Evidence map›Paper›PMID 41400419›Full record

ArticleMicrobiology spectrum2026

Advancing mold identification in the routine laboratory, performance of smartphone-based imaging and a newly developed convolutional neural network.

Lukas Weber, Sarah Brueningk, Bettina Schulthess, Gloria Stillhart, Michelle Bressan, Nadja Pulver-Vontobel, Jasmin Schimetzki, Rosmi Puthan, Andrea Egli-Berini, Oliver Nolte and 1 more

Abstract read
In one paragraph

Article in Microbiology spectrum, 2026. 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

11 authors.

Lukas WeberInstitute of Medical Microbiology, University of Zurich, Zurich, Switzerland.ORCID 0009-0006-3861-1263
Sarah BrueningkDepartment of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.ORCID 0000-0003-3176-1032
Bettina SchulthessInstitute of Medical Microbiology, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-1562-4583
Gloria StillhartInstitute of Medical Microbiology, University of Zurich, Zurich, Switzerland.
Michelle BressanInstitute of Medical Microbiology, University of Zurich, Zurich, Switzerland.
Nadja Pulver-VontobelInstitute of Medical Microbiology, University of Zurich, Zurich, Switzerland.
Jasmin SchimetzkiInstitute of Medical Microbiology, University of Zurich, Zurich, Switzerland.
Rosmi PuthanInstitute of Medical Microbiology, University of Zurich, Zurich, Switzerland.
Andrea Egli-BeriniBildungszentrum Gesundheit, Basel-Stadt, Switzerland.
Oliver NolteInstitute of Medical Microbiology, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-1761-0812
Adrian EgliInstitute of Medical Microbiology, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-3564-8603

Funding

Swiss National Science Foundation 310030_213019
6 · The paper itself

Abstract

Mold identification in clinical diagnostics is traditionally labor-intensive and is dependent on expert interpretation. MoldVision is a deep-learning approach that uses smartphone images of mold cultures to automate identification. We analyzed 161 clinical isolates across four common mold genera:

Indexed as

FungiImage Processing, Computer-AssistedMycosesNeural Networks, ComputerSmartphoneAspergillusCladosporiumConvolutional Neural NetworksDeep LearningFusariumHumansPenicilliumartificial intelligencedeep learningdiagnostic mycologymachine learningmycology

Identifiers

PMID41400419
PMCPMC12889110

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.