Evidence map›Paper›PMID 41162986›Full record

ArticleBMC pulmonary medicine2025

New lung ultrasound system for rapid triage of pulmonary disease without a radiologist or sonographer.

Thomas J Marini, Ajeesh Ajayan Nayaruparambil, Timothy M Baran, Thapasya Murali, Stephen Drury, Yu T Zhao, Katherine Kaproth-Joslin, Robert Ambrosini, Sean Cleary, Stan L Weiss and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pulmonary medicine, 2025. 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
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.

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

14 authors.

Thomas J MariniUniversity of Rochester Medical Center, 601 Elmwood Avenue, Box 648, Rochester, NY, 14642, USA. Rochesterradiology2021@gmail.com.
Ajeesh Ajayan NayaruparambilGoergen Institute for Data Science and Artificial Intelligence, University of Rochester, Wegmans Hall, 250 Hutchison Road, Rochester, NY, 14620, USA.
Timothy M BaranUniversity of Rochester Medical Center, 601 Elmwood Avenue, Box 648, Rochester, NY, 14642, USA.
Thapasya MuraliGoergen Institute for Data Science and Artificial Intelligence, University of Rochester, Wegmans Hall, 250 Hutchison Road, Rochester, NY, 14620, USA.
Stephen DruryGoergen Institute for Data Science and Artificial Intelligence, University of Rochester, Wegmans Hall, 250 Hutchison Road, Rochester, NY, 14620, USA.
Yu T ZhaoUniversity of Rochester Medical Center, 601 Elmwood Avenue, Box 648, Rochester, NY, 14642, USA.
Katherine Kaproth-JoslinUniversity of Rochester Medical Center, 601 Elmwood Avenue, Box 648, Rochester, NY, 14642, USA.
Robert AmbrosiniUniversity of Rochester Medical Center, 601 Elmwood Avenue, Box 648, Rochester, NY, 14642, USA.
Sean ClearyUniversity of Rochester Medical Center, 601 Elmwood Avenue, Box 648, Rochester, NY, 14642, USA.
Stan L WeissUniversity of Rochester Medical Center, 601 Elmwood Avenue, Box 648, Rochester, NY, 14642, USA.
Alex KesslerUniversity of Rochester Medical Center, 601 Elmwood Avenue, Box 648, Rochester, NY, 14642, USA.
Benjamin CastanedaDepartment of Biomedical Engineering, University of Rochester, 204 Robert B. Goergen Hall, P.O. Box 270168, Rochester, NY, 14627, USA.
Maria Clara LorcaMassachusetts General Hospital, 55 Fruit Street, Austen 202, Boston, MA, 02114, USA.
Ajay AnandGoergen Institute for Data Science and Artificial Intelligence, University of Rochester, Wegmans Hall, 250 Hutchison Road, Rochester, NY, 14620, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMost people in the world lack access to medical imaging including for assessment of pulmonary disease. We sought to improve access to pulmonary imaging by developing a rapid automated system for triage of pulmonary disease using lung ultrasound requiring neither a radiologist nor experienced sonographer utilizing volume sweep imaging (VSI) and artificial intelligence (AI).

methodsWe conducted a retrospective study of lung ultrasound VSI data collected from May 2019 to January 2020. AI analysis utilizing a convolutional neural network and random forest-based machine learning classifier was performed on 70 normal lung ultrasound VSI video clips and 49 abnormal lung ultrasound VSI video clips obtained by individuals without prior ultrasound experience. The accuracy of the AI was assessed for the ability to distinguish between normal and abnormal lung ultrasound video clips.

resultsAmong test VSI clips (n = 36 clips), AI achieved 91.7% accuracy, 85.7% sensitivity, 95.5% specificity, and an F1 score of 0.89 for an abnormal lung ultrasound VSI clip. Among test subjects (n = 20) from which these clips were obtained, 90.0% accuracy, 87.5% sensitivity, 91.7% specificity, and an F1 score of 0.88 were achieved.

conclusionsLung ultrasound VSI integrated with AI shows potential to provide preliminary triage of pulmonary disease allowing a system for rapid automatic triage of pulmonary disease requiring neither a radiologist nor sonographer.

Indexed as

LungLung DiseasesTriageAgedArtificial IntelligenceFemaleHumansMachine LearningMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityUltrasonographyArtificial intelligenceLung ultrasoundPleural effusionPneumoniaPulmonary edema

Identifiers

PMID41162986
PMCPMC12573963

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.