NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features

Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer’s Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.La versión final de este artículo fue publicada el 29 de junio de 2023 en Brain Topography (Springer). Se encuentra accesible desde Biblioteca Di Tella a través de Prim

A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings.

Introduction: The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects. Patients: MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). Methods: For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected. Results: In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis. Chirped-pulse φOTDR provides distributed strain measurement via a time-delay estimation process. We propose a lower bound for performance, after reducing sampling error and compensating phase-noise. We attempt to reach the limit, attaining unprecedented pε/√Hz sensitivities. Conclusion: In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.Secretaría de Estado de Investigación, Desarrollo e InnovaciónInstituto de Salud Carlos II

Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided

Computer-aided diagnosis of multiple sclerosis using a support vector machine and optical coherence tomography features

The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of optic neuritis and forty-eight healthy control subjects were selected. Swept-source optical coherence tomography (SS-OCT) was performed using a DRI (deep-range imaging) Triton OCT device (Topcon Corp., Tokyo, Japan). Mean values (right and left eye) for macular thickness (retinal and choroidal layers) and peripapillary area (retinal nerve fibre layer, retinal, ganglion cell layer—GCL, and choroidal layers) were compared between both groups. Based on the analysis of the area under the receiver operator characteristic curve (AUC), the 3 variables with the greatest discriminant capacity were selected to form the feature vector. A SVM was used as an automatic classifier, obtaining the confusion matrix using leave-one-out cross-validation. Classification performance was assessed with Matthew’s correlation coefficient (MCC) and the AUCCLASSIFIER. The most discriminant variables were found to be the total GCL++ thickness (between inner limiting membrane to inner nuclear layer boundaries), evaluated in the peripapillary area and macular retina thickness in the nasal quadrant of the outer and inner rings. Using the SVM classifier, we obtained the following values: MCC = 0.81, sensitivity = 0.89, specificity = 0.92, accuracy = 0.91, and AUCCLASSIFIER = 0.97. Our findings suggest that it is possible to classify control subjects and MS patients without previous optic neuritis by applying machine-learning techniques to study the structural neurodegeneration in the retina

Computational methods for new clinical applications using imaging techniques

Esta tesis tiene por objetivo desarrollar diferentes métodos computacionales con aplicación clínica en varias enfermedades. De este modo, la investigación aquí presentada pretende aumentar el conocimiento sobre cómo el análisis y el estudio de los datos procedentes de técnicas de imagen pueden convertirse en un gran valor clínico para los profesionales de la medicina. Por lo tanto, dichos métodos pueden ser incorporados en la práctica clínica, lo que supone un beneficio para el paciente.Por un lado, la mejora de los diferentes dispositivos de imagen aumenta el abanico de posibilidades de análisis y presentación de los datos. Algunas técnicas de imagen arrojan directamente datos numéricos que tradicionalmente sólo se usaban para la monitorización de enfermedades. Sin embargo, dichos datos pueden ser empleados como biomarcadores tanto para el diagnóstico como para la predicción de enfermedades mediante la inteligencia artificial. Hoy en día, la inteligencia artificial se utiliza en muchos campos ya que todo lo que proporciona datos es abordable por estas nuevas tecnologías. Parece que no hay límite y se están desarrollando nuevas aplicaciones que hace sólo unas décadas parecían imposibles.Por otro lado, las técnicas de imagen nos permiten analizar diferentes partes del cuerpo humano en los respectivos pacientes y compararlas con controles sanos. Del mismo modo, con las imágenes se puede realizar el seguimiento de los tratamientos aplicados en dichos pacientes y, así, verificar su eficacia. Además, estas tecnologías, que proporcionan imágenes de alta resolución, son fáciles de usar, rentables y objetivas.Para resumir, esta tesis se ha centrado en desarrollar varias aplicaciones clínicas, basadas en los métodos numéricos descritos, que podrían ser una poderosa herramienta para aportar mayor información que ayude a los clínicos en la toma de decisiones.This thesis aims to develop different computational methods with clinical application in various diseases. In this way, the research presented here aims to increase knowledge on how the analysis and study of data from imaging techniques can be of great clinical value to medical professionals. Therefore, these methods can be incorporated into clinical practice, which is of benefit to the patient. On the one hand, the improvement of different imaging devices increases the range of possibilities for data analysis and presentation. Some imaging techniques directly yield numerical data that were traditionally only used for disease monitoring. However, these data can be used as biomarkers for both diagnosis and disease prediction using artificial intelligence. Today, artificial intelligence is used in many fields as everything that provides data can be addressed by these new technologies. There seems to be no limit and new applications are being developed that only a few decades ago seemed impossible. On the other hand, imaging techniques allow us to analyse different parts of the human body in the respective patients and compare them with healthy controls. In the same way, imaging can be used to monitor the treatments applied to these patients and, thus, verify their efficacy. Moreover, these technologies, which provide high-resolution images, are easy to use, cost-effective and objective. To summarise, this thesis has focused on developing several clinical applications, based on the described numerical methods, which could be a powerful tool to provide further information to help clinicians in decision making.<br /

Multivariate Analysis of MR Images in Temporal Lobe Epilepsy

Epilepsy stands aside from other neurological diseases because clinical patterns of progression are unknown: The etiology of each epilepsy case is unique and so it is the individual prognosis. Temporal lobe epilepsy (TLE) is the most frequent type of focal epilepsy and the surgical excision of the hippocampus and the surrounding tissue is an accepted treatment in refractory cases, specially when seizures become frequent increasingly affecting the performance of daily tasks and significantly decreasing the quality of life of the patient. The sensitivity of clinical imaging is poor for patients with no hippocampal involvement and invasive procedures such as the Wada test and intracranial EEG are required to detect and lateralize epileptogenic tissue. This thesis develops imaging processing techniques using quantitative relaxometry and diffusion tensor imaging with the aiming to provide a less invasive alternative when detectability is low. Chapter 2 develops the concept of individual feature maps on regions of interest. A laterality score on these maps correctly distinguished left TLE from right TLE in 12 out of 15 patients. Chapter 3 explores machine learning models to detect TLE, obtaining perfect classification for left patients, and 88.9% accuracy for right TLE patients. Chapter 4 focuses on temporal lobe asymmetry developing a voxel-based method for assessing asymmetry and verifying its applicability to individual predictions (92% accuracy) and group-wise statistical analyses. Informative ROI and voxel-based informative features are described for each experiment, demonstrating the relative importance of mean diffusivity over other MR imaging alternatives in identification and lateralization of TLE patients. Finally, the conclusion chapter discuss contributions, main limitations and outlining options for future research