Evidence map›Paper›PMID 36553187›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics.

Meng-Chiung Lin, Vincent S Tseng, Chih-Sheng Lin, Shao-Wen Chiu, Lung-Kwang Pan, Lung-Fa Pan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

6 authors.

Meng-Chiung LinDepartment of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 10041, Taiwan.
Vincent S TsengDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 10041, Taiwan.
Chih-Sheng LinDepartment of Radiology, BenQ Medical Center, The Affiliated BenQ Hospital of the Nanjing Medical University, Nanjing 210000, China.
Shao-Wen ChiuDepartment of Pet Business Management, Taipei University of Marine Technology, Taipei 10001, Taiwan.
Lung-Kwang PanDepartment of Medical Imaging and Radiological Science, Central Taiwan University of Science and Technology, Taichung 40044, Taiwan.ORCID 0000-0002-9485-4722
Lung-Fa PanDepartment of Medical Imaging and Radiological Science, Central Taiwan University of Science and Technology, Taichung 40044, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quantitative prediction of the SYNTAX score for cardiovascular artery disease patients using the inverse problem algorithm (IPA) technique in artificial intelligence was explored in this study. A 29-term semi-empirical formula was defined according to seven risk factors: (1) age, (2) mean arterial pressure, (3) body surface area, (4) pre-prandial blood glucose, (5) low-density-lipoprotein cholesterol, (6) Troponin I, and (7) C-reactive protein. Then, the formula was computed via the STATISTICA 7.0 program to obtain a compromised solution for a 405-patient dataset with a specific loss function [actual-predicted]

Indexed as

artificial intelligencecardiovascular artery diseasecomputational analysisinverse problem algorithmSYNTAX

Identifiers

PMID36553187
PMCPMC9777487

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.