Breast cancer is the most prevalent genetic malignancy, causing cancer-related fatalities among women worldwide. This highlights the importance of early detection and accurate response evaluation in improving patient prognosis and long-term survival rates. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCEMRI) captures voxel-wise temporal insights that reveal perfusion heterogeneity and vascular permeability, thereby facilitating the discrimination of pCR (pathological complete response) and non-pCR (non-pathological complete response) when evaluating treatment response. Accordingly, this work proposes a temporally derived fractional order derivative (FOD) based radiomics model for breast cancer treatment response classification. Herein, the model is primarily validated using the QIN Breast DCEMRI dataset, consisting of a limited patients (pCR=3, nonpCR=7) scanned at 32 time points. However, each patient encompasses prominent temporal insights, assisting in validating the model. Initially, the I-max maps are extracted from 4D DCE-MRIs, which are utilized for computing the Fractional Order Derivative (FOD) gradient images. A comprehensive set of radiomic features was extracted from the FOD gradient images corresponding to two chemotherapy cycles (Visit 1 and Visit 2). Generally, the FOD images provide complementary information, enhancing the predictive ability of the radiomics model. Further, a robust feature dimensionality reduction technique is incorporated for selecting optimal features from the combined feature sets (Visit 1, Visit 2, and.(V2, V1). The selected optimal features are utilized for training and testing the classification models, namely, Support Vector Machine (SVM) kernels (Linear, RBF, polynomial), Logistic Regression, and Random Forest for classifying pCR and non-pCR classes. Further, the classifying models are evaluated by metrics, obtaining an interpretable and balanced model. Hence, the clinical application of the proposed model aids personalised therapy and improves the survival rate.
Electroencephalogram (EEG) signals are often contaminated by cardiac artifacts, which can significantly degrade the performance of downstream analysis. This paper presents a Fourier Domain Denoising Convolutional Neural Network (FD-DCNN) that leverages frequency domain convolution to effectively suppress electrocardiogram (ECG) artifacts in single channel EEG signals. Unlike conventional, time domain CNN-based approaches, the proposed model operates entirely in the spectral domain, replacing computationally expensive convolutions with element-wise complex multiplications, suitable for area and power constrained biomedical hardware. A complex-domain non-linear activation, PhaseReLU, is introduced after each spectral convolution to preserve magnitude while enabling non-linear feature extraction. The network is trained using a Morphology Preserving Loss (MPL) combining mean squared error and correlation to maintain amplitude fidelity and waveform morphology. The publicly available EEGdenoiseNet and MIT-BIH Polysomnographic databases were used to generate the simulated dataset for model training and evaluation. FD-DCNN achieves a lower Relative Root Mean Square Error (RRMSE) of 0.4301 in the time domain and 0.3548 in the frequency domain, along with a higher correlation coefficient of 0.8879, while requiring only 0.67M parameters, 2.5MB of memory, and 2.31M floating point operations per second (FLOPs), which is substantially less than a comparable time domain CNN with similar architecture. These results establish FD-DCNN as a lightweight, high-performance denoising architecture well-suited for efficient hardware implementation and real-time EEG processing.
Seismocardiographic signal (SCG) is the chest vibration induced during the cardiac cycle due to cardiac muscle contraction, cardiac valves movement and blood momentum changes [ 1 – 5 ]. SCG can be used for estimating cardiac time intervals (CTI), which has been shown to be of potential value in predicting certain heart diseases [7 , 8] . Detecting CTI relies on accurate annotation of SCG and electrocardiogram (ECG) fiducial points including aortic valve opening (AO) and closure (AC), mitral valve opening (MO) and closure (MC) and ECG Q, R and T peaks [3 , 9] . Several studies addressed fiducial points detection using different methods [3 , 9 – 11] , however, a fully automated method that is simple and accurate is not yet available. Therefore, the objective of the current study is to introduce such a method and validate it by comparing the extracted CTI to the literature and highlight discrepancies in reporting CTI. The focus will be on the MC and AC events in SCG which are associated with the first and second heart sounds respectively [13] .
This review briefly investigates how artificial intelligence (AI) methods are being applied to microbiota-based clinical tasks. We analyse a curated set of peer-reviewed studies to characterize the models used, the nature of microbiota-derived inputs, and the clinical goals addressed. Our findings show a preference for classical machine learning approaches, especially random forests, due to their robustness and interpretability. Deep learning methods are less frequent and primarily employed in multimodal contexts. Most studies focus on disease prediction or classification, though some explore treatment response or drug-microbiota interactions. Gut-derived profiles dominate the input data, with limited exploration of other microbiota niches. Key challenges include the lack of external validation, inconsistent preprocessing practices, and limited use of explainability techniques. These observations point to the need for more standardized, transparent, and clinically grounded research to advance the integration of AI with microbiome science.
A non-invasive brain-computer interface (BCI) system based on electroencephalogram (EEG) signals is presented to facilitate intuitive control of assistive devices for individuals with motor impairments. EEG recordings corresponding to actual and imagined motor movements are transformed into two-dimensional representations us-ing the Gramian Angular Summation Field (GASF) technique, effectively encoding temporally significant fea- tures. The resulting GASF images are classified using the GoogLeNet convolutional neural network architecture, achieving a classification accuracy of up to 94.4% across both real and imagined movement tasks. Experimental results demonstrate that the proposed model attains higher accuracy in recognizing actual movements compared to imagery-based movements, indicating its potential as a practical solution for reliable motor intention decoding in assistive and rehabilitative applications.
