An evaluation of the impact of mHealth interventions on patients’ attendance to treatment for three common ophthalmic diseases that cause blindness: a systematic review

Background: Ophthalmic diseases are those that affect the eye, including cataracts, glaucoma, age-related macular degeneration and diabetic retinopathy. These diseases can lead to blindness and vision loss, especially at advanced stages. Cataracts, glaucoma and diabetic retinopathy are the most common ophthalmic diseases that cause blindness. Patients encounter challenges with attendance to appointments for treatment because they may forget the date, time and/or place of the surgery. mHealth interventions are a means of addressing the challenge of patients missing appointments. This study reviews the use of mHealth reminders to improve patients’ attendance to ophthalmic disease treatment. Methods: A systematic review was conducted to assess the literature from various databases including; PubMed, Scopus, (Africa-Wide Information, CINAHL, Computers & Applied Sciences Complete, Health Source: Nursing/Academic Edition by Ebscohost) and Web of Science. We searched different sources for grey literature including; Google.com, Open Grey, New York Academy of Medicine, WHO, Cochran library, and Cochrane Central Register of Controlled Trials. The interventions were limited to SMS and telephone calls. Studies were considered eligible if they were randomized control trials (RCT), prospective or retrospective cohort studies, cross-sectional studies, or if they reported on outcomes primarily related to patient attendance to ophthalmic disease appointments. Results: Ten studies that met the eligibility criteria were included in the systematic review. The study setting included developed countries and low-and-middle-income countries (LMIC). Sixty percent of these studies were conducted in LMIC, while forty percent were conducted in developed countries. Eighty percent of the study participants were older than 55 years and the mean age of participants was 61.5 years. Both male and female participants were included, with approximately fifty nine percent of them being female. Discussion: The assessment of the literature highlighted that mHealth reminders resulted in significant improvement in patient attendance to treatment for the three common ophthalmic diseases. The mHealth platform was particularly relevant in LMIC, and SMS was the most successful intervention. Women were the major users of mHealth tools to gain access to services. Conclusion: This systematic review aimed to inform healthcare workers and decision makers in the health system on the use of mobile phone messaging as reminders to improve patient attendance to the three common ophthalmic diseases treatments that cause blindness. The evidence obtained from the systematic review will bring new opportunities for further research regarding the use of mHealth interventions as reminders for treatment adherence in general and ophthalmic diseases such as cataracts, glaucoma and diabetic retinopathy

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Deep learning analysis of eye fundus images to support medical diagnosis

Machine learning techniques have been successfully applied to support medical decision making of cancer, heart diseases and degenerative diseases of the brain. In particular, deep learning methods have been used for early detection of abnormalities in the eye that could improve the diagnosis of different ocular diseases, especially in developing countries, where there are major limitations to access to specialized medical treatment. However, the early detection of clinical signs such as blood vessel, optic disc alterations, exudates, hemorrhages, drusen, and microaneurysms presents three main challenges: the ocular images can be affected by noise artifact, the features of the clinical signs depend specifically on the acquisition source, and the combination of local signs and grading disease label is not an easy task. This research approaches the problem of combining local signs and global labels of different acquisition sources of medical information as a valuable tool to support medical decision making in ocular diseases. Different models for different eye diseases were developed. Four models were developed using eye fundus images: for DME, it was designed a two-stages model that uses a shallow model to predict an exudate binary mask. Then, the binary mask is stacked with the raw fundus image into a 4-channel array as an input of a deep convolutional neural network for diabetic macular edema diagnosis; for glaucoma, it was developed three deep learning models. First, it was defined a deep learning model based on three-stages that contains an initial stage for automatically segment two binary masks containing optic disc and physiological cup segmentation, followed by an automatic morphometric features extraction stage from previous segmentations, and a final classification stage that supports the glaucoma diagnosis with intermediate medical information. Two late-data-fusion methods that fused morphometric features from cartesian and polar segmentation of the optic disc and physiological cup with features extracted from raw eye fundus images. On the other hand, two models were defined using optical coherence tomography. First, a customized convolutional neural network termed as OCT-NET to extract features from OCT volumes to classify DME, DR-DME and AMD conditions. In addition, this model generates images with highlighted local information about the clinical signs, and it estimates the number of slides inside a volume with local abnormalities. Finally, a 3D-Deep learning model that uses OCT volumes as an input to estimate the retinal thickness map useful to grade AMD. The methods were systematically evaluated using ten free public datasets. The methods were compared and validated against other state-of-the-art algorithms and the results were also qualitatively evaluated by ophthalmology experts from Fundación Oftalmológica Nacional. In addition, the proposed methods were tested as a diagnosis support tool of diabetic macular edema, glaucoma, diabetic retinopathy and age-related macular degeneration using two different ocular imaging representations. Thus, we consider that this research could be potentially a big step in building telemedicine tools that could support medical personnel for detecting ocular diseases using eye fundus images and optical coherence tomography.Las técnicas de aprendizaje automático se han aplicado con éxito para apoyar la toma de decisiones médicas sobre el cáncer, las enfermedades cardíacas y las enfermedades degenerativas del cerebro. En particular, se han utilizado métodos de aprendizaje profundo para la detección temprana de anormalidades en el ojo que podrían mejorar el diagnóstico de diferentes enfermedades oculares, especialmente en países en desarrollo, donde existen grandes limitaciones para acceder a tratamiento médico especializado. Sin embargo, la detección temprana de signos clínicos como vasos sanguíneos, alteraciones del disco óptico, exudados, hemorragias, drusas y microaneurismas presenta tres desafíos principales: las imágenes oculares pueden verse afectadas por artefactos de ruido, las características de los signos clínicos dependen específicamente de fuente de adquisición, y la combinación de signos locales y clasificación de la enfermedad no es una tarea fácil. Esta investigación aborda el problema de combinar signos locales y etiquetas globales de diferentes fuentes de adquisición de información médica como una herramienta valiosa para apoyar la toma de decisiones médicas en enfermedades oculares. Se desarrollaron diferentes modelos para diferentes enfermedades oculares. Se desarrollaron cuatro modelos utilizando imágenes de fondo de ojo: para DME, se diseñó un modelo de dos etapas que utiliza un modelo superficial para predecir una máscara binaria de exudados. Luego, la máscara binaria se apila con la imagen de fondo de ojo original en una matriz de 4 canales como entrada de una red neuronal convolucional profunda para el diagnóstico de edema macular diabético; para el glaucoma, se desarrollaron tres modelos de aprendizaje profundo. Primero, se definió un modelo de aprendizaje profundo basado en tres etapas que contiene una etapa inicial para segmentar automáticamente dos máscaras binarias que contienen disco óptico y segmentación fisiológica de la copa, seguido de una etapa de extracción de características morfométricas automáticas de segmentaciones anteriores y una etapa de clasificación final que respalda el diagnóstico de glaucoma con información médica intermedia. Dos métodos de fusión de datos tardíos que fusionaron características morfométricas de la segmentación cartesiana y polar del disco óptico y la copa fisiológica con características extraídas de imágenes de fondo de ojo crudo. Por otro lado, se definieron dos modelos mediante tomografía de coherencia óptica. Primero, una red neuronal convolucional personalizada denominada OCT-NET para extraer características de los volúmenes OCT para clasificar las condiciones DME, DR-DME y AMD. Además, este modelo genera imágenes con información local resaltada sobre los signos clínicos, y estima el número de diapositivas dentro de un volumen con anomalías locales. Finalmente, un modelo de aprendizaje 3D-Deep que utiliza volúmenes OCT como entrada para estimar el mapa de espesor retiniano útil para calificar AMD. Los métodos se evaluaron sistemáticamente utilizando diez conjuntos de datos públicos gratuitos. Los métodos se compararon y validaron con otros algoritmos de vanguardia y los resultados también fueron evaluados cualitativamente por expertos en oftalmología de la Fundación Oftalmológica Nacional. Además, los métodos propuestos se probaron como una herramienta de diagnóstico de edema macular diabético, glaucoma, retinopatía diabética y degeneración macular relacionada con la edad utilizando dos representaciones de imágenes oculares diferentes. Por lo tanto, consideramos que esta investigación podría ser potencialmente un gran paso en la construcción de herramientas de telemedicina que podrían ayudar al personal médico a detectar enfermedades oculares utilizando imágenes de fondo de ojo y tomografía de coherencia óptica.Doctorad

