We propose a novel AI-based system for breast cancer (BCa) assessment to predict response to neoadjuvant chemotherapy (NAC) into one of three responses: Partial Response (PR), Complete Response (CR), and Stationary Disease (SD), providing a full insight for medical experts about treatment regimens. The proposed AI-based system integrates machine learning (ML) and deep learning (DL) approaches to incorporate both global and local markers for more accurate prediction. The ML approach, based on a decision tree model, learns patterns from global markers extracted through pathology assessments to determine molecular subtypes. This analysis incorporates four standard tests: ER, PR, HER2, and Ki-67. Additionally, it integrates global radiomics descriptors, including tumor morphology, lesion count, and radiologist assessments for axillary nodes (benign vs. suspicious). In addition to assessing global markers, we employed a pre-trained Vision Transformer (ViT-b16) with a multihead adaptive self-attention mechanism to extract local markers from the Region of Interest (ROI) around the breast tumor. This approach eliminates the need for segmentation, which could impact the accuracy of the local AI model’s prediction. The outputs of both models are fused using a GradientBoosting algorithm to predict the response to NAC. The proposed system was tested on 736 2D images along with their corresponding radiomics and pathological markers (CR = 156, PR = 353, and SD = 227). The developed AI-based system achieved an accuracy of 98.91%. Explainability was enabled through heatmaps which visually highlight areas with high attention for decision-making. These results demonstrate promising potential for AI-based early assessment in BCa management.

COVID-19, a respiratory illness caused by SARS-CoV-2, has brought attention to a common symptom: loss of smell and taste. Anosmia, a prevalent symptom of COVID-19, varies in severity from mild to severe, necessitating accurate diagnostic tools. The study proposes a novel framework for predicting COVID-19 anosmia severity utilizing YOLOv8 for classification and EigenCAM for interpretability. YOLOv8, optimized for object detection, is adapted for classification tasks using advanced architectural enhancements and mosaic augmentation. EigenCAM provides interpretability by highlighting image regions crucial for predictions, aiding clinical decision-making. Evaluation across multiple YOLOv8 model sizes using DTI and FLAIR modalities reveals robust performance, with the Large model excelling in DTI and the Nano model in FLAIR. Compared to our previous work, the framework significantly enhances accuracy and interpretability in predicting anosmia severity, marking a substantial advancement in medical image analysis. This study underscores the potential of deep learning for precise and interpretable medical diagnostics, offering insights into anosmia severity prediction.

Hepatocellular carcinoma (HCC), – the main form of liver cancer –, is the second global leading cause of cancer-related mortality. LI-RADS is considered the worldwide non-invasive standard method for imaging interpretation and reporting in patients with HCC eliminating the need for biopsy. However, it might be prone to interpretation subjectivity. Therefore, we develop an objective non-invasive AI-based grading system for HCC for appropriate etiology treatment plans. The developed system integrates potential image-based markers that represent the tumor’s morphology, functionality, and appearance/texture with the associated clinical biomarkers. The study encompasses 117 patients diagnosed with HCC and was divided into three different groups (group 1: benign low-grade (LR 1,2), N = 41; group 2: malignant high-grade (LR 4,5), N = 39; and group 3: malignant not HCC (LR-M), N = 37). Diffusion-weighted magnetic resonance imaging (DWI) was acquired for imaging-based markers identification. The developed grading system pipeline includes: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of appearance/textural markers using a novel rotation invariant circular binary pattern model, iii) calculation of the functional markers by constructing the representative cumulative distribution functions of the estimated apparent diffusion coefficients, and iv) integrating the aforementioned imaging-based markers with the associated clinical biomarkers, known as Alpha-fetoprotein. The integrated markers were optimized to train and test multiple machine learning (ML) classifiers and a hyper-tuned custom CNN. On a randomly stratified train (80%) test (20%) split scheme, the developed obtained an overall accuracy of 88% in differentiating between the three groups using the integrated markers along with the CatBoost classifier, surpassing the diagnostic performance of individual marker sets, other ML classifiers, and the CNN as well. The obtained results demonstrate the feasibility of the developed system as a novel tool for non-invasive and objective HCC grading.

