Evidence map›Paper›PMID 37817876›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering

Automated Tubular Morphometric Visualization for Whole Kidney Biopsy.

Neil Kavthekar, Brandon Ginley, Samuel Border, Nicholas Lucarelli, Kuang-Yu Jen, Pinaki Sarder

Open access · greenAbstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering. 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
0.1field-weighted citation impact, top 59% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
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

6 authors at 2 institutions in 1 country.

Neil KavthekarDepartments of Biomedical Engineering, University at Buffalo, the State University of New York.
Brandon GinleyPathology & Anatomical Sciences, University at Buffalo, the State University of New York.
Samuel BorderPathology & Anatomical Sciences, University at Buffalo, the State University of New York.
Nicholas LucarelliPathology & Anatomical Sciences, University at Buffalo, the State University of New York.
Kuang-Yu JenDepartment of Pathology and Laboratory Medicine, University of California, Davis.
Pinaki SarderDepartments of Biomedical Engineering, University at Buffalo, the State University of New York.
University at Buffalo, State University of New York · USUniversity of California, Davis · US

Funding

Coordinating and Bioinformatics Unit for the MMPC/DiaCompU24DK076169 · NIDDK · AUGUSTA UNIVERSITY · PI MCINDOE, RICHARD A. · 2006 to 2018
$42.4M
Central Hub for Kidney Precision MedicineU24DK114886 · NIDDK · UNIVERSITY OF WASHINGTON · PI Jonathan Himmelfarb, Matthias Kretzler · 2022 to 2026
$21.1M
ConProject-002U2CDK114886 · NIDDK · UNIVERSITY OF WASHINGTON · PI HIMMELFARB, JONATHAN · 2017 to 2021
$21.0M
Coordinating Unit for DiaCompU24DK115255 · NIDDK · AUGUSTA UNIVERSITY · PI MCINDOE, RICHARD A. · 2017 to 2020
$11.6M
Organ Specific ProjectU54HL145608 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SOLDATOV, RUSLAN · 2018 to 2021
$5.6M
Computational Imaging of Renal Structures for Diagnosing DiabeticNephropathyR01DK114485 · NIDDK · UNIVERSITY OF FLORIDA · PI Michael Thomas Eadon, Avi Z Rosenberg · 2018 to 2026
$4.7M
Rapid quantitative renal fibrosis evaluation with dual-mode microscopyR01DK131189 · NIDDK · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Farzad Fereidouni · 2022 to 2026
$2.7M
NHLBI NIH HHS U54 HL145608NIDDK NIH HHS R01 DK114485NIDDK NIH HHS R01 DK131189NIDDK NIH HHS U24 DK076169NIDDK NIH HHS U24 DK114886NIDDK NIH HHS U24 DK115255NIDDK NIH HHS U2C DK114886
6 · The paper itself

Abstract

One of the strongest prognostic predictors of chronic kidney disease is interstitial fibrosis and tubular atrophy (IFTA). The ultimate goal of IFTA calculation is an estimation of the functional nephritic area. However, the clinical gold standard of estimation by pathologist is imprecise, primarily due to the overwhelming number of tubules sampled in a standard kidney biopsy. Artificial intelligence algorithms could provide significant benefit in this aspect as their high-throughput could identify and quantitatively measure thousands of tubules in mere minutes. Towards this goal, we use a custom panoptic convolutional network similar to Panoptic-DeepLab to detect tubules from 87 WSIs of biopsies from native diabetic kidneys and transplant kidneys. We measure 206 features on each tubule, including commonly understood features like tubular basement membrane thickness and tubular diameter. Finally, we have developed a tool which allows a user to select a range of tubule morphometric features to be highlighted in corresponding WSIs. The tool can also highlight tubules in WSI leveraging multiple morphometric features through selection of regions-of-interest in a uniform manifold approximation and projection plot.

Indexed as

computational image analysismorphometric visualizationpanoptic segmentationTubular atrophy

Identifiers

PMID37817876
PMCPMC10563114
OpenAlexW4220832339

What OpenQuestion holds

Textmetadata
LicenceTDM
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.