ArticleACS sensors2025
Leveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data.
Article in ACS sensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Colorimetric/chemiluminescent vertical flow immunoassay based on multifunctional cobalt-gold bimetallic nanoclusters for Brucella antibody detection and deep learning-assisted semiquantitative analysis.Mikrochimica acta · 2026Article
- The evolution of next-generation lateral flow assays for bacterial and fungal diagnostics.Mikrochimica acta · 2026Review
- Nanozymes Integrated Biochips Toward Smart Detection System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent AI-driven disease diagnosis, the success of models has depended mainly on extensive data sets and advanced algorithms. However, creating traditional data sets for rare or emerging diseases presents significant challenges. To address this issue, this study introduces a direct-self-attention Wasserstein generative adversarial network (DSAWGAN) designed to improve diagnostic capabilities in infectious diseases with limited data availability. DSAWGAN enhances convergence speed, stability, and image quality by integrating attention modules and leveraging the Wasserstein distance optimization. We compared DSAWGAN-generated images with traditional data augmentation and other image generation techniques, evaluating their effectiveness using classification neural networks for diagnostic accuracy. This model integration was then applied to a mobile app, enabling rapid, portable, and cost-effective diagnostic testing across various concentration ranges. Using only half of the raw data (
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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.