Evidence map›Paper›PMID 36928805›Full record

ArticlePloS one2023

Artificial intelligence-based point-of-care lung ultrasound for screening COVID-19 pneumoniae: Comparison with CT scans.

Yumi Kuroda, Tomohiro Kaneko, Hitomi Yoshikawa, Saori Uchiyama, Yuichi Nagata, Yasushi Matsushita, Makoto Hiki, Tohru Minamino, Kazuhisa Takahashi, Hiroyuki Daida and 1 more

Open access · goldFull text read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
6.6field-weighted citation impact, top 2% 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

21 citing papers in PubMed, 31 citations in OpenAlex.

  1. Trial
  2. Review
  3. Review
  4. AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026
    Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Chest ultrasound for lung cancer: present and future.Journal of medical ultrasonics (2001) · 2024
    Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Artificial intelligence in respiratory care.Frontiers in digital health · 2024
    Review
  20. 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.

Yumi KurodaDepartment of Respiratory Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.
Tomohiro KanekoDepartment of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.
Hitomi YoshikawaDepartment of Respiratory Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.
Saori UchiyamaDepartment of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.
Yuichi NagataDepartment of Respiratory Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.
Yasushi MatsushitaDepartment of Emergency Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.
Makoto HikiDepartment of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.
Tohru MinaminoDepartment of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.ORCID 0000-0003-1627-6151
Kazuhisa TakahashiDepartment of Respiratory Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.
Hiroyuki DaidaDepartment of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.
Nobuyuki KagiyamaDepartment of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan.ORCID 0000-0002-2305-0049
Juntendo University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough lung ultrasound has been reported to be a portable, cost-effective, and accurate method to detect pneumonia, it has not been widely used because of the difficulty in its interpretation. Here, we aimed to investigate the effectiveness of a novel artificial intelligence-based automated pneumonia detection method using point-of-care lung ultrasound (AI-POCUS) for the coronavirus disease 2019 (COVID-19).

methodsWe enrolled consecutive patients admitted with COVID-19 who underwent computed tomography (CT) in August and September 2021. A 12-zone AI-POCUS was performed by a novice observer using a pocket-size device within 24 h of the CT scan. Fifteen control subjects were also scanned. Additionally, the accuracy of the simplified 8-zone scan excluding the dorsal chest, was assessed. More than three B-lines detected in one lung zone were considered zone-level positive, and the presence of positive AI-POCUS in any lung zone was considered patient-level positive. The sample size calculation was not performed given the retrospective all-comer nature of the study.

resultsA total of 577 lung zones from 56 subjects (59.4 ± 14.8 years, 23% female) were evaluated using AI-POCUS. The mean number of days from disease onset was 9, and 14% of patients were under mechanical ventilation. The CT-validated pneumonia was seen in 71.4% of patients at total 577 lung zones (53.3%). The 12-zone AI-POCUS for detecting CT-validated pneumonia in the patient-level showed the accuracy of 94.5% (85.1%- 98.1%), sensitivity of 92.3% (79.7%- 97.3%), specificity of 100% (80.6%- 100%), positive predictive value of 95.0% (89.6% - 97.7%), and Kappa of 0.33 (0.27-0.40). When simplified with 8-zone scan, the accuracy, sensitivity, and sensitivity were 83.9% (72.2%- 91.3%), 77.5% (62.5%- 87.7%), and 100% (80.6%- 100%), respectively. The zone-level accuracy, sensitivity, and specificity of AI-POCUS were 65.3% (61.4%- 69.1%), 37.2% (32.0%- 42.7%), and 97.8% (95.2%- 99.0%), respectively.

interpretationAI-POCUS using the novel pocket-size ultrasound system showed excellent agreement with CT-validated COVID-19 pneumonia, even when used by a novice observer.

Indexed as

COVID-19PneumoniaArtificial IntelligenceFemaleHumansLungMalePoint-of-Care SystemsRetrospective StudiesTomography, X-Ray Computed

Identifiers

PMID36928805
PMCPMC10019704
OpenAlexW4327566817

What OpenQuestion holds

Textfull text, public
LicenceCC BY
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identifiers read7
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.