Evidence map›Paper›PMID 39570395›Full record

ReviewPathologie (Heidelberg, Germany)2024

Reconstructing 3D histological structures using machine learning (artificial intelligence) algorithms.

J Báskay, M Kivovics, D Pénzes, E Kontsek, A Pesti, A Kiss, M Szócska, O Németh, P Pollner

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pathologie (Heidelberg, Germany), 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. 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

9 authors.

J BáskayDepartment of Biological Physics, Eötvös Loránd University, Pázmány Péter Sétány 1/a, 1117, Budapest, Hungary.
M KivovicsDepartment of Community Dentistry, Semmelweis University, Szentkirályi Utca 40, 1088, Budapest, Hungary.
D PénzesDepartment of Community Dentistry, Semmelweis University, Szentkirályi Utca 40, 1088, Budapest, Hungary.
E KontsekDepartment of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary. kontsek.endre@semmelweis.hu.
A PestiDepartment of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary.
A KissDepartment of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary.
M SzócskaData-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Semmelweis University, Kútvölgyi út 2, 1125, Budapest, Hungary.
O NémethDepartment of Community Dentistry, Semmelweis University, Szentkirályi Utca 40, 1088, Budapest, Hungary.
P PollnerDepartment of Biological Physics, Eötvös Loránd University, Pázmány Péter Sétány 1/a, 1117, Budapest, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHistomorphometry is currently the gold standard for bone microarchitectural examinations. This relies on two-dimensional (2D) sections to deduce the spatial properties of structures. Micromorphometric parameters are calculated from these sections based on the assumption of a plate-like 3D microarchitecture, resulting in the loss of 3D structure due to the destructive nature of classical histological processing. MATERIALS AND

methodsTo overcome the limitation of histomorphometry and reconstruct the 3D architecture of bone core biopsy samples from 2D histological sections, bone core biopsy samples were decalcified and embedded in paraffin. Subsequently, 5 µm thick serial sections were stained with hematoxylin and eosin and scanned using a 3DHISTECH PANNORAMIC 1000 Digital Slide Scanner (3DHISTECH, Budapest, Hungary). A modified U‑Net architecture was trained to categorize tissues on the sections. LoFTR feature matching combined with affine transformations was employed to create the histologic reconstruction. Micromorphometric parameters were calculated using Bruker's CTAn software (v. 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets.

resultsOur method achieved an overall accuracy of 95.26% (95% confidence interval (CI): [94.15%, 96.37%]) with an F‑score of 0.9320 (95% CI: [0.9211, 0.9429]) averaged across all classes. Correlation coefficients between micromorphometric parameters measured on microCT imaging and histological reconstruction showed a strong linear relationship, with Spearman's ρ‑values of 0.777, 0.717, 0.705, 0.666, and 0.687 for bone volume/tissue volume (BV/TV), bone surface/TV, trabecular pattern factor, trabecular thickness, and trabecular separation, respectively. Bland-Altman and mountain plots indicated good agreement between the methods for BV/TV measurements.

conclusionThis method enables examination of tissue microarchitecture in 3D with an even higher resolution than microcomputed tomography (microCT), without losing information on cellularity. However, the limitation of this procedure is its destructive nature, which precludes subsequent mechanical testing of the sample or any further secondary measurements. Furthermore, the number of histological sections that can be created from a single sample is limited.

Indexed as

Imaging, Three-DimensionalMachine LearningAlgorithmsArtificial IntelligenceBone and BonesHumansX-Ray MicrotomographyCancellous boneDental implantationHistologyNeural networks, computerSensitivity and specificity

Identifiers

PMID39570395

What OpenQuestion holds

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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.