Evidence map›Paper›PMID 36010337›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Automated Identification of Coronary Arteries in Assisting Inexperienced Readers: Comparison between Two Commercial Vendors.

Domenico De Santis, Giuseppe Tremamunno, Carlotta Rucci, Tiziano Polidori, Marta Zerunian, Giulia Piccinni, Luca Pugliese, Benedetta Masci, Nicolò Ubaldi, Andrea Laghi and 1 more

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. 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
0.1field-weighted citation impact, top 53% 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. Article
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

11 authors at 1 institution in 1 country.

Domenico De SantisDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.ORCID 0000-0002-1197-456X
Giuseppe TremamunnoDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.ORCID 0000-0001-6484-9765
Carlotta RucciDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.
Tiziano PolidoriDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.
Marta ZerunianDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.ORCID 0000-0003-4635-7999
Giulia PiccinniDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.
Luca PuglieseDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.
Benedetta MasciDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.ORCID 0000-0003-0935-6364
Nicolò UbaldiDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.
Andrea LaghiDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.ORCID 0000-0002-3091-7819
Damiano CarusoDepartment of Medical Surgical Sciences and Translational Medicine-Sapienza, University of Rome, Radiology Unit-Sant'Andrea University Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.ORCID 0000-0001-9285-4764
Sapienza University of Rome · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: to assess the performance and speed of two commercially available advanced cardiac software packages in the automated identification of coronary vessels as an aiding tool for inexperienced readers. Methods: Hundred and sixty patients undergoing coronary CT angiography (CCTA) were prospectively enrolled from February until September 2021 and randomized in two groups, each one composed by 80 patients. Patients in group 1 were scanned on Revolution EVO CT Scanner (GE Healthcare), while patients in group 2 had the CCTA performed on Brilliance iCT (Philips Healthcare); each examination was evaluated on the respective vendor proprietary advanced cardiac software (software 1 and 2, respectively). Two inexperienced readers in cardiac imaging verified the software performance in the automated identification of the three major coronary vessels: (RCA, LCx, and LAD) and in the number of identified coronary segments. Time of analysis was also recorded. Results: software 1 correctly and automatically nominated 202/240 (84.2%) of the three main coronary vessels, while software 2 correctly identified 191/240 (79.6%) (p = 0.191). Software 1 achieved greater performances in recognizing the LCx (81.2% versus 67.5%; p = 0.048), while no differences have been reported in detecting the RCA (p = 0.679), and the LAD (p = 0.618). On a per-segment analysis, software 1 outperformed software 2, automatically detecting 942/1062 (88.7%) coronary segments, while software 2 detected 797/1078 (73.9%) (p < 0.001). Average reconstruction and detection time was of 13.8 s for software 1 and 21.9 s for software 2 (p < 0.001). Conclusions: automated cardiac software packages are a reliable and time-saving tool for inexperienced reader. Software 1 outperforms software 2 and might therefore better assist inexperienced CCTA readers in automated identification of the three main vessels and coronaries segments, with a consistent time saving of the reading session.

Indexed as

automated coronary analysisCCTAcoronary arteriescoronary artery disease

Identifiers

PMID36010337
PMCPMC9406865
OpenAlexW4292291635

What OpenQuestion 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 OpenQuestion graph.