Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis.

Multiple sclerosis (MS) is a progressive demyelinating disease impacting the central nervous system. Conventional Magnetic Resonance Imaging (MRI) techniques (e.g., T2w images) help diagnose MS, although they sometimes reveal non-specific lesions. Quantitative MRI techniques are capable of quantifying imaging biomarkers in vivo, offering the potential to identify specific signs related to pre-clinical inflammation. Among those techniques, Quantitative Susceptibility Mapping (QSM) is particularly useful for studying processes that influence the magnetic properties of brain tissue, such as alterations in myelin concentration. Because of its intrinsic quantitative nature, it is particularly well-suited to be analyzed through radiomics, including techniques that extract a high number of complex and multi-dimensional features from radiological images. The dataset presented in this work provides information about normal-appearing white matter (NAWM) in a cohort of MS patients and healthy controls. It includes QSM-based radiomic features from NAWM and its tracts, and MR sequences necessary to implement the pipeline: T1w, T2w, QSM, DWI. The workflow is outlined in this article, along with an application showing feature reliability assessment.

Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis.

Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.

Full-Reference Image Quality Expression via Genetic Programming.

Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.

Determinants of academic achievement: How parents and teachers influence high school students' performance.

This study explores the contribution of various drivers of attainment in secondary education in Portugal. We propose a model explaining the influence of students, teachers, and parents' traits on high school achievement, measured by the self-reported Math and Portuguese final grades of 220 students. Using PLS-SEM, we show that previous achievement predicts current achievement in both subjects; however, noteworthy differences were found. Portuguese grades are significantly better for students whose parents have post-secondary education and communicate higher expectations about their offspring's school careers. At the same time, Math achievement is influenced by students' perception of teachers' involvement but not by parents' expectations or education. Previous retention and receiving educational allowance impair Math achievement, but not Portuguese. Results and implications are discussed.

A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images.

Neovascular age-related macular degeneration (nAMD) is one of the major causes of irreversible blindness and is characterized by accumulations of different lesions inside the retina. AMD biomarkers enable experts to grade the AMD and could be used for therapy prognosis and individualized treatment decisions. In particular, intra-retinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelium detachment (PED) are prominent biomarkers for grading neovascular AMD. Spectral-domain optical coherence tomography (SD-OCT) revolutionized nAMD early diagnosis by providing cross-sectional images of the retina. Automatic segmentation and quantification of IRF, SRF, and PED in SD-OCT images can be extremely useful for clinical decision-making. Despite the excellent performance of convolutional neural network (CNN)-based methods, the task still presents some challenges due to relevant variations in the location, size, shape, and texture of the lesions. This work adopts a transformer-based method to automatically segment retinal lesion from SD-OCT images and qualitatively and quantitatively evaluate its performance against CNN-based methods. The method combines the efficient long-range feature extraction and aggregation capabilities of Vision Transformers with data-efficient training of CNNs. The proposed method was tested on a private dataset containing 3842 2-dimensional SD-OCT retina images, manually labeled by experts of the Franziskus Eye-Center, Muenster. While one of the competitors presents a better performance in terms of Dice score, the proposed method is significantly less computationally expensive. Thus, future research will focus on the proposed network's architecture to increase its segmentation performance while maintaining its computational efficiency.

A stacking-based artificial intelligence framework for an effective detection and localization of colon polyps.

Polyp detection through colonoscopy is a widely used method to prevent colorectal cancer. The automation of this process aided by artificial intelligence allows faster and improved detection of polyps that can be missed during a standard colonoscopy. In this work, we propose to implement various object detection algorithms for polyp detection. To improve the mean average precision (mAP) of the detection, we combine the baseline models through a stacking approach. The experiments demonstrate the potential of this new methodology, which can reduce the workload for oncologists and increase the precision of the localization of polyps. Our proposal achieves a mAP of 0.86, translated into an improvement of 34.9% compared to the best baseline model and 28.8% with respect to the weighted boxes fusion ensemble technique.

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.

Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.

Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey.

Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study.

Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features.

Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies. Therefore, we propose a Generative Adversarial Network (GAN)-based lesion-focused framework for Computed Tomography (CT) image Super-Resolution (SR); for the lesion (i.e., cancer) patch-focused training, we incorporate Spatial Pyramid Pooling (SPP) into GAN-Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). At [Formula: see text] SR, the proposed model achieved better perceptual quality with less blurring than the other considered state-of-the-art SR methods, while producing comparable results at [Formula: see text] SR. We also evaluated the robustness of our model's radiomic feature in terms of quantization on a different lung cancer CT dataset using Principal Component Analysis (PCA). Intriguingly, the most important radiomic features in our PCA-based analysis were the most robust features extracted on the GAN-super-resolved images. These achievements pave the way for the application of GAN-based image Super-Resolution techniques for studies of radiomics for robust biomarker discovery.

Object detection for automatic cancer cell counting in zebrafish xenografts.

Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes. In this paper, we introduce a deep learning-based cell detection and quantification methodology to automate the cell counting process in the zebrafish xenograft cancer model, an innovative technique for studying tumor biology and for personalizing medicine. First, we implemented a fine-tuned architecture based on the Faster R-CNN using the Inception ResNet V2 feature extractor. Second, we performed several adjustments to optimize the process, paying attention to constraints such as the presence of overlapped cells, the high number of objects to detect, the heterogeneity of the cells' size and shape, and the small size of the data set. This method resulted in a median error of approximately 1% of the total number of cell units. These results demonstrate the potential of our novel approach for quantifying cells in poorly labeled images. Compared to traditional Faster R-CNN, our method improved the average precision from 71% to 85% on the studied data set.

Generative adversarial networks for generating synthetic features for Wi-Fi signal quality.

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions. A common barrier faced by these specialized companies is the lack of data to develop and test their products. This paper investigates the use of generative adversarial networks (GANs), which are state-of-the-art generative models, for generating synthetic telecommunication data related to Wi-Fi signal quality. We developed, trained, and compared two of the most used GAN architectures: the Vanilla GAN and the Wasserstein GAN (WGAN). Both models presented satisfactory results and were able to generate synthetic data similar to the real ones. In particular, the distribution of the synthetic data overlaps the distribution of the real data for all of the considered features. Moreover, the considered generative models can reproduce the same associations observed for the synthetic features. We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.

Improving convolutional neural networks performance for image classification using test time augmentation: a case study using MURA dataset.

Bone fractures are one of the main causes to visit the emergency room (ER); the primary method to detect bone fractures is using X-Ray images. X-Ray images require an experienced radiologist to classify them; however, an experienced radiologist is not always available in the ER. An accurate automatic X-Ray image classifier in the ER can help reduce error rates by providing an instant second opinion to the emergency doctor. Deep learning is an emerging trend in artificial intelligence, where an automatic classifier can be trained to classify musculoskeletal images. Image augmentations techniques have proven their usefulness in increasing the deep learning model's performance. Usually, in the image classification domain, the augmentation techniques are used during training the network and not during the testing phase. Test time augmentation (TTA) can increase the model prediction by providing, with a negligible computational cost, several transformations for the same image. In this paper, we investigated the effect of TTA on image classification performance on the MURA dataset. Nine different augmentation techniques were evaluated to determine their performance compared to predictions without TTA. Two ensemble techniques were assessed as well, the majority vote and the average vote. Based on our results, TTA increased classification performance significantly, especially for models with a low score.

Using artificial intelligence to overcome over-indebtedness and fight poverty.

This research examines how artificial intelligence may contribute to better understanding and to overcome over-indebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a field database of 1654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning using Self-Organizing Maps generated three over-indebtedness clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). Second, supervised machine learning with exhaustive grid search hyperparameters (32,730 predictive models) suggests that Nu-Support Vector Machine had the best accuracy in predicting families' over-indebtedness risk factors (89.5%). By proposing an AutoML approach on over-indebtedness, our research adds both theoretically and methodologically to current models of scarcity with important practical implications for business research and society. Our findings also contribute to novel ways to identify and characterize poverty risk in earlier stages, allowing customized interventions for different profiles of over-indebtedness.

[What happens if algorithmic music meets medicine]. / Cosa succede se la musica algoritmica incontra la medicina.

SUMMARY: Since ancient times there has been recognition of music's therapeutic powers, inherent in the properties of sound and its effects on human beings at a psychophysical level. Literature showed the development of therapeutic applications of music in numerous clinical settings. Music-listening itself can qualify as an effective therapeutic means within clinical contexts. Numerous studies document the potentialities of this practice. Whilst, it appears to be difficult to study the phenomenon of music from a scientific point of view, it may be possible to attempt moving music closer to science. Algorithms are of help in this process. Only recently has algorithmic music been used within the context of composing music with therapeutic aims helping to create songs for precise therapeutic aims: music characteristics can be altered and re-modelled and, above all, simplified. It was exactly this intent that recently brought into being an algorithm, Melomics-Health, which composes music with a "therapeutic" logic. Melomics-Health allows us to study the effect of specific musical parameters and structures on individuals (including neuro-scientific aspects) with the possibility to correlate effectiveness and efficiency to those precise musical aspects and to re-model the latter based on these findings. The use of algorithms applied to music as therapy constitutes a new starting point, an attempt to bring art and science closer together, to increase awareness and effectiveness in the use of music in therapeutic contexts; a new perspective integrating art, science and technology in the service of medicine, in clinical work and research.

Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country.

Understanding academic achievement (AA) is one of the most global challenges, as there is evidence that it is deeply intertwined with economic development, employment, and countries' wellbeing. However, the research conducted on this topic grounds in traditional (statistical) methods employed in survey (sample) data. This paper presents a novel approach, using state-of-the-art artificial intelligence (AI) techniques to predict the academic achievement of virtually every public high school student in Portugal, i.e., 110,627 students in the academic year of 2014/2015. Different AI and non-AI methods are developed and compared in terms of performance. Moreover, important insights to policymakers are addressed.

