Análisis avanzado de registros de electrorretinografía multifocal aplicado al diagnóstico de esclerosis múltiple

La esclerosis múltiple (EM) es una enfermedad desmielinizante, adquirida, crónica, que impide el funcionamiento normal de la sustancia blanca del sistema nervioso central. En un gran número de casos, la vía visual se ve afectada durante el curso de la EM, e incluso en fases previas a la enfermedad. Por este motivo, es pertinente el estudio de la estructura y función de la vía visual en el diagnóstico. La técnica electrofisiológica de electrorretinografía multifocal (mfERG) permite obtener la respuesta retiniana en un número elevado de zonas de la retina. Muy pocos trabajos previos investigan la capacidad discriminante del análisis de las señales de mfERG para el diagnóstico de EM, todos ellos utilizan el análisis clásico de amplitudes y latencias y, los resultados obtenidos en estos estudios no son concluyentes. El objetivo de la presente tesis ha sido explorar la capacidad de la electrorretinografía multifocal para la investigación y el diagnóstico clínico de esclerosis múltiple, utilizando algoritmos avanzados de análisis de señal. Se ha utilizado una base de datos de registros mfERG de dos grupos de sujetos: 6 controles (M:H=3:3) y 10 pacientes con diagnóstico de esclerosis múltiple, sin historial de neuritis óptica (M:H=7:3) obtenidos mediante el equipo Reti-Port/scan 21 de Roland. Las señales de mfERG han sido analizadas mediante diversas técnicas matemáticas hasta ahora no aplicadas en este campo clínico, con el objetivo de facilitar nuevos biomarcadores para la detección de EM. Estas técnicas son el análisis espectral singular, la representación dispersa de una señal y la descomposición empírica en modos. Además se propone el empleo de redes neuronales, la utilización de la función de correlación como característica discriminante y la realización de un análisis topográfico más detallado para mejorar su aplicabilidad. La capacidad discriminante de los métodos propuestos ha sido evaluada mediante el área bajo la curva ROC: AUC. Mediante el análisis de los marcadores de amplitudes y latencias, se obtienen valores de media de AUC inferiores a 0,6158, mientras que tras el empleo de las técnicas matemáticas descritas, se consigue mejorar en gran medida la capacidad de discriminación: redes neuronales, AUC de 0,7650; análisis espectral singular, AUC de 0,8348; representación dispersa, AUC de 0,7515; descomposición empírica en modos, AUC de 0,8726; y análisis topográfico, AUC de 0,8854. En todos los métodos de análisis de los registros de mfERG propuestos los valores de discriminación entre controles y pacientes son superiores a los conseguidos con la técnica tradicional de análisis de amplitud y latencias. Dichos resultados sugieren que el análisis de los registros mfERG sería aplicable para el diagnóstico de esclerosis múltiple en sus fases iniciales

Applying systematic review search methods to the grey literature: a review of education and training courses on breastfeeding support for health professionals

Background: Currently, lactation training courses aimed at health professionals are important for informing and supporting mothers who are breastfeeding. In this review, we seek to analyze similarities and/or variations in course content, modes of delivery, costs, teaching style and learning strategies among courses. To our knowledge, a review of lactation training courses available worldwide is lacking. Thus, the aim of this review is to describe course models aimed at training health professionals in lactation support for mothers. Methods: Through searching grey literature, training courses were obtained from several directories, including the Alaska Breastfeeding Coalition, International Board of Lactation Consultant Examiners (IBLCE), International Lactation Consultant Association (ILCA), Lactation Education Accreditation Association and Approval Review Committee (LEAARC), World Alliance for Breastfeeding Action (WABA), World Health Organization (WHO), and United Nations Children's Fund (UNICEF). Results: Descriptions of ten training programs were included in the final review. Our group found variations in costs, modes of delivery and duration among courses. Conclusions: Certified training for health professionals in lactation is a promising approach for increasing and protecting breastfeeding. Breastfeeding mothers might also benefit from specifically trained health professionals, yet, well-conducted research on such training courses is still required. The variability in the mode of teaching, tuition costs and course content in breastfeeding education programs around the globe must be kept in mind when considering providing training on the optimal competency for health professionals

Leveraging Equivariant Features for Absolute Pose Regression

While end-to-end approaches have achieved state-of-the-art performance in many perception tasks, they are not yet able to compete with 3D geometry-based methods in pose estimation. Moreover, absolute pose regression has been shown to be more related to image retrieval. As a result, we hypothesize that the statistical features learned by classical Convolutional Neural Networks do not carry enough geometric information to reliably solve this inherently geometric task. In this paper, we demonstrate how a translation and rotation equivariant Convolutional Neural Network directly induces representations of camera motions into the feature space. We then show that this geometric property allows for implicitly augmenting the training data under a whole group of image plane-preserving transformations. Therefore, we argue that directly learning equivariant features is preferable than learning data-intensive intermediate representations. Comprehensive experimental validation demonstrates that our lightweight model outperforms existing ones on standard datasets.Comment: 11 pages, 8 figures, CVPR202

CubeSat-CDT: A Cross-Domain Dataset for 6-DoF Trajectory Estimation of a Symmetric Spacecraft

