Evidence map›Paper›PMID 38906615›Full record

ArticleThe Lancet. Digital health2024

Automated cooling tower detection through deep learning for Legionnaires' disease outbreak investigations: a model development and validation study.

Karen K Wong, Thaddeus Segura, Gunnar Mein, Jia Lu, Elizabeth J Hannapel, Jasen M Kunz, Troy Ritter, Jessica C Smith, Alberto Todeschini, Fred Nugen and 1 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet. Digital health, 2024. 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
–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

1 citing paper in PubMed.

  1. Preliminary report: The most considerable outbreak of Legionnaires' disease in France in the last two decades, Albertville, September 2025.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025
    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.

Karen K WongCenters for Disease Control and Prevention, Atlanta, GA, USA; University of California, Berkeley, CA, USA. Electronic address: kwong.cdc@gmail.com.
Thaddeus SeguraUniversity of California, Berkeley, CA, USA.
Gunnar MeinUniversity of California, Berkeley, CA, USA.
Jia LuUniversity of California, Berkeley, CA, USA.
Elizabeth J HannapelCenters for Disease Control and Prevention, Atlanta, GA, USA.
Jasen M KunzCenters for Disease Control and Prevention, Atlanta, GA, USA.
Troy RitterCenters for Disease Control and Prevention, Atlanta, GA, USA.
Jessica C SmithCenters for Disease Control and Prevention, Atlanta, GA, USA.
Alberto TodeschiniUniversity of California, Berkeley, CA, USA.
Fred NugenUniversity of California, Berkeley, CA, USA.
Chris EdensCenters for Disease Control and Prevention, Atlanta, GA, USA.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

backgroundCooling towers containing Legionella spp are a high-risk source of Legionnaires' disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible.

methodsBetween Jan 1 and 31, 2021, we extracted satellite view images of Philadelphia (PN, USA) and New York state (NY, USA) from Google Maps and annotated cooling towers to create training datasets. We augmented training data with synthetic data and model-assisted labelling of additional cities. Using 2051 images containing 7292 cooling towers, we trained a two-stage model using YOLOv5, a model that detects objects in images, and EfficientNet-b5, a model that classifies images. We assessed the primary outcomes of sensitivity and positive predictive value (PPV) of the model against manual labelling on test datasets of 548 images, including from two cities not seen in training (Boston [MA, USA] and Athens [GA, USA]). We compared the search speed of the model with that of manual searching by four epidemiologists.

findingsThe model identified visible cooling towers with 95·1% sensitivity (95% CI 94·0-96·1) and a PPV of 90·1% (95% CI 90·0-90·2) in New York City and Philadelphia. In Boston, sensitivity was 91·6% (89·2-93·7) and PPV was 80·8% (80·5-81·2). In Athens, sensitivity was 86·9% (75·8-94·2) and PPV was 85·5% (84·2-86·7). For an area of New York City encompassing 45 blocks (0·26 square miles), the model searched more than 600 times faster (7·6 s; 351 potential cooling towers identified) than did human investigators (mean 83·75 min [SD 29·5]; mean 310·8 cooling towers [42·2]).

interpretationThe model could be used to accelerate investigation and source control during outbreaks of Legionnaires' disease through the identification of cooling towers from aerial imagery, potentially preventing additional disease spread. The model has already been used by public health teams for outbreak investigations and to initialise cooling tower registries, which are considered best practice for preventing and responding to outbreaks of Legionnaires' disease.

fundingNone.

Identifiers

PMID38906615
PMCPMC12038768

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LicenceCC BY-NC-ND
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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.