Evidence map›Paper›PMID 36683096›Full record

ReviewVisual computing for industry, biomedicine, and art2023

Preliminary landscape analysis of deep tomographic imaging patents.

Qingsong Yang, Donna L Lizotte, Wenxiang Cong, Ge Wang

Abstract readReview
In one paragraph

Review in Visual computing for industry, biomedicine, and art, 2023. 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

4 authors.

Qingsong YangRensselaer Polytechnic Institute, Troy, NY, 12180, USA.
Donna L LizotteMurtha Cullina LLP, Boston, MA, 02110, USA.
Wenxiang CongRensselaer Polytechnic Institute, Troy, NY, 12180, USA.
Ge WangRensselaer Polytechnic Institute, Troy, NY, 12180, USA. wangg6@rpi.edu.ORCID http://orcid.org/0000-0002-2656-7705

Funding

High dose efficiency CT SystemU01EB017140 · NIBIB · STANFORD UNIVERSITY · PI EDIC, PETER, PELC, NORBERT J. · 2014 to 2018
$5.4M
Cardiac CT DebloomingR01HL151561 · NHLBI · GENERAL ELECTRIC GLOBAL RESEARCH CTR · PI BUDOFF, MATTHEW J, DE MAN, BRUNO · 2020 to 2023
$3.8M
Development and Validation of Prognostic Radiomic Markers of Response and Recurrence for Patients with Colorectal Liver MetastasesR01CA233888 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI CHUN, YUN SHIN, DO, RICHARD KINH GIAN · 2019 to 2023
$3.7M
Photon-counting X-ray and Optical Tomography for Preclinical Cancer ResearchR01CA237267 · NCI · RENSSELAER POLYTECHNIC INSTITUTE · PI BARROSO, MARGARIDA, INTES, XAVIER · 2019 to 2023
$2.9M
Life time imaging with pulsed x-ray based x-ray luminescence computed tomographyR01EB026646 · NIBIB · UNIVERSITY OF CALIFORNIA, MERCED · PI LI, CHANGQING · 2018 to 2021
$2.6M
National Institutes of Health (NIH) R01CA233888National Institutes of Health (NIH) R01CA237267National Institutes of Health (NIH) R01EB026646National Institutes of Health (NIH) R01HL151561National Institutes of Health (NIH) U01EB017140
6 · The paper itself

Abstract

Over recent years, the importance of the patent literature has become increasingly more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature. Our search tool is PatSeer. Our patent bibliometric data is summarized in various figures and tables. In particular, we qualitatively analyze key deep tomographic patent literature.

Indexed as

Artificial intelligenceDeep learningImage reconstructionMachine learningMedical imagingTomography

Identifiers

PMID36683096
PMCPMC9868030

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.