ArticleBioinformation2020
An ANN model for the differential diagnosis of tuberculosis and sarcoidosis.
Article in Bioinformation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A Review of Machine Learning Algorithms for Biomedical Applications.Annals of biomedical engineering · 2024Review
- Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques.Sensors (Basel, Switzerland) · 2022Article
- BATF2 and PDK4 as diagnostic molecular markers of sarcoidosis and their relationship with immune infiltration.Annals of translational medicine · 2022Article
- Development of a random forest model to classify sarcoidosis and tuberculosis.American journal of translational research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sarcoidosis is often misdiagnosed as tuberculosis and consequently mistreated owing to inherent limitations in histopathological and radiological presentations. It is known that the differential diagnosis of Tuberculosis and Sarcoidosis is often non-trivial and requires expertise and experience from clinicians. Therefore, it is of interest to describe a multilayer neural network model to differentiate pulmonary tuberculosis from Sarcoidosis using signal intensity data from blood transcriptional microarray. Genes that are significantly upregulated in Pulmonary Tuberculosis and Sarcoidosis in comparison with healthy controls were used in the model. The model classified Pulmonary Tuberculosis and Sarcoidosis with 95.8% accuracy. The model also helps to identify gene markers that are differentially upregulated in the two clinical conditions.
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Registered trials
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.