Alzheimer’s disease (AD) and its precursor stages present an inherent ordinal structure, progressing from cognitively normal (NOR) through mild cognitive impairment (MCI) to full AD. This work exploits that natural ordering by comparing nominal and ordinal classification approaches applied to structural MRI (sMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We evaluate a linear Support Vector Machine (SVM), a Logistic Regression (LR) classifier, as well as LogAT and LogIT as nominal baselines, and the Support Vector Ordinal Regression EXtension (SVOREX) as an ordinal counterpart. All classifiers operate directly on voxel-level gray matter (GM) maps and are evaluated across binary (six pairwise combinations), three-class and four-class settings using Correct Classification Rate (CCR), Mean Absolute Error (MAE) and Average Mean Absolute Error (AMAE) under a 5-fold cross-validation protocol. Results show that SVOREX consistently achieves the lowest ordinal errors, particularly in the clinically most relevant multi-class scenarios, supporting the value of incorporating class-order information in Alzheimer staging from MRI data.

Accurately predicting age at symptom onset in genetic frontotemporal dementia (FTD) remains challenging due to substantial inter-individual variability, even among carriers of distinct pathogenic variants within the same gene. Time-to-event models provide a natural framework to address this problem while accounting for right censoring in presymptomatic individuals. In this study, we analyze age-at-onset prediction in a multicenter cohort of individuals at genetic risk for FTD using a hierarchical survival modeling strategy. We first develop incremental Cox proportional hazards models to systematically evaluate the progressive predictive value of different covariate blocks. We then compare the best-performing Cox model with more flexible non-linear approaches, including DeepSurv and Random Survival Forests. Models are evaluated using strictly out-of-sample five-fold cross-validation, with performance assessed via the concordance index and the integrated Brier score. Our results show that genetic group and progenitor age at onset are the primary drivers of predictive performance, yielding clearly distinct symptom free survival trajectories across risk groups. Non-linear models do not substantially outperform the optimized Cox model, despite their increased flexibility. These findings indicate that Cox models capture most of the prognostic information available in this setting, providing a robust framework for modeling disease trajectories in genetic FTD.

In recent years, cross-frequency coupling (CFC) has emerged as a valuable tool in the study of a wide range of cognitive processes due to the strong evidence of its functional role in neural computation and communication. CFC computed from electroencephalography (EEG) signals provides powerful information for detecting certain neurological conditions associated with atypical cerebral lateralization. The use of deep learning (DL) in this context offers several advantages, including improved scalability and adaptability to individual variability. However, it presents several significant challenges related to the limited availability of labelled samples and the high-dimensional and noisy nature of EEG data, which can lead to overfitting, poor generalization, and temporal and spatial variability between subjects. In this work, we propose a novel deep learning approach to reveal lateralization patterns based on inter-hemispheric functional differences via CFC. To overcome the challenges associated to the use of DL in this context, we propose the use of synthetic signals for pre-training the neural network that computes a specific type of CFC, phase-amplitude coupling (PAC), and a symmetric architecture for evaluating inter-hemispheric differences. Finally, our model incorporates a custom attention layer designed to learn the most relevant information across different EEG channels and its relative importance, further enhancing its ability to detect subtle hemispheric differences and providing the necessary explainability for clinical applications. The results demonstrate a good classification performance (AUC up to 0.85) in assessing lateralization, providing explainable insights into the mechanisms of the disorder. This may aid in early detection and provide a better understanding of the neural basis associated with this condition.

Medical imaging fusion combines complementary information from multiple modalities to enhance diagnostic accuracy. However, evaluating the quality of fused images remains challenging, with many studies relying solely on classification performance, which may lead to incorrect conclusions. We introduce a novel framework for improving image fusion, focusing on preserving fine-grained details. Our model uses a siamese autoencoder to process T1-MRI and FDG-PET images in the context of Alzheimer’s disease (AD). The framework optimizes fusion by minimizing reconstruction error between generated and input images, while maximizing differences between modalities through cosine distance. Additionally, we propose a supervised variant, incorporating binary cross-entropy loss between diagnostic labels and probabilities. Fusion quality is rigorously assessed through three tests: 1) classification of AD patients and controls using fused images; 2) an atlas-based occlusion test for identifying regions relevant to cognitive decline; and 3) analysis of structural-functional relationships via Euclidean distance. Results show an AUC of 0.92 for AD detection, reveal the involvement of brain regions linked to preclinical AD stages, and demonstrate preserved structural-functional brain networks, indicating that subtle differences are successfully captured through our fusion approach.

The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia.

Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson’s disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson’s Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson’s disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.