Musculoskeletal Images Classification for Detection of Fractures Using Transfer Learning.

The classification of the musculoskeletal images can be very challenging, mostly when it is being done in the emergency room, where a decision must be made rapidly. The computer vision domain has gained increasing attention in recent years, due to its achievements in image classification. The convolutional neural network (CNN) is one of the latest computer vision algorithms that achieved state-of-the-art results. A CNN requires an enormous number of images to be adequately trained, and these are always scarce in the medical field. Transfer learning is a technique that is being used to train the CNN by using fewer images. In this paper, we study the appropriate method to classify musculoskeletal images by transfer learning and by training from scratch. We applied six state-of-the-art architectures and compared their performance with transfer learning and with a network trained from scratch. From our results, transfer learning did increase the model performance significantly, and, additionally, it made the model less prone to overfitting.

Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images.

The classification of histopathology images requires an experienced physician with years of experience to classify the histopathology images accurately. In this study, an algorithm was developed to assist physicians in classifying histopathology images; the algorithm receives the histopathology image as an input and produces the percentage of cancer presence. The primary classifier used in this algorithm is the convolutional neural network, which is a state-of-the-art classifier used in image classification as it can classify images without relying on the manual selection of features from each image. The main aim of this research is to improve the robustness of the classifier used by comparing six different first-order stochastic gradient-based optimizers to select the best for this particular dataset. The dataset used to train the classifier is the PatchCamelyon public dataset, which consists of 220,025 images to train the classifier; the dataset is composed of 60% positive images and 40% negative images, and 57,458 images to test its performance. The classifier was trained on 80% of the images and validated on the rest of 20% of the images; then, it was tested on the test set. The optimizers were evaluated based on their AUC of the ROC curve. The results show that the adaptative based optimizers achieved the highest results except for AdaGrad that achieved the lowest results.

Weighted Hierarchical Grammatical Evolution.

Grammatical evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings, of a language defined by a user-provided context-free grammar. In this paper, we propose a novel procedure for mapping genotypes to phenotypes that we call weighted hierarchical GE (WHGE). WHGE imposes a form of hierarchy on the genotype and encodes grammar symbols with a varying number of bits based on the relative expressive power of those symbols. WHGE does not impose any constraint on the overall GE framework, in particular, WHGE may handle recursive grammars, uses the classical genetic operators, and does not need to define any bound in advance on the size of phenotypes. We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results of the standard GE framework as well as two of the most significant enhancements proposed in the literature: 1) position-independent GE and 2) structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure.

Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial.

BACKGROUND: The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods. METHODS: Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation. RESULTS: A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency. CONCLUSIONS: The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.

Oficina de comunicación a distancia del Hospital de Pediatría "Prof. Dr. Juan P. Garrahan" ­ más de 20 años construyendo telemedicina / Outreach Communication Office of Hospital de Pediatría "Prof. Dr. Juan P. Garrahan" ­ more than 20 years building telemedicine

Introducción: El Hospital de Pediatría "Prof. Dr. Juan P. Garrahan" ha sido pionero en la atención de la salud de la población infantil del país y de Latinoamérica. El 53% de los pacientes que concurren al hospital viven más allá del área metropolitana de Buenos Aires. En agosto de 1997, se creó la Oficina de Comunicación a Distancia como una herramienta para sostener la continuidad asistencial de los pacientes de áreas remotas; su apertura significó el embrión del primer programa de telemedicina del país. Objetivos: Promover la construcción de redes integradas de servicios de salud. Impulsar nuevos procesos que mejoren el acceso a la salud. Metodología: Basados en la lógica de redes integradas de servicios de salud con la incorporación de las tecnologías de la información y la comunicación. Resultados: Desde 1997 hasta el primer semestre de 218 se crearon 283 OCD. Se asistieron más de 70.000 consultas asincrónicas y se realizaron más de 5000 videoconferencias. Conclusiones: La modalidad de comunicación a distancia y telemedicina estimula el trabajo colaborativo interinstitucional favoreciendo la atención de los pacientes en origen

Introduction: Hospital de Pediatría "Prof. Dr. Juan P. Garrahan" has been a Pioneer in health care for children in Argentina and Latin America. Overall, 53% of the patients that are covered by the hospital live outside the metropolitan area of Buenos Aires. In August 1997, the Outreach Communication Office (OCO) was created as a tool to sustain continuous care for patients living in remote areas; its inauguration was the seed for the first telemedicine program in the country. Objectives: To promote the development of networks of health-care services. To initiate processes to improve Access to health care. Methods: Development of comprehensive health-care networks with the incorporation of information and communication technologies. Results: Between 1997 and the first semester of 218, 283 OCO's were created. Overall, 70,000 asynchronous consultations were conducted and more than 5000 videoconferences were held. Conclusions: The modality of outreach communication and telemedicine encourages interinstitutional collaboration favoring patient care at their site of origin