Evidence map›Paper›PMID 38177496›Full record

ArticleInternational journal of legal medicine2024

A new, deep learning-based method for the analysis of autopsy kidney samples used to study sex differences in glomerular density and size in a forensic population.

Valérie Vilmont, Nadine Ngatchou, Ghislaine Lioux, Sabrina Kalucki, Wendy Brito, Michel Burnier, Samuel Rotman, Christelle Lardi, Menno Pruijm

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Article in International journal of legal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

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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

9 authors.

Valérie VilmontService of Nephrology and Hypertension, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 17, 1011, Lausanne, Switzerland.
Nadine NgatchouService of Nephrology and Hypertension, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 17, 1011, Lausanne, Switzerland.
Ghislaine LiouxIndica Labs, Albuquerque, NM, USA.
Sabrina KaluckiService of Nephrology and Hypertension, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 17, 1011, Lausanne, Switzerland.
Wendy BritoService of Nephrology and Hypertension, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 17, 1011, Lausanne, Switzerland.
Michel BurnierService of Nephrology and Hypertension, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 17, 1011, Lausanne, Switzerland.
Samuel RotmanService of Clinical Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Christelle LardiUniversity Center of Legal Medicine Geneva, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.
Menno PruijmService of Nephrology and Hypertension, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 17, 1011, Lausanne, Switzerland. menno.pruijm@chuv.ch.ORCID http://orcid.org/0000-0003-1027-1851

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung SNF 320030-169191
6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly used in forensic anthropology and genetics to identify the victim and the cause of death. The large autopsy samples from persons with traumatic causes of death but without comorbidities also offer possibilities to analyze normal histology with AI. We propose a new deep learning-based method to rapidly count glomerular number and measure glomerular density (GD) and volume in post-mortem kidney samples obtained in a forensic population. We assessed whether this new method detects glomerular differences between men and women without known kidney disease. Autopsies performed between 2009 and 2015 were analyzed if subjects were aged ≥ 18 years and had no known kidney disease, diabetes mellitus, or hypertension. A large biopsy was taken from each kidney, stained with hematoxylin and eosin, and scanned. An in-house developed deep learning-based algorithm counted the glomerular density (GD), number, and size. Out of 1165 forensic autopsies, 86 met all inclusion criteria (54 men). Mean (± SD) age was 43.5 ± 14.6; 786 ± 277 glomeruli were analyzed per individual. There was no significant difference in GD between men and women (2.18 ± 0.49 vs. 2.30 ± 0.57 glomeruli/mm

Indexed as

Deep LearningKidney DiseasesArtificial IntelligenceAutopsyFemaleHumansKidneyMaleSex CharacteristicsArtificial intelligenceForensic medicineGlomerular densityHistologyKidney autopsyNephron numberSex

Identifiers

PMID38177496
PMCPMC11003899

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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.