---
title: "Multimodal CNN Combining UV Fluorescent Imaging and Compression Force for Tablet Dissolution Predi"
id: "pubmed-42546994"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42546994"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42546994/"
doi: "10.1016/j.ijpharm.2026.127276"
published_at: "2026-09-05T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Multimodal CNN Combining UV Fluorescent Imaging and Compression Force for Tablet Dissolution Predi
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42546994
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42546994/)
- **DOI:** [10.1016/j.ijpharm.2026.127276](https://doi.org/10.1016%2Fj.ijpharm.2026.127276)
- **Published At:** 2026-09-05T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- The study developed a **multimodal convolutional neural network (MI-CNN)** to predict tablet-level dissolution profiles for immediate-release tablets using UV fluorescent images and compression force as inputs. - Models compared: MI-CNN (images + compression force), SI-CNN (images only), and an MLP using hand-crafted histogram descriptors plus compression force. - Dataset was generated using a Design of Experiments covering multiple compression forces, disintegrant concentrations, and acetylsalicylic acid particle size fractions; an unseen particle size range was included to test generalization. - The MI-CNN delivered the most consistent performance with similar training and validation errors (RMSEtrain: 13.09% and RMSEval: 12.54%), indicating robust generalization across formulation conditions. - The SI-CNN (images only) had reduced validation accuracy (RMSEval: 25.41%), particularly where dissolution differences were driven by **tablet compaction**. - The MLP showed strong training fit (RMSEtrain: 2.94%) but poor validation performance (RMSEval: 27.15%), suggesting overfitting and insufficient representational power of histogram-based features for this dataset. - Explainable AI using SHapley Additive exPlanations (SHAP) indicated that both **compression force** and image-derived features contributed to model predictions. - The findings support combining process variables with image-based information for surrogate dissolution modeling and potential real-time release testing strategies. - Details such as exact model architectures, training hyperparameters, dataset size, and preprocessing steps were not reported in the abstract and are available only in the full text.
## Clinical Analysis & Structured Key Points
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Epub 2026 Aug 3. # Multimodal convolutional neural network for tablet-level dissolution prediction using compression force and UV fluorescent imaging [Barbara Honti](https://pubmed.ncbi.nlm.nih.gov/?term=Honti+B&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#full-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Lilla Alexandra Mészáros](https://pubmed.ncbi.nlm.nih.gov/?term=M%C3%A9sz%C3%A1ros+LA&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#full-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Bence Szabó-Szőcs](https://pubmed.ncbi.nlm.nih.gov/?term=Szab%C3%B3-Sz%C5%91cs+B&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#full-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Zsombor Kristóf Nagy](https://pubmed.ncbi.nlm.nih.gov/?term=Nagy+ZK&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#full-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Hajnalka Pataki](https://pubmed.ncbi.nlm.nih.gov/?term=Pataki+H&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#full-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Brigitta Nagy](https://pubmed.ncbi.nlm.nih.gov/?term=Nagy+B&cauthor_id=42546994)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#full-view-affiliation-2 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary. Electronic address: nagy.brigitta@vbk.bme.hu.") Affiliations Expand ### Affiliations * 1 Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary. * 2 Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary. Electronic address: nagy.brigitta@vbk.bme.hu. * PMID: **42546994** * DOI: [ 10.1016/j.ijpharm.2026.127276 ](https://doi.org/10.1016/j.ijpharm.2026.127276) Free article Item in Clipboard # Multimodal convolutional neural network for tablet-level dissolution prediction using compression force and UV fluorescent imaging Barbara Honti et al. Int J Pharm. 2026. Free article Show details Display options Display options Format Abstract PubMed PMID Int J Pharm Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Int+J+Pharm%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Int+J+Pharm%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42546994/) . 