Evidence map›Paper›PMID 39677271›Full record

ArticleCureus2024

Nth Slice Eraser: An Automated Algorithm to Ease Editing Workflow of Organ Contours Generated by Artificial Intelligence.

Arjun Karnwal, Brittney Chau, Blake Chang, Marios P Tsotras, Colin Yeo, Arthur J Olch, Kenneth Wong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cureus, 2024. 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.

No citing paper in PubMed yet.

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

7 authors.

Arjun KarnwalAlfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, USA.
Brittney ChauDepartment of Radiation Oncology, University of Southern California Keck School of Medicine, Los Angeles, USA.
Blake ChangCollege of Humanities and Sciences, Stanford University, Los Angeles, USA.
Marios P TsotrasThomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA.
Colin YeoMedical School, University of Southern California Keck School of Medicine, Los Angeles, USA.
Arthur J OlchDepartment of Radiation Oncology, University of Southern California Keck School of Medicine, Los Angeles, USA.
Kenneth WongDepartment of Radiation Oncology, University of Southern California Keck School of Medicine, Los Angeles, USA.

Funding

USC/CHLA Summer Oncology Research Fellowship (SORF) Program for Medical StudentsR25CA225513 · NCI · CHILDREN'S HOSPITAL OF LOS ANGELES · PI ANAT ERDREICH-EPSTEIN, WIJBE MARTIN KAST · 2019 to 2026
$1.7M
NCI NIH HHS R25 CA225513
6 · The paper itself

Abstract

The time-consuming process of manual contouring of healthy tissue and organs in radiation therapy has prompted the development of computational systems to aid and automate this process, such as artificial intelligence (AI) segmentation and interpolation algorithms. These algorithms are useful in saving time, however, they are not always accurate. Fixing such inaccuracies by editing contours is a manual, time-consuming process as no 'undo' feature currently exists in the most commonly used treatment planning system (TPS). In this short technical report, we present our script for automated de-interpolation to enable faster editing of contours to streamline the adoption of AI-assisted methodologies to enhance clinical workflow.

Indexed as

deep learning artificial intelligenceeclipseradiation oncologyradiation oncology trainingradiation therapy contouringtreatment planning automation

Identifiers

PMID39677271
PMCPMC11645465

What OpenQuestion holds

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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.