Evidence map›Paper›PMID 38201355›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Feasibility of Encord Artificial Intelligence Annotation of Arterial Duplex Ultrasound Images.

Tiffany R Bellomo, Guillaume Goudot, Srihari K Lella, Eric Landau, Natalie Sumetsky, Nikolaos Zacharias, Chanel Fischetti, Anahita Dua

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 61% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 3 institutions in 1 country.

Tiffany R BellomoDivision of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA.
Guillaume GoudotDivision of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA.ORCID 0000-0003-4327-5568
Srihari K LellaDivision of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA.
Eric LandauEncord, Cord Technologies Inc., New York City, NY 10013, USA.
Natalie SumetskyDivision of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA.
Nikolaos ZachariasDivision of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA.
Chanel FischettiHarvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA.
Anahita DuaDivision of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA.
Harvard University · USMassachusetts General Hospital · USBrigham and Women's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DUS measurements for popliteal artery aneurysms (PAAs) specifically can be time-consuming, error-prone, and operator-dependent. To eliminate this subjectivity and provide efficient segmentation, we applied artificial intelligence (AI) to accurately delineate inner and outer lumen on DUS. DUS images were selected from a cohort of patients with PAAs from a multi-institutional platform. Encord is an easy-to-use, readily available online AI platform that was used to segment both the inner lumen and outer lumen of the PAA on DUS images. A model trained on 20 images and tested on 80 images had a mean Average Precision of 0.85 for the outer polygon and 0.23 for the inner polygon. The outer polygon had a higher recall score than precision score at 0.90 and 0.85, respectively. The inner polygon had a score of 0.25 for both precision and recall. The outer polygon false-negative rate was the lowest in images with the least amount of blur. This study demonstrates the feasibility of using the widely available Encord AI platform to identify standard features of PAAs that are critical for operative decision making.

Indexed as

artificial intelligencedeep learningpopliteal artery aneurysmultrasound

Identifiers

PMID38201355
PMCPMC10795888
OpenAlexW4390234912

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.