Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences
Deep fused explainable neural architecture for the automatic recognition of therapeutic medicinal plants
BACKGROUND: Medicinal plants have been utilized for centuries for their beneficial properties and significant impact on traditional medical systems worldwide. Indian medicinal plants and their therapeutic properties have recently gained increased attention from the global healthcare community. Accurate identification and classification of medicinal plants ensure proper therapeutic applications. However, botanists manually identify these plants, which is subjective, labor-intensive, and time-consuming. OBJECTIVE: The main objective of this study is to develop an accurate and automated classification of these medicinal plants for diverse applications such as conservation initiatives, quality assurance, and the formulation of novel herbal treatments. METHODS: A Deep Fused Explainable Neural Architecture termed MedPlantNet is proposed for robust recognition of medicinal plant leaves. The deep discriminative features are extracted from the medicinal plant images using a fine-tuned Deep Convolutional Neural Network (DCNN). A customized Support Vector Machine is developed to efficiently classify these extracted deep features. To enhance model interpretability, a meta-visualization framework incorporating Grad-CAM and Occlusion Sensitivity visualization is implemented to identify the regions of the image that contribute to the classification decisions. We utilized the Mendeley benchmark Medicinal plant leaf database with 30 classes to train and evaluate the model. RESULTS: To assess the effectiveness of the proposed model, accuracy, specificity, sensitivity, and AUC are calculated. The proposed system outperforms existing state-of-the-art (SOTA) algorithms for medicinal plant classification, achieving an optimal maximum validation accuracy of 99.87% and AUC of 1. To further validate the generalizability external validation was performed on Bangladeshi medicinal plant dataset and MedLeaves dataset where the proposed model MedPlantNet attained 99.27% and 99.80% validation accuracy respectively. CONCLUSION: MedPlantNet provides an automatic framework for medicinal plant identification based on deep feature extraction, classification and explainable visualization techniques. The results obtained indicate its potential utility in supporting biodiversity conservation, quality assurance and herbal medicine research.
13 July 2026
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Forecasting-based biomedical time-series data synthesis for open data and robust AI
The limited data availability due to strict privacy regulations and significant resource demands severely constrains biomedical time-series AI development, which creates a critical gap between data requirements and accessibility. Synthetic data generation presents a promising solution by producing artificial datasets that maintain the statistical properties of real biomedical time-series data without compromising patient confidentiality. While GANs, VAEs, and diffusion models capture global data distributions, forecasting models offer inductive biases tailored for sequential dynamics. We propose a framework for synthetic biomedical time-series data generation based on recent forecasting models that accurately replicates complex electrophysiological signals such as EEG and EMG with high fidelity. These synthetic datasets can be freely shared for open AI development and consistently improve downstream model performance. Numerical results on sleep-stage classification show up to a 3.71% performance gain with augmentation and a 91.00% synthetic-only accuracy that surpasses the real-data-only baseline.
16 Apr 2026
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