ArticlePharmaceutics2021
Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development.
Article in Pharmaceutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Active learning in pharmaceutical 3D printing: a multi-dataset comparison.Drug delivery and translational research · 2026Article
- Review
- Characteristics, preparation and applicability in oral delivery systems of cellulose ether-based buccal films.Drug delivery · 2025Review
- Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review.Pharmaceutics · 2025Review
- The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives.Chromatographia · 2025Article
- Preparation and In Vitro Evaluation of Montelukast Sodium-Loaded 3D Printed Orodispersible Films for the Treatment of Asthma.AAPS PharmSciTech · 2024Article
- Cold Laser Sintering of Medicines: Toward Carbon Neutral Pharmaceutical Printing.ACS sustainable chemistry & engineering · 2024Article
- Rising role of 3D-printing in delivery of therapeutics for infectious disease.Journal of controlled release : official journal of the Controlled Release Society · 2024Review
- Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images.Pharmaceutics · 2023Article
- Article
- Modernising Orodispersible Film Characterisation to Improve Palatability and Acceptability Using a Toolbox of Techniques.Pharmaceutics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.
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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.