Evidence map›Paper›PMID 40534634›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024

DECODING RADIOLOGISTS' INTENTIONS: A NOVEL SYSTEM FOR ACCURATE REGION IDENTIFICATION IN CHEST X-RAY IMAGE ANALYSIS.

Akash Awasthi, Safwan Ahmad, Bryant Le, Hien Nguyen

Abstract read
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. 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. 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

4 authors.

Akash AwasthiElectrical and Computer Engineering Department, University of Houston.
Safwan AhmadElectrical and Computer Engineering Department, University of Houston.
Bryant LeElectrical and Computer Engineering Department, University of Houston.
Hien NguyenElectrical and Computer Engineering Department, University of Houston.

Funding

SCH: AI-DOCTOR COLLABORATIVE MEDICAL DIAGNOSISR01CA277739 · NCI · UNIVERSITY OF HOUSTON · PI NGUYEN, HIEN VAN · 2022 to 2025
$929k
NCI NIH HHS R01 CA277739
6 · The paper itself

Abstract

In the realm of chest X-ray (CXR) image analysis, radiologists meticulously examine various regions, documenting their observations in reports. The prevalence of errors in CXR diagnoses, particularly among inexperienced radiologists and hospital residents, underscores the importance of understanding radiologists' intentions and the corresponding regions of interest. This understanding is crucial for correcting mistakes by guiding radiologists to the accurate regions of interest, especially in the diagnosis of chest radiograph abnormalities. In response to this imperative, we propose a novel system designed to identify the primary intentions articulated by radiologists in their reports and the corresponding regions of interest in CXR images. This system seeks to elucidate the visual context underlying radiologists' textual findings, with the potential to rectify errors made by less experienced practitioners and direct them to precise regions of interest. Importantly, the proposed system can be instrumental in providing constructive feedback to inexperienced radiologists or junior residents in the hospital, bridging the gap in face-to-face communication. The system represents a valuable tool for enhancing diagnostic accuracy and fostering continuous learning within the medical community.

Indexed as

DVCIntenionsTGID

Identifiers

PMID40534634
PMCPMC12176413

What Socratic 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 Socratic graph.