Evidence map›Paper›PMID 36711704›Full record

ArticlebioRxiv : the preprint server for biology2023

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.

Daniel R Wong, Shino D Magaki, Harry V Vinters, William H Yong, Edwin S Monuki, Christopher K Williams, Alessandra C Martini, Charles DeCarli, Chris Khacherian, John P Graff and 2 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 1 country.

Daniel R WongInstitute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, 94158, USA.
Shino D MagakiDepartment of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Harry V VintersDepartment of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
William H YongDepartment of Pathology & Laboratory Medicine, University of California, Irvine, CA 92697, USA.
Edwin S MonukiDepartment of Pathology & Laboratory Medicine, University of California, Irvine, CA 92697, USA.
Christopher K WilliamsDepartment of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Alessandra C MartiniDepartment of Pathology & Laboratory Medicine, University of California, Irvine, CA 92697, USA.
Charles DeCarliDepartment of Neurology, School of Medicine, University of California-Davis, Davis, CA 95817, USA.
Chris KhacherianDepartment of Pathology & Laboratory Medicine, University of California, Irvine, CA 92697, USA.
John P GraffDepartment of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA.
Brittany N DuggerDepartment of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA.
Michael J KeiserInstitute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, 94158, USA.
University of California, Irvine · USUniversity of California, Davis · USUniversity of California, Los Angeles · USUniversity of California, San Francisco · US

Funding

UC Davis Alzheimer's Disease Core CenterP30AG010129 · NIA · UNIVERSITY OF CALIFORNIA DAVIS · PI DECARLI, CHARLES · 1991 to 2020
$28.3M
UC Davis Alzheimer's Disease Research CenterP30AG072972 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Charles DeCarli, Rachel A Whitmer · 2021 to 2026
$25.2M
The Neuropathologic Landscape of Alzheimer's Disease in Hispanic DecedentsR01AG062517 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Brittany Nicole Dugger · 2019 to 2026
$10.5M
NIA NIH HHS P30 AG010129NIA NIH HHS P30 AG072972NIA NIH HHS R01 AG062517
6 · The paper itself

Abstract

Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathies (CAAs). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes. We evaluated the detector on a new manually-annotated dataset of whole slide images (WSIs) from three institutions, four staining procedures, and four human experts. The detector matched the cohort of neuropathology experts, achieving 0.64 (model) vs. 0.64 (cohort) average precision (AP) for cored plaques and 0.75 vs. 0.51 AP for CAAs at a 0.5 IOU threshold. It provided count and locality predictions that correlated with gold-standard CERAD-like WSI scoring (p=0.07± 0.10). The openly-available model can quickly score WSIs in minutes without a GPU on a standard workstation.

Identifiers

PMID36711704
PMCPMC9882138
OpenAlexW4317392721

What OpenQuestion 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 OpenQuestion graph.