This paper introduces a new cross-domain dataset, CubeSat- CDT, that includes 21 trajectories of a real CubeSat acquired in a labora- tory setup, combined with 65 trajectories generated using two rendering engines – i.e. Unity and Blender. The three data sources incorporate the same 1U CubeSat and share the same camera intrinsic parameters. In ad- dition, we conduct experiments to show the characteristics of the dataset using a novel and efficient spacecraft trajectory estimation method, that leverages the information provided from the three data domains. Given a video input of a target spacecraft, the proposed end-to-end approach re- lies on a Temporal Convolutional Network that enforces the inter-frame coherence of the estimated 6-Degree-of-Freedom spacecraft poses. The pipeline is decomposed into two stages; first, spatial features are ex- tracted from each frame in parallel; second, these features are lifted to the space of camera poses while preserving temporal information. Our re- sults highlight the importance of addressing the domain gap problem to propose reliable solutions for close-range autonomous relative navigation between spacecrafts. Since the nature of the data used during training impacts directly the performance of the final solution, the CubeSat-CDT dataset is provided to advance research into this direction

Leveraging Equivariant Features for Absolute Pose Regression

Pose estimation enables vision-based systems to refer to their environment, supporting activities ranging from scene navigation to object manipulation. However, end-to-end approaches, that have achieved state-of-the-art performance in many perception tasks, are still unable to compete with 3D geometry-based methods in pose estimation. Indeed, absolute pose regression has been proven to be more related to image retrieval than to 3D structure. Our assumption is that statistical features learned by classical convolutional neural networks do not carry enough geometrical information for reliably solving this task. This paper studies the use of deep equivariant features for end-to-end pose regression. We further propose a translation and rotation equivariant Convolutional Neural Network whose architecture directly induces representations of camera motions into the feature space. In the context of absolute pose regression, this geometric property allows for implicitly augmenting the training data under a whole group of image plane-preserving transformations. Therefore, directly learning equivariant features efficiently compensates for learning intermediate representations that are indirectly equivariant yet data-intensive. Extensive experimental validation demonstrates that our lightweight model outperforms existing ones on standard datasets

Identification of clusters in multifocal electrophysiology recordings to maximize discriminant capacity (patients vs. control subjects)

Purpose To propose a new method of identifying clusters in multifocal electrophysiology (multifocal electroretinogram: mfERG; multifocal visual-evoked potential: mfVEP) that conserve the maximum capacity to discriminate between patients and control subjects. Methods The theoretical framework proposed creates arbitrary N-size clusters of sectors. The capacity to discriminate between patients and control subjects is assessed by analysing the area under the receiver operator characteristic curve (AUC). As proof of concept, the method is validated using mfERG recordings taken from both eyes of control subjects (n = 6) and from patients with multiple sclerosis (n = 15). Results Considering the amplitude of wave P1 as the analysis parameter, the maximum value of AUC = 0.7042 is obtained with N = 9 sectors. Taking into account the AUC of the amplitudes and latencies of waves N1 and P1, the maximum value of the AUC = 0.6917 with N = 8 clustered sectors. The greatest discriminant capacity is obtained by analysing the latency of wave P1: AUC = 0.8854 with a cluster of N = 12 sectors. Conclusion This paper demonstrates the effectiveness of a method able to determine the arbitrary clustering of multifocal responses that possesses the greatest capacity to discriminate between control subjects and patients when applied to the visual field of mfERG or mfVEP recordings. The method may prove helpful in diagnosing any disease that is identifiable in patients’ mfERG or mfVEP recordings and is extensible to other clinical tests, such as optical coherence tomography

Differential Study of Retinal Thicknesses in the Eyes of Alzheimer"s Patients, Multiple Sclerosis Patients and Healthy Subjects

Multiple sclerosis (MS) and Alzheimer"s disease (AD) cause retinal thinning that is detectable in vivo using optical coherence tomography (OCT). To date, no papers have compared the two diseases in terms of the structural differences they produce in the retina. The purpose of this study is to analyse and compare the neuroretinal structure in MS patients, AD patients and healthy subjects using OCT. Spectral domain OCT was performed on 21 AD patients, 33MS patients and 19 control subjects using the Posterior Pole protocol. The area under the receiver operating characteristic (AUROC) curve was used to analyse the differences between the cohorts in nine regions of the retinal nerve fibre layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL) and outer nuclear layer (ONL). The main differences between MS and AD are found in the ONL, in practically all the regions analysed (AUROCFOVEAL = 0.80, AUROCPARAFOVEAL = 0.85, AUROCPERIFOVEAL = 0.80, AUROC_PMB = 0.77, AUROCPARAMACULAR = 0.85, AUROCINFERO_NASAL = 0.75, AUROCINFERO_TEMPORAL = 0.83), and in the paramacular zone (AUROCPARAMACULAR = 0.75) and infero-temporal quadrant (AUROCINFERO_TEMPORAL = 0.80) of the GCL. In conclusion, our findings suggest that OCT data analysis could facilitate the differential diagnosis of MS and AD

Empirical mode decomposition-based filter applied to multifocal electroretinograms in multiple sclerosis diagnosis

As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients

Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI?port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.Agencia Estatal de InvestigaciónInstituto de Salud Carlos II

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