Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge. Despite its rising prevalence, particularly in low and middle-income countries, early diagnosis remains inadequate, with projections estimating over 55 million affected individuals by 2022, expected to triple by 2050. Accurate early detection is critical for effective intervention. This study presents Neuroimaging-based Early Detection of Alzheimer's Disease using Deep Learning (NEDA-DL), a novel computer-aided diagnostic (CAD) framework leveraging a hybrid ResNet-50 and AlexNet architecture optimized with CUDA-based parallel processing. The proposed deep learning model processes MRI and PET neuroimaging data, utilizing depthwise separable convolutions to enhance computational efficiency. Performance evaluation using key metrics including accuracy, sensitivity, specificity, and F1-score demonstrates state-of-the-art classification performance, with the Softmax classifier achieving 99.87% accuracy. Comparative analyses further validate the superiority of NEDA-DL over existing methods. By integrating structural and functional neuroimaging insights, this approach enhances diagnostic precision and supports clinical decision-making in Alzheimer's disease detection.

This study investigates a deep learning approach for sex estimation using 3D hyoid bone models derived from computed tomography (CT) scans of a Croatian population. We analyzed 202 hyoid samples (101 male, 101 female), converting CT-derived meshes into 2048-point clouds for processing with an adapted PointNet++ network. The model, optimized for small datasets with 1D convolutional layers and global size features, was first applied in an unsupervised framework. Unsupervised clustering achieved 87.10% accuracy, identifying natural sex-based morphological patterns. Subsequently, supervised classification with a support vector machine yielded an accuracy of 88.71% (Matthews Correlation Coefficient, MCC = 0.7746) on a test set (n = 62). Interpretability analysis highlighted key regions influencing classification, with males exhibiting larger, U-shaped hyoids and females showing smaller, more open structures. Despite the modest sample size, the method effectively captured sex differences, providing a data-efficient and interpretable tool. This flexible approach, combining computational efficiency with practical insights, demonstrates potential for aiding sex estimation in cases with limited skeletal remains and may support broader applications in forensic anthropology.

Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and patient care. Deep convolutional neural networks trained on MRI data can enhance diagnostic precision. This study aims to utilize deep convolutional neural networks (CNNs) trained on MRI data for Alzheimer's disease diagnosis and classification. In this study, the Alzheimer MRI Preprocessed Dataset was used, which includes 6735 brain structural MRI scan images. After data preprocessing and normalization, four models Xception, VGG19, VGG16 and InceptionResNetV2 were utilized. Generalization and hyperparameter tuning were applied to improve training. Early stopping and dynamic learning rate were used to prevent overfitting. Model performance was evaluated based on accuracy, F-score, recall, and precision. The InceptionResnetV2 model showed superior performance in predicting Alzheimer's patients with an accuracy, F-score, recall, and precision of 0.99. Then, the Xception model excelled in precision, recall, and F-score, with values of 0.97 and an accuracy of 96.89. Notably, InceptionResnetV2 and VGG19 demonstrated faster learning, reaching convergence sooner and requiring fewer training iterations than other models. The InceptionResNetV2 model achieved the highest performance, with precision, recall, and F-score of 100% for both mild and moderate dementia classes. The Xception model also performed well, attaining 100% for the moderate dementia class and 99-100% for the mild dementia class. Additionally, the VGG16 and VGG19 models showed strong results, with VGG16 reaching 100% precision, recall, and F-score for the moderate dementia class. Deep convolutional neural networks enhance Alzheimer's diagnosis, surpassing traditional methods with improved precision and efficiency. Models like InceptionResnetV2 show outstanding performance, potentially speeding up patient interventions.

Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT) can be highly challenging since these diseases cause minor changes in the brain's anatomy. Magnetic Resonance Imaging (MRI) is a vital tool for diagnosing and visualizing these ND; however, standard techniques contingent upon human analysis can be inaccurate, require a long-time, and detect early-stage symptoms necessary for effective treatment. Spatial Feature Extraction (FE) has been improved by Convolutional Neural Networks (CNN) and hybrid models, both of which are changes in Deep Learning (DL). However, these analysis methods frequently fail to accept temporal dynamics, which is significant for a complete test. The present investigation introduces the STGCN-ViT, a hybrid model that integrates CNN + Spatial-Temporal Graph Convolutional Networks (STGCN) + Vision Transformer (ViT) components to address these gaps. The model causes the reference to EfficientNet-B0 for FE in space, STGCN for FE in time, and ViT for FE using AM. By applying the Open Access Series of Imaging Studies (OASIS) and Harvard Medical School (HMS) benchmark datasets, the recommended approach proved effective in the investigations, with Group A attaining an accuracy of 93.56%, a precision of 94.41% and an Area under the Receiver Operating Characteristic Curve (AUC-ROC) score of 94.63%. Compared with standard and transformer-based models, the model attains better results for Group B, with an accuracy of 94.52%, precision of 95.03%, and AUC-ROC score of 95.24%. Those results support the model's use in real-time medical applications by providing proof of the probability of accurate but early-stage ND diagnosis.

We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer's disease (AD) detection that constructs complex ML predictors from modular components. In our directed computational graph, datasets are represented as nodes [Formula: see text], and deep learning (DL) models are represented as directed edges [Formula: see text], allowing us to model complex image-processing pipelines [Formula: see text] as end-to-end DL predictors. Each directed path in the graph functions as a DL predictor, supporting both forward propagation for transforming data representations, as well as backpropagation for model finetuning, saliency map computation, and input data optimization. We demonstrate our model on Alzheimer's disease prediction, a complex problem that requires integrating multimodal data containing scans of different modalities and contrasts, genetic data and cognitive tests. We built a graph of 11 nodes (data) and 14 edges (ML models), where each model has been trained on handling a specific task (e.g. skull-stripping MRI scans, AD detection,image2image translation, ...). By using a modular and adaptive approach, our framework effectively integrates diverse data types, handles distribution shifts, and scales to arbitrary complexity, offering a practical tool that remains accurate even when modalities are missing for advancing Alzheimer's disease diagnosis and potentially other complex medical prediction tasks.

The rapid integration of artificial intelligence (AI) into healthcare has enhanced diagnostic accuracy; however, patient engagement and satisfaction remain significant challenges that hinder the widespread acceptance and effectiveness of AI-driven clinical tools. This study introduces CareAssist-GPT, a novel AI-assisted diagnostic model designed to improve both diagnostic accuracy and the patient experience through real-time, understandable, and empathetic communication. CareAssist-GPT combines high-resolution X-ray images, real-time physiological vital signs, and clinical notes within a unified predictive framework using deep learning. Feature extraction is performed using convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformer-based NLP modules. Model performance was evaluated in terms of accuracy, precision, recall, specificity, and response time, alongside patient satisfaction through a structured user feedback survey. CareAssist-GPT achieved a diagnostic accuracy of 95.8%, improving by 2.4% over conventional models. It reported high precision (94.3%), recall (93.8%), and specificity (92.7%), with an AUC-ROC of 0.97. The system responded within 500 ms-23.1% faster than existing tools-and achieved a patient satisfaction score of 9.3 out of 10, demonstrating its real-time usability and communicative effectiveness. CareAssist-GPT significantly enhances the diagnostic process by improving accuracy and fostering patient trust through transparent, real-time explanations. These findings position it as a promising patient-centered AI solution capable of transforming healthcare delivery by bridging the gap between advanced diagnostics and human-centered communication.

Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However, Deep CNNs require substantial computational power and memory, particularly for large datasets and complex architectures. Additionally, optimising the hyperparameters of deep CNNs, although critical for enhancing model performance, is challenging due to the high computational costs involved, making it difficult without access to high-performance computing resources. To address these limitations, this study presents a fast and efficient model that aims to achieve superior classification performance compared to popular Deep CNNs by developing lightweight CNNs combined with the Nonlinear Lévy chaotic moth flame optimiser (NLCMFO) for automatic hyperparameter optimisation. NLCMFO integrates the Lévy flight, chaotic parameters, and nonlinear control mechanisms to enhance the exploration capabilities of the Moth Flame Optimiser during the search phase while also leveraging the Lévy flight theorem to improve the exploitation phase. To assess the efficiency of the proposed model, empirical analyses were performed using a dataset of 2314 brain tumour detection images (1245 images of brain tumours and 1069 normal brain images). The evaluation results indicate that the CNN_NLCMFO outperformed a non-optimised CNN by 5% (92.40% accuracy) and surpassed established models such as DarkNet19 (96.41%), EfficientNetB0 (96.32%), Xception (96.41%), ResNet101 (92.15%), and InceptionResNetV2 (95.63%) by margins ranging from 1 to 5.25%. The findings demonstrate that the lightweight CNN combined with NLCMFO provides a computationally efficient yet highly accurate solution for medical image classification, addressing the challenges associated with traditional deep CNNs.