Prostate cancer (PCa) is a widespread type of cancer that leads to numerous fatalities and a high financial cost. The chance of survival for PCa patients increases when the disease is detected at an early stage. This study discusses the development of a non-invasive computer-aided diagnosis (CAD) system that utilizes intravoxel incoherent motion (IVIM) parameters to detect and diagnose prostate cancer. The study focuses on IVIM, which can separate the diffusion of water molecules in capillaries from the molecular diffusion outside of the vessels, and its diagnostic efficacy in the central and peripheral zones of prostate cancer. The study proposes a two-step segmentation approach for tumor detection, starting with the precise localization of the prostate gland using a robust level-sets technique and then using an Attention U-Net to extract the tumor-containing region of interest (ROI) from the segmented image. The study evaluates the performance of the CAD system, the best classifier and IVIM parameters for differentiation, and the diagnostic value of IVIM parameters compared to ADC. The results of this study contribute to the development of non-invasive methods for early prostate cancer detection and diagnosis. The IVIM (CZ + PZ) parameters that utilized the extra trees classifier (ETC) and were implemented without principal component analysis (PCA) and standardization scaling achieved the best metrics. They produced an accuracy of 84.62%, a balanced accuracy of 82.58%, a precision of 80%, a specificity of 67.86%, a sensitivity of 97.30%, an F1-score of 87.12%, an IoU of 78.26%, a ROC of 83.88%, and a weighted sum metric (WSM) of 82.79%.

Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Glioma grading is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive noninvasive multimodal magnetic resonance (MR)-based computeraided diagnostic (CAD) system that has the ability to differentiate between high grade gliomas (HGG) and low grade gliomas (LGG). The proposed glioma grading (GG-CAD) system utilizes three different MR imaging modalities, namely; contrast-enhanced T1-MR, T2-MR known as fluid-attenuated inversion-recovery (FLAIR), and diffusion-weighted (DW-MR) to extract the following imaging features: (i) morphological features based on constructing the histogram of oriented gradients (HOG) and estimating the glioma volume, (ii) first and second orders textural features by constructing histogram, gray-level run length matrix (GLRLM), gray-level co-occurrence matrix (GLCM), and (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and wash-in slope. These features are then integrated together and processed using a multi-layer perceptron artificial neural networks (MLP-ANN) classification model towards getting the final diagnosis of a glioma as HGG or LGG. The GG-CAD system was evaluated on a total of 82 gliomas (HGG = 42 and LGG = 40) using a k-fold cross-validation approach (k = 82, 10, and 5). The GG-CAD achieved 98.8%±1.0% accuracy, 99.2%±1.1% sensitivity, 98.3%±1.2% specificity, and 0.99%±0.01% F1score at k = 82 and an outstanding diagnostic performance at k = 10 and 5. The obtained diagnostic results hold promise of the developed GG-CAD system as a non-invasive diagnostic tool.

Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based sources. The image-based sources included apparent diffusion coefficients (ADCs) and the amount of deoxygenated hemoglobin (R2*). More specifically, these ADCs were extracted from 47 diffusion weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm2), while the R2* values were extracted from 30 blood oxygen leveldependent MRI (BOLD-MRI) scans at 5 different echo times (2ms,7ms, 12ms, 17ms, and 22ms). The clinical sources included serum creatinine (SCr) and creatinine clearance (CrCl). First, the kidney was segmented through the RT-CAD system using a geometric deformable model called a level-set method. Second, both ADCs and R2* were estimated for common patients (N=30) and then were integrated with the corresponding SCr and CrCl. Last, these integrated biomarkers were considered the discriminatory features to be used as trainers and testers for future deep learning-based classifiers such as stacked auto-encoders (SAEs). We used a k-fold cross-validation criteria to evaluate the RT-CAD system diagnostic performance, which achieved the following scores: 93.3%, 90.0%, and 95.0% in terms of accuracy, sensitivity, and specificity in differentiating between acute renal rejection (AR) and non-rejection (NR). The reliability and completeness of the RT-CAD system was further accepted by the area under the curve score of 0.92. The conclusions ensured that the presented RT-CAD system has a high reliability to diagnose the status of the renal transplant in a non-invasive way.

