Explainable AI in Pharmaceutics: Grad-CAM Analysis of Surface Dissolution Imaging Using Convolutional Neural Networks
Year: 2026
Type: article
Abstract: Background: The dissolution of oral solid dosage forms is a key determinant of drug bioavailability, yet traditional testing methods do not capture the real-time surface dynamics of drug release. This study introduces a novel framework combining surface dissolution imaging (SDi2) with an interpretable, dual-wavelength convolutional neural network (CNN) to predict and understand dissolution behavior. Methods: Eight tablet formulations containing acetylsalicylic acid, sodium salicylate, or salicylamide, combined with either lactose or methylcellulose, were analyzed under two distinct, compendial conditions (pH 1.2 and pH 6.8). Results: Our final CNN model, which synergistically processes spectral images (280 nm for API release and 520 nm for structural changes), temporal data, and formulation composition, accurately predicted dissolution profiles, achieving a coefficient of determination of 0.89 and a root mean square error (RMSE) of 11.57. To overcome the “black-box” nature of deep learning, we employed Gradient-weighted Class Activation Mapping (Grad-CAM) to interpret the model’s predictions. The analysis revealed that the model focused on tablet edges at 280 nm, consistent with surface dissolution, and on bulk regions at 520 nm, reflecting structural changes including erosion and gel-layer growth. Conclusions: These findings suggest that integrating real-time imaging with explainable AI methods can support better understanding of dissolution processes in pharmaceutical formulation development.
Source: Pharmaceutics
Institution: Jagiellonian University
Language: English
Subfield: Pharmaceutical Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,499
Awards: G2246726965, G8635014400
OpenAlex ID: W7154354777