Artificial Intelligence (AI) has improved our ability to process large amounts of data. These tools are particularly interesting in medical contexts because they evaluate the variables from patients’ screening evaluation and disentangle the information that they contain. In this study, we propose a novel method for detecting developmental dyslexia by extracting heart signals from NIRS. Features in terms of different domains based on heart rate variability (HRV) are computed from the extracted signal, and dimensionality of the resulting data is reduced through Principal Component Analysis (PCA). To evaluate the discriminability of the information patterns associated with normal controls and dyslexic patients, the resulting components are entered into a linear classifier to evaluate the discriminability of the information patterns associated with normal controls and dyslexic patients, leading to an area under the ROC curve of 0.79. The explanatory nature of our framework, based on Shapley Additive Explanations (SHAP), yields a deeper understanding of the evaluated phenomenon, revealing the presence of behavioral variables highly correlated with the model’s features. These findings demonstrate that heart information can be extracted from a different equipment than electrocardiogram tools, and that cardiac signal variables can be used to detect dyslexia in an early stage.

The global prevalence of dementia is on the rise, posing a challenge to healthcare systems worldwide. The disease leads to irreversible deterioration of cognitive function, which underlines the importance of early detection to mitigate its impact. The Clock Drawing Test (CDT) is a widely used tool in cognitive assessment, as it involves manually drawing a clock on a piece of paper. Despite its widespread use, CDT scoring methods often rely on subjective expert assessments. Thus, machine learning and deep learning-based models are recently being proposed for the automated evaluation of CDT drawings. In this study, we compare two state-of-the-art models, a simple CNN and API-Net, as cognitive state classification systems. Two databases were used, one from Spanish clinical centers (7009 samples) and the other from a hospital in Thailand (3108 samples). The obtained results align with expected accuracy rates in such scenarios (around 80 % ) and are similar in both models. Specifically, the accuracy rates obtained with the Spanish database are 75.65 % and 72.42 % , and with the Thai database, 86.42 % and 86.90 % . This reflects that the implementation of an excessively complex model is not necessary given the available sample size and the binary classification scenario. Therefore, although both models could be useful in the clinical domain, opting for models with lower computational costs is advisable to make them more cost-effective and easily accessible.

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. The diagnosis of PD is based on clinical and neuroimaging data. This work proposes a novel approach that jointly models several Variational Autoencoder (VAE) architectures in order to maximize cross-modality prediction. We hypothesize that 123I-ioflupane SPECT could be related to motor symptomatology and other dopaminergic deficits. We propose a joint modelling of several VAE architectures for maximizing cross-modality prediction of the PD Clinical and Neuroimaging Data. The final model, with 5 common latents and 2 neuroimaging and data specific latents achieve R2 values up to 0.8 for scores related to PD, including well known PD symptomatology scales such as UPDRS (R2 = 0.545), at the same time that provides tools for interpreting the results and the common latent distribution for both clinical data and neuroimaging, paving the way for interpretable machine learning tools in neurodegeneration.

The combination of different sources of information is currently one of the most relevant aspects in the diagnostic process of several diseases. In the field of neurological disorders, different imaging modalities providing structural and functional information are frequently available. Those modalities are usually analyzed separately, although a joint of the features extracted from both sources can improve the classification performance of Computer-Aided Diagnosis (CAD) tools. Previous studies have computed independent models from each individual modality and combined them in a subsequent stage, which is not an optimum solution. In this work, we propose a method based on the principles of siamese neural networks to fuse information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). This framework quantifies the similarities between both modalities and relates them with the diagnostic label during the training process. The resulting latent space at the output of this network is then entered into an attention module in order to evaluate the relevance of each brain region at different stages of the development of Alzheimer's disease. The excellent results obtained and the high flexibility of the method proposed allow fusing more than two modalities, leading to a scalable methodology that can be used in a wide range of contexts.

The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a promising approach. This integration enhances the precision and efficiency of current diagnostic and treatment strategies while offering invaluable insights into disease mechanisms. In this comprehensive review, we delve into the transformative impact of ML and DL in this domain. Firstly, a brief analysis is provided of how these algorithms have evolved and which are the most widely applied in this domain. Their different potential applications in nuclear imaging are then discussed, such as optimization of image adquisition or reconstruction, biomarkers identification, multimodal fusion and the development of diagnostic, prognostic, and disease progression evaluation systems. This is because they are able to analyse complex patterns and relationships within imaging data, as well as extracting quantitative and objective measures. Furthermore, we discuss the challenges in implementation, such as data standardization and limited sample sizes, and explore the clinical opportunities and future horizons, including data augmentation and explainable AI. Together, these factors are propelling the continuous advancement of more robust, transparent, and reliable systems.

The development of methods based on artificial intelligence for the classification of medical imaging is widespread. Given the high dimensionality of this type of images, it is imperative to use the information contained in relevant regions for further classification. This information can be derived from the morphology of the region of interest, in terms of measurements such as area, perimeter, etc. However, the performance of the classification system strongly depends on the correct selection of the type of information employed. We propose in this work an alternative for evaluating differences between brain regions that relies on the basis of Siamese neural networks. Initially, brain scans are delimited by an anatomical atlas. Next, each pair of regions of interest is then entered into a Siamese network, which is formed by relating the distance between the two individual outputs and the corresponding label. Features are extracted from the embeddings of the final linear layer. Finally, the classification is performed by combining the characteristics of each pair of regions into an ensemble architecture. Performance was assessed by determining how asymmetry between the right and left hemispheres changes during progressive brain degeneration, from mild cognitive impairment to severe atrophy associated with Alzheimer's disease (AD). Our method discriminates with an accuracy of 98.95% between controls and AD patients, and most important, it predicts the cognitive decline in patients suffering from mild cognitive impairment that will develop AD before it occurs with an accuracy of 78.41%. These results demonstrate the applicability of our proposal in the study of a wide range of pathologies.(c) 2023 Elsevier B.V. All rights reserved.