Early assessment of liver cancer patients with hepatocellular carcinoma (HCC) is of immense importance to provide the proper treatment plan. In this paper, we developed a two-stage classification computer-aided diagnostic (CAD) system that has the ability to detect and grade the liver observations from multiphase contrast enhanced magnetic resonance imaging (CE-MRI). The proposed approach consists of three main steps. First, a pre-processing is applied to the CE-MRI scans to delineate the tumor lesions that will be used as a region of interest (ROI) across the four different phases of the CE-MRI, (namely, the pre-contrast, late-arterial, portal-venous, and delayed-contrast). Second, a group of three features are modeled to provide a quantitative discrimination between the tumor lesions, namely: (i) the tumor appearance that is modeled using a set of texture features, (namely; the first-order histogram features, second-order gray-level co-occurrence matrix (GLCM) features, and second-order gray-level run-length matrix (GLRLM) features), to capture any discrimination that may appear in the lesion texture; (ii) the spherical harmonics (SH) based shape features that have the ability to describe the shape complexity of the liver tumors; and (iii) the functional features that are based on the calculation of the wash-in/wash-out slopes to evaluate the intensity changes across different phases. Finally, the aforementioned individual features were integrated together to obtain the combined features to be fed to a machine learning classifier towards getting the final diagnostic decision. The proposed CAD system was tested using hepatic observations obtained from 85 participating patients, 34 patients with benign tumors (LR-1 = 17 and LR-2 = 17), 34 patients with intermediate tumors (LR-3) and 34 with malignant tumors (LR-4 = 17 and LR-5 = 17). Using a random forests classifier with a leave-one-subject-out (LOSO) cross-validation, the developed CAD system achieved an 87.1% accuracy in distinguishing malignant, intermediate and benign tumors (i.e. First stage classification). Using the same classifier and validation, the LR-1 lesions were classified from LR-2 benign lesions with 91.2% accuracy, while 85.3% accuracy was achieved differentiating between LR-4 and LR-5 malignant tumors. The classification performance was then evaluated using k-fold (10 and 5-fold) cross-validation approaches to examine the robustness of the system. The obtained results hold a promise of the proposed framework to be reliably used as a noninvasive diagnostic tool for the early detection and grading of liver cancer tumors.

Non-invasive evaluation of renal transplant function is essential to minimize and manage renal rejection. A computer-assisted diagnostic (CAD) system was developed to evaluate kidney function post-transplantation. The developed CAD system utilizes the amount of blood-oxygenation extracted from 3D (2D + time) blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) to estimate renal function. BOLD-MRI scans were acquired at five different echo-times (2, 7, 12, 17, and 22) ms from 15 transplant patients. The developed CAD system first segments kidneys using the level-sets method followed by estimation of the amount of deoxyhemoglobin, also known as apparent relaxation rate (R2*). These R2* estimates were used as discriminatory features (global features (mean R2*) and local features (pixel-wise R2*)) to train and test state-of-the-art machine learning classifiers to differentiate between non-rejection (NR) and acute renal rejection. Using a leave-one-out cross-validation approach along with an artificial neural network (ANN) classifier, the CAD system demonstrated 93.3% accuracy, 100% sensitivity, and 90% specificity in distinguishing AR from non-rejection . These preliminary results demonstrate the efficacy of the CAD system to detect renal allograft status non-invasively.

Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant functions. In this paper, a computer-aided diagnostic (CAD) system is developed to assess renal transplant functionality, which integrates both clinical and diffusion MRI -derived markers extracted from 4D DW-MRI (i.e. 3D + b-value). To extract the DW-MR image-markers, our framework performs multiple image processing steps, including kidney segmentation using a level-set approach and estimation of image-markers. To extract these image-markers, apparent diffusion coefficients (ADCs) are estimated from the segmented DW-MRIs and cumulative distribution functions (CDFs) of the ADCs are constructed at different b-values (i.e. gradient field strengths and duration). Finally, these markers (i.e. CDFs) are integrated with clinical biomarkers (e.g., creatinine clearance and serum plasma creatinine) to assess transplant status using stacked auto-encoders with non-negativity constraints based on deep learning classification approach. Our CAD system consists of two consecutive classification stages. The first stage classifier achieved a 96% accuracy, a 95% sensitivity, and a 100% specificity in distinguishing non-rejection (NR) from dysfunctional (DF) transplanted kidneys. Additionally, an overall accuracy of 94% has been obtained in the second stage in separating DF to acute rejection (AR) and different renal disease (DRD) transplants. Our preliminary results hold strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status.

