichPhilips HealthCare, Cambridge, MA, USA.
Hendrik WiebelPhilips HealthCare, Cambridge, MA, USA.
Marcus SchreckenbergPhilips HealthCare, Cambridge, MA, USA.
Federico M AschMedStar Health Research Institute, Washington, DC, USA.
Victor Mor-AviUniversity of Chicago Medical Center, Chicago, IL, USA. vmoravi@bsd.uchicago.edu.
Roberto M LangUniversity of Chicago Medical Center, Chicago, IL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of artificial intelligence (AI) in echocardiography has been growing continuously, particularly in chamber quantification and disease detection. Although studies have validated different AI approaches for automated quantification of cardiac size and function, it is unknown whether different AI software tools provide concordant measurements. Accordingly, we aimed to: (1) evaluate the inter-software variability of the automated, AI-based measurements, and (2) compare their variability to that of conventional measurements performed by expert readers.Echocardiographic images from 116 randomly selected subjects, including 66 patients with cardiac pathology and 50 healthy volunteers were analyzed to obtain the following: (1) linear measurements: end-diastolic left ventricular internal diameter (LVIDd), basal right ventricular diameter (RVDd), and LV posterior wall thickness (LVPWd), (2) Doppler measurements (e' lateral, e' septal, mitral inflow E/A ratio), and (3) end-diastolic and end-systolic left ventricular volumes (LVEDV, LVESV), ejection fraction (LVEF), and end-systolic left atrial volume (LAESV). These measurements were performed by: (1) two fully automated AI-based software tools (Us2.AI and Philips), and (2) two expert readers using guidelines recommended methodology. Inter-software and inter-observer variability were assessed by percent absolute difference (PAD) and intraclass correlation coefficients (ICC) between corresponding measurements.AI measurements differed from each other, with the differences reaching significance for 6/10 parameters, while conventional measurements by experts significantly differed from each other for 3/10 parameters. Overall, the variability between AI tools was similar to the conventional methodology, with smaller PAD and higher ICC values noted for 6/10 and 2/10 parameters, respectively. Although different commercial AI-based tools for automated analysis of echocardiographic images do not provide identical results, the variability in the majority of these measurements was similar to or better than that of experts using the conventional methodology. Further studies are needed to ascertain these findings for other AI algorithms and larger numbers of readers and parameters.

Indexed as

Artificial IntelligenceEchocardiography, DopplerHeart DiseasesImage Interpretation, Computer-AssistedAdultAgedAutomationCase-Control StudiesFemaleHumansMaleMiddle AgedObserver VariationPredictive Value of TestsReproducibility of ResultsSoftwareAutomated measurementsEchocardiographic imagingMachine learning

Identifiers

PMID41486249

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Registered trials

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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.