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/ or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data. As shown by our results, the CAD proposal is able to detect PD with [Formula: see text] of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human -level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.

The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. These tools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screening analysis and disentangle the information that they contain. We propose in this work a novel method for evaluating the role of electrocardiogram (ECG) signals in the human cognitive decline. This framework offers a complete solution for all the steps in the classification pipeline, from the preprocessing of the raw signals to the final classification stage. Numerous metrics are computed from the original data in terms of different domains (time, frequency, etc.), and dimensionality is reduced through a Principal Component Analysis (PCA). The resulting characteristics are used as inputs of different classifiers (linear/non-linear Support Vector Machines, Random Forest, etc.) to determine the amount of information that they contain. Our system yielded an area under the Receiver Operating Characteristic (ROC) curve of 0.80 identifying Mild Cognitive Impairment (MCI) patients, showing that ECG contain crucial information for predicting the appearance of this pathology. These results are specially relevant given the fact that ECG acquisition is much more affordable and less invasive than brain imaging used in most of these intelligent systems, allowing our method to be used in environments of any socioeconomic range.

The use of automatic systems for medical image classification has revolutionized the diagnosis of a high number of diseases. These alternatives, which are usually based on artificial intelligence (AI), provide a helpful tool for clinicians, eliminating the inter and intra-observer variability that the diagnostic process entails. Convolutional Neural Network (CNNs) have proved to be an excellent option for this purpose, demonstrating a large performance in a wide range of contexts. However, it is also extremely important to quantify the reliability of the model’s predictions in order to guarantee the confidence in the classification. In this work, we propose a multi-level ensemble classification system based on a Bayesian Deep Learning approach in order to maximize performance while providing the uncertainty of each classification decision. This tool combines the information extracted from different architectures by weighting their results according to the uncertainty of their predictions. Performance is evaluated in a wide range of real scenarios: in the first one, the aim is to differentiate between different pulmonary pathologies: controls vs bacterial pneumonia vs viral pneumonia. A two-level decision tree is employed to divide the 3-class classification into two binary classifications, yielding an accuracy of 98.19%. In the second context, performance is assessed for the diagnosis of Parkinson’s disease, leading to an accuracy of 95.31%. The reduced preprocessing needed for obtaining this high performance, in addition to the information provided about the reliability of the predictions evidence the applicability of the system to be used as an aid for clinicians.

The prevalence of dementia is currently increasing worldwide. This syndrome produces a deterioration in cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing its progress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessment in which an individual has to manually draw a clock on a paper. There are a lot of scoring systems for this test and most of them depend on the subjective assessment of the expert. This study proposes a computer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDT and obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessing pipeline in which the clock is detected, centered and binarized to decrease the computational burden. Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informative patterns within the CDT drawings that are relevant for the assessment of the patient's cognitive status. Performance is evaluated in a real context where patients with CI and controls have been classified by clinical experts in a balanced sample size of [Formula: see text] drawings. The proposed method provides an accuracy of [Formula: see text] in the binary case-control classification task, with an AUC of [Formula: see text]. These results are indeed relevant considering the use of the classic version of the CDT. The large size of the sample suggests that the method proposed has a high reliability to be used in clinical contexts and demonstrates the suitability of CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods are applied to identify the most relevant regions during classification. Finding these patterns is extremely helpful to understand the brain damage caused by CI. A validation method using resubstitution with upper bound correction in a machine learning approach is also discussed.

BackgroundFrailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. ObjectiveThe objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC). MethodsA search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables. ResultsWe identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ≥90. ConclusionsThe findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.

The emergence of new technologies has changed the way clinicians perform diagnosis. Medical imaging play a crucial role in this process, given the amount of information that they usually provide as non-invasive techniques. Despite the high quality offered by these images and the expertise of clinicians, the diagnostic process is not a straightforward task since different pathologies can have similar signs and symptoms. For this reason, it is extremely useful to assist this process with the inclusion of an automatic tool that reduces the bias when analyzing this kind of images. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify relevant patterns while providing information about the reliability of the classification. Specifically, each image is divided into patches and features contained in each one of them are extracted by applying kernel principal component analysis (PCA). The use of base classifiers within an ensemble allows our system to identify the informative patterns regardless of their size or location. Decisions of each individual patch are then combined according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario where distinguishing between pneumonia patients and controls from chest Computed Tomography (CCT) images, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would highly increase the computational cost) evidence the applicability of our proposal in a real-world environment.