The following topics are dealt with: learning (artificial intelligence); feature extraction; image classification; feedforward neural nets; neural nets; convolution; object detection; image segmentation; face recognition; image representation.

Although renal biopsy remains the gold standard for diagnosing the type of renal rejection, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. Therefore, there is an urgent need to explore a non-invasive technique that can early classify renal rejection types. In this paper, we develop a computer-aided diagnostic (CAD) system that can classify acute renal transplant rejection (ARTR) types early via the analysis of apparent diffusion coefficients (ADCs) extracted from diffusion-weighted (DW) MRI data acquired at low-(accounting for perfusion) and high-(accounting for diffusion) b-values. The developed framework mainly consists of three steps: (i) data co-alignment using a 3D B-spline-based approach (to handle local deviations due to breathing and heart beat motions) and segmentation of kidney tissue with an evolving geometric (level-set based) deformable model guided by a voxel-wise stochastic speed function, which follows a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and visual kidney-background appearances of DW-MRI data (image intensities and spatial interactions); (ii) construction of a cumulative empirical distribution of ADC at low and high b-values of the segmented kidney accounting for blood perfusion and water diffusion, respectively, to be our discriminatory ARTR types feature; and (iii) classification of ARTR types (acute tubular necrosis (ATN) anti-body- and T-cell-mediated rejection) based on deep learning of a non-negative constrained stacked autoencoder. Results show that 98% of the subjects were correctly classified in our “leave-one-subject-out” experiments on 39 subjects (namely, 8 out of 8 of the ATN group and 30 out of 31 of the T-cell group). Thus, the proposed approach holds promise as a reliable non-invasive diagnostic tool.

This paper introduces a novel computer-aided diagnostic (CAD) system for the assessment of renal transplant status that integrates image-based biomarkers derived from 4D (3D + b -value) diffusion-weighted (DW) MRI, and clinical biomarkers. To analyze DW-MRI, our framework starts with kidney tissue segmentation using a level set approach after DW-MRI data alignment to handle the motion effects. Secondly, the cumulative empirical distributions (i.e., CDFs) of apparent diffusion coefficients (ADCs) of the segmented DW-MRIs are estimated at low and high gradient strengths and duration ( b -values) accounting for both blood perfusion and diffusion, respectively. Finally, these CDFs are fused with laboratory-based biomarkers (creatinine clearance and serum plasma creatinine) for the classification of transplant status using a deep learning-based classification approach utilizing a stacked non-negativity constrained auto-encoder. Using “leave-one-subject-out” experiments on a cohort of 58 subjects, the proposed CAD system distinguished non-rejection transplants from kidneys with abnormalities with a 95 % accuracy (sensitivity = 95 %, specificity = 94 %) and achieved a 95 % correct classification between early rejection and other kidney diseases. Our preliminary results demonstrate the promise of the proposed CAD system as a reliable non-invasive diagnostic tool for renal transplants assessment.

Developing any non-invasive computer-aided diagnostic (CAD) system for the diagnosis of kidney diseases essentially requires the extraction of the kidney from medical images. We propose a shape based level-set framework for 3D kidney segmentation from diffusion-weighted magnetic resonance imaging (DW-MRI). A stochastic speed relationship is used to control the deformable model evolutions. This speed relationship is based on an adaptive shape prior guided by the first- and second-order visual appearance features of the DW-MRI data. These pre-mentioned image features are integrated into a joint Markov-Gibbs random field (MGRF) model of the kidney and its background. DW-MRI data sets from eight subjects acquired at different b-values ranging from 0 to 1000 s/mm2are tested using a leave-one-subject-out method to evaluate the proposed segmentation approach, and to compare its performance with other segmentation methods using three evaluation metrics: the Dice similarity coefficient (DSC), the 95-percentile modified Hausdorff distance, and the absolute kidney volume difference. Robustness and accuracy of the proposed approach are confirmed through the experimental results' evaluation between manually drawn and automatically segmented contours.