To develop an automated grading model for rectocele (RC) based on radiomics and evaluate its efficacy. This study retrospectively analyzed a total of 9,392 magnetic resonance imaging (MRI) images obtained from 222 patients who underwent dynamic magnetic resonance defecography (DMRD) over the period from August 2021 to June 2023. The focus was specifically on the defecation phase images of the DMRD, as this phase provides critical information for assessing RC. To develop and evaluate the model, the MRI images from all patients were randomly divided into two groups. 70% of the data were allocated to the training cohort to build the model, and the remaining 30% was reserved as a test cohort to evaluate its performance. First, the severity of RC was assessed using the RC MRI grading criteria by two independent radiologists. To extract and select radiomic features, two additional radiologists independently delineated the regions of interest (ROIs). These features were then dimensionality reduced to retain only the most relevant data for the analysis. The radiomics features were reduced in dimension, and a machine learning model was developed using a Support Vector Machine (SVM). Finally, receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate the classification efficiency of the model. The AUC (macro/micro) of the model using defecation phase images was 0.794/0.824, and the overall accuracy was 0.754. The radiomics model built using the combination of DMRD defecation phase images is well suited for grading RC and helping clinicians diagnose and treat the disease.

Accurate preoperative assessment of lymph node metastasis (LNM) and overall survival (OS) status is essential for patients with locally advanced gastric cancer receiving neoadjuvant chemotherapy, providing timely guidance for clinical decision-making. However, current approaches to evaluate LNM and OS have limited accuracy. In this study, we used longitudinal CT images from 1,021 patients with locally advanced gastric cancer to develop and validate a multitask deep learning model, named co-attention tri-oriented spatial Mamba (CTSMamba), to simultaneously predict LNM and OS. CTSMamba was trained and validated on 398 patients, and the performance was further validated on 623 patients at two additional centers. Notably, CTSMamba exhibited significantly more robust performance than a clinical model in predicting LNM across all of the cohorts. Additionally, integrating CTSMamba survival scores with clinical predictors further improved personalized OS prediction. These results support the potential of CTSMamba to accurately predict LNM and OS from longitudinal images, potentially providing clinicians with a tool to inform individualized treatment approaches and optimized prognostic strategies. CTSMamba is a multitask deep learning model trained on longitudinal CT images of neoadjuvant chemotherapy-treated locally advanced gastric cancer that accurately predicts lymph node metastasis and overall survival to inform clinical decision-making. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.

Low-dose computed tomography (LDCT) imaging employed in lung cancer screening (LCS) programs is increasing in uptake worldwide. LCS programs herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease. Yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis. Designed for broad accessibility and rapid adaptation, TANGERINE can be fine-tuned off the shelf for a wide range of disease-specific tasks with limited computational resources and training data. Relative to models trained from scratch, TANGERINE demonstrates fast convergence during fine-tuning, thereby requiring significantly fewer GPU hours, and displays strong label efficiency, achieving comparable or superior performance with a fraction of fine-tuning data. Pretrained using self-supervised learning on over 98,000 thoracic LDCTs, including the UK's largest LCS initiative to date and 27 public datasets, TANGERINE achieves state-of-the-art performance across 14 disease classification tasks, including lung cancer and multiple respiratory diseases, while generalising robustly across diverse clinical centres. By extending a masked autoencoder framework to 3D imaging, TANGERINE offers a scalable solution for LDCT analysis, departing from recent closed, resource-intensive models by combining architectural simplicity, public availability, and modest computational requirements. Its accessible, open-source lightweight design lays the foundation for rapid integration into next-generation medical imaging tools that could transform LCS initiatives, allowing them to pivot from a singular focus on lung cancer detection to comprehensive respiratory disease management in high-risk populations.