Machine learning improves early detection of liver fibrosis by quantitative ultrasound radiomics.
- DOI
- 10.1109/ius54386.2022.9957180
- Published
- 2022 Oct
- Container
- IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1109/ius54386.2022.9957180,
title = {Machine learning improves early detection of liver fibrosis by quantitative ultrasound radiomics.},
author = {Al-Hasani M and Sultan LR and Sagreiya H and Cary TW and Karmacharya MB and Sehgal CM},
year = {2022},
journal = {IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium},
doi = {10.1109/ius54386.2022.9957180},
url = {https://doi.org/10.1109/ius54386.2022.9957180}
}RIS
TY - JOUR TI - Machine learning improves early detection of liver fibrosis by quantitative ultrasound radiomics. AU - Al-Hasani M AU - Sultan LR AU - Sagreiya H AU - Cary TW AU - Karmacharya MB AU - Sehgal CM PY - 2022 JO - IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium DO - 10.1109/ius54386.2022.9957180 UR - https://doi.org/10.1109/ius54386.2022.9957180 ER -
APA
M, A., LR, S., H, S., TW, C., MB, K., & CM, S. (2022). Machine learning improves early detection of liver fibrosis by quantitative ultrasound radiomics.. IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium. https://doi.org/10.1109/ius54386.2022.9957180
Source records
- pubmed · retrieved 2026-09-26T07:27:02.088Z