ArticleCureus2025
Comparing No-Code Platforms and Deep Learning Models for Glaucoma Detection From Fundus Images.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Multi-omics technologies: Novel tools and methods for assessing nerve injury and regeneration.Neural regeneration research · 2026Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study compares the performance of two no-code machine learning platforms, Google's Teachable Machine (TM) (Google LLC, Mountain View, CA, USA) and Apple's Create ML (Apple Inc., Cupertino, CA, USA), alongside a traditional deep learning model, ResNet200d, in classifying optic nerve fundus images into glaucoma and non-glaucoma categories using the ACRIMA dataset.
methodsA comparative cross-sectional analysis was conducted using 705 labeled fundus images from the ACRIMA dataset (326 glaucomatous, 239 non-glaucomatous). Models were trained separately on each platform, and a validation set comprising 70 glaucomatous and 70 non-glaucomatous images was used to assess performance. Performance metrics, such as sensitivity, specificity, F1 score, and Cohen's kappa, were assessed with 95% confidence intervals. Statistical analysis was performed using DATAtab (DATAtab e.U. Graz, Austria (https://datatab.net)).
resultsThe ResNet200d model demonstrated the highest performance, with an accuracy of 99.29%, a sensitivity of 98.57%, a specificity of 100%, and an F1 score of 99.29%. Create ML achieved a sensitivity of 93.24%, a specificity of 98.48%, and an F1 score of 95.83%. TM exhibited a sensitivity of 95.71%, a specificity of 94.29%, and an F1 score of 95.04%. Both no-code platforms demonstrated strong performance, with Create ML excelling in specificity and TM showing higher sensitivity.
conclusionWhile the ResNet200d model outperformed both no-code platforms in diagnostic accuracy, the no-code platforms demonstrated robust capabilities, highlighting their potential to democratize artificial intelligence (AI) in healthcare. These results highlight the potential of no-code platforms for democratizing medical image analysis, especially in resource-limited contexts. Further studies with diverse datasets are recommended to validate these results.
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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 Socratic graph.