AI-Enhanced Deep Learning Framework for Pulmonary Embolism Detection in CT Angiography.

Nan-Han Lu, Chi-Yuan Wang, Kuo-Ying Liu, Yung-Hui Huang, Tai-Been Chen

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. 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.

Artificial intelligence in computed tomography imaging for pulmonary embolism: a narrative review from computed tomography pulmonary angiography to non-contrast computed tomography.Quantitative imaging in medicine and surgery · 2026
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

5 authors.

Nan-Han LuDepartment of Radiology, E-DA Cancer Hospital, I-Shou University, No. 21, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung 82445, Taiwan.ORCID 0009-0002-1881-3504

Chi-Yuan WangDepartment of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung 82445, Taiwan.

Kuo-Ying LiuDepartment of Radiology, E-DA Cancer Hospital, I-Shou University, No. 21, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung 82445, Taiwan.ORCID 0000-0002-4661-0248

Yung-Hui HuangDepartment of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung 82445, Taiwan.ORCID 0000-0003-3808-9998

Tai-Been ChenDepartment of Radiological Technology, Teikyo University, Tokyo 173-8605, Japan.

Funding

EDA Hospital Taiwan EDCHP114003National Science and Technology Council in Taiwan NSTC 113-2221-E-214-007

6 · The paper itself

Abstract

Pulmonary embolism (PE) on CT pulmonary angiography (CTPA) demands rapid, accurate assessment, yet small, low-contrast clots in distal arteries remain challenging. We benchmarked ten fully convolutional network (FCN) backbones and introduced Consensus Intersection-Optimized Fusion (CIOF)-a K-of-M, pixel-wise mask fusion with the voting threshold

Indexed as

consensus intersection-optimized fusion (CIOF)CT pulmonary angiographydeep learningensemble segmentationmedical imagingpulmonary embolism

Identifiers

PMID41155054

DOI10.3390/bioengineering12101055

PMCPMC12561262

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Registered trials

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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.