Evidence map›Paper›PMID 35510535›Full record

ArticleMedical physics2022

A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with

Tomohiro Kajikawa, Noriyuki Kadoya, Yosuke Maehara, Hiroshi Miura, Yoshiyuki Katsuta, Shinsuke Nagasawa, Gen Suzuki, Hideya Yamazaki, Nagara Tamaki, Kei Yamada

Abstract read
In one paragraph

Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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  8. Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024
    Article
  9. Article
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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

10 authors.

Tomohiro KajikawaDepartment of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Noriyuki KadoyaDepartment of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
Yosuke MaeharaDepartment of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Hiroshi MiuraDepartment of Radiology, Japanese Red Cross Kyoto Daini Hospital, Kyoto, Japan.
Yoshiyuki KatsutaDepartment of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
Shinsuke NagasawaDepartment of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Gen SuzukiDepartment of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Hideya YamazakiDepartment of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Nagara TamakiDepartment of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Kei YamadaDepartment of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.

Funding

Japan Society of for the Promotion of Science/Grant-in-Aid for Scientific Research (C) 20K16733
6 · The paper itself

Abstract

purposeThis study aimed to evaluate the accuracy of deep learning (DL)-based computed tomography (CT) ventilation imaging (CTVI).

methodsA total of 71 cases that underwent single-photon emission CT

resultsThe average indexes with one SD (1SD) between CTVI

conclusionWe evaluated DL-based framework for estimating lung-functional ventilation images only from CT images. The results indicated that the DL-based approach could potentially be used for lung-ventilation estimation.

Indexed as

Deep LearningFour-Dimensional Computed TomographyImage Processing, Computer-AssistedLungPulmonary VentilationTomography, Emission-Computed, Single-Photondeep learningfunctional imagingradiotherapy

Identifiers

PMID35510535
PMCPMC9545310

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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.