Related Experiment Video
Updated: May 5, 2026
09:59
Coherent anti-Stokes Raman Scattering CARS Microscopy Visualizes Pharmaceutical Tablets During Dissolution
Published on: July 4, 2014
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Explainable AI in Pharmaceutics: Grad-CAM Analysis of Surface Dissolution Imaging Using Convolutional Neural Networks
Abdullah Al-Baghdadi1, Adam Pacławski1, Jakub Szlęk1
1Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, 30-688 Kraków, Poland.
Pharmaceutics
|May 4, 2026
Summary
This study introduces surface dissolution imaging (SDi2) and a dual-wavelength convolutional neural network (CNN) to predict drug release from tablets. Explainable AI methods reveal how the model analyzes surface and bulk changes for better pharmaceutical development.
Area of Science:
- Pharmaceutical Sciences
- Drug Delivery
- Computational Chemistry
Background:
- Dissolution is critical for oral drug bioavailability.
- Traditional methods lack real-time surface dynamics.
- Novel imaging and AI approaches are needed.
Purpose of the Study:
- To develop a predictive model for drug dissolution using surface imaging and AI.
- To understand the real-time surface dynamics of drug release.
- To interpret the AI model's predictions using explainable AI.
Main Methods:
- Surface dissolution imaging (SDi2) was employed.
- A dual-wavelength convolutional neural network (CNN) was developed.
- Eight tablet formulations were tested under compendial conditions (pH 1.2 and 6.8).
Main Results:
- The CNN model accurately predicted dissolution profiles (R²=0.89, RMSE=11.57).
- Synergistic processing of spectral images, temporal data, and formulation composition improved predictions.
- Explainable AI (Grad-CAM) identified model focus on tablet edges (280 nm) and bulk regions (520 nm).
Conclusions:
- Real-time imaging combined with explainable AI enhances understanding of dissolution.
- The developed framework supports pharmaceutical formulation development.
- This approach offers insights into surface dissolution and structural changes.
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