2026 Sep 5:702:127276. doi: 10.1016/j.ijpharm.2026.127276. Epub 2026 Aug 3. ### Authors [Barbara Honti](https://pubmed.ncbi.nlm.nih.gov/?term=Honti+B&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#short-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Lilla Alexandra Mészáros](https://pubmed.ncbi.nlm.nih.gov/?term=M%C3%A9sz%C3%A1ros+LA&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#short-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Bence Szabó-Szőcs](https://pubmed.ncbi.nlm.nih.gov/?term=Szab%C3%B3-Sz%C5%91cs+B&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#short-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Zsombor Kristóf Nagy](https://pubmed.ncbi.nlm.nih.gov/?term=Nagy+ZK&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#short-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Hajnalka Pataki](https://pubmed.ncbi.nlm.nih.gov/?term=Pataki+H&cauthor_id=42546994)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#short-view-affiliation-1 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary."), [Brigitta Nagy](https://pubmed.ncbi.nlm.nih.gov/?term=Nagy+B&cauthor_id=42546994)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42546994/#short-view-affiliation-2 "Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary. Electronic address: nagy.brigitta@vbk.bme.hu.") ### Affiliations * 1 Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary. * 2 Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary. Electronic address: nagy.brigitta@vbk.bme.hu. * PMID: **42546994** * DOI: [ 10.1016/j.ijpharm.2026.127276 ](https://doi.org/10.1016/j.ijpharm.2026.127276) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID ## Abstract The prediction of tablet dissolution from in-process data remains a key challenge in pharmaceutical manufacturing, as in vitro dissolution is a critical quality attribute that cannot be measured inline. In this study, a multimodal convolutional neural network (MI-CNN) was developed to predict tablet-level dissolution profiles for immediate-release tablets, combining images taken under UV illumination and compression force. To evaluate the contribution of input selection and feature representation, the MI-CNN was compared with a single-input CNN (SI-CNN) using images only, and a multilayer perceptron (MLP) based on hand-crafted image descriptors and compression force. All models were evaluated on a dataset generated using a Design of Experiments approach, covering multiple compression forces, disintegrant concentrations, and acetylsalicylic acid particle size fractions. The MI-CNN achieved the most consistent performance, with comparable training and validation errors (RMSE: 13.09% and 12.54%, respectively), and demonstrated robust generalization across formulation conditions, including an unseen particle size range. The SI-CNN showed reduced accuracy (RMSEval: 25.41%), particularly in cases where dissolution differences were governed by tablet compaction. The MLP model exhibited excellent training performance (RMSEtrain: 2.94%) but poor generalization (RMSEval: 27.15%), indicating overfitting due to the limited ability of histogram-based features to adequately represent the complexity of the dataset. Model explanation using SHapley Additive exPlanations (SHAP) revealed that both compression force and image-derived features contributed to the predictions. Overall, the results demonstrate that combining process variables with image-based information enables accurate and robust dissolution prediction at the tablet level, supporting data-driven approaches for real-time release testing. **Keywords:** Convolutional neural networks; Explainable artificial intelligence (XAI); Multimodal deep learning; Surrogate dissolution modeling; UV fluorescent imaging. Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ## Similar articles * [ Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data. ](https://pubmed.ncbi.nlm.nih.gov/33545285/) Galata DL, Könyves Z, Nagy B, Novák M, Mészáros LA, Szabó E, Farkas A, Marosi G, Nagy ZK.Galata DL, et al.Int J Pharm. 2021 Mar 15;597:120338. doi: 10.1016/j.ijpharm.2021.120338. Epub 2021 Feb 2.Int J Pharm. 2021.PMID: 33545285 * [ ANN-assisted UV imaging for non-destructive dissolution prediction of HPMC matrix tablets. ](https://pubmed.ncbi.nlm.nih.gov/41653940/) Péterfi O, Mészáros LA, Szabó-Szőcs B, Ficzere M, Nagy B, Sipos E, Lenk S, Nagy ZK, Galata DL.Péterfi O, et al.Int J Pharm. 2026 Mar 10;692:126649. doi: 10.1016/j.ijpharm.2026.126649. Epub 2026 Feb 5.Int J Pharm. 2026.PMID: 41653940 * [ UV/VIS-imaging of white caffeine tablets for prediction of CQAs: API content, crushing strength, friability, disintegration time and dissolution profile. ](https://pubmed.ncbi.nlm.nih.gov/39117063/) Mészáros LA, Madarász L, Ficzere M, Bicsár R, Farkas A, Nagy ZK.Mészáros LA, et al.Int J Pharm. 2024 Sep 30;663:124565. doi: 10.1016/j.ijpharm.2024.124565. Epub 2024 Aug 8.Int J Pharm. 2024.PMID: 39117063 * [ Deep convolutional neural networks for age and gender estimation using orthopantomogram images: a systematic review. ](https://pubmed.ncbi.nlm.nih.gov/41882112/) Israel DS, Cruz Michael SHR, Angelo JM, Sounder Ida M, Israel RS.Israel DS, et al.Evid Based Dent. 2026 Jun;27(2):43-44. doi: 10.1038/s41432-026-01208-0. Epub 2026 Mar 25.Evid Based Dent. 2026.PMID: 41882112 * [ Artificial intelligence in Kellgren-Lawrence grading of knee osteoarthritis: bridging radiographic tradition with algorithmic precision. ](https://pubmed.ncbi.nlm.nih.gov/42112224/) Rawat S, Chaturvedi VP, Vaidya B, Shanmugam H, Shah A, Airen L.Rawat S, et al.Ther Adv Musculoskelet Dis. 2026 Apr 29;18:1759720X261442408. doi: 10.1177/1759720X261442408. eCollection 2026.Ther Adv Musculoskelet Dis. 2026.PMID: 42112224Free PMC article.Review. 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