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Severity scoring approach using modified optical flow method and lesion identification for facial nerve paralysis assessment

The facial nerve controls facial movement and expression. Hence, a patient with facial nerve paralysis will experience affected social interactions, psychological distress, and low self-esteem. Upon the first presentation, it is crucial to determine the severity level of the paralysis and take out the possibility of stroke or any other serious causes by recognising the type of lesion in preventing any mistreatment of the patient. Clinically, the facial nerve is assessed subjectively by observing voluntary facial movement and assigning a score based on the deductions made by the clinician. However, the results are not uniform among different examiners evaluating the same patients. This is extremely undesirable for both medical diagnostic and treatment considerations. Acknowledging the importance of this assessment, this research was conducted to develop a facial nerve assessment that can classify both the severity level of facial nerve function and also the types of facial lesion, Upper Motor Neuron (UMN) and Lower Motor Neuron (LMN), in facial regional assessment and lesion assessment, respectively. For regional assessment, two optical flow techniques, Kanade-Lucas-Tomasi (KLT) and Horn-Schunck (HS) were used in this study to determine the local and global motion information of facial features. Nevertheless, there is a problem with the original KLT which is the inability of the Eigen features to distinguish the normal and patient subjects. Thus, the KLT method was modified by introducing polygonal measurements and the landmarks were placed on each facial region. Similar to the HS method, the multiple frames evaluation was proposed rather than a single frame evaluation of the original HS method to avoid the differences between frames becoming too small. The features of these modified methods, Modified Local Sparse (MLS) and Modified Global Dense (MGD), were combined, namely the Combined Modified Local-Global (CMLG), to discover both local (certain region) and global (entire image) flow features. This served as the input into the k-NN classifier to assess the performance of each of them in determining the severity level of paralysis. For the lesion assessment, the Gabor filter method was used to extract the wrinkle forehead features. Thereafter, the Gabor features combined with the previous features of CMLG, by focusing only on the forehead region to evaluate both the wrinkle and motion information of the facial features. This is because, in an LMN lesion, the patient will not be able to move the forehead symmetrically during the rising eyebrows movement and unable to wrinkle the forehead due to the damaged frontalis muscle. However, the patient with a UMN lesion exhibits the same criteria as a normal subject, where the forehead is spared and can be lifted symmetrically. The CMLG technique in regional assessment showed the best performance in distinguishing between patient and normal subjects with an accuracy of 92.26% compared to that of MLS and MGD, which were 88.38% and 90.32%, respectively. From the results, some assessment tools were developed in this study namely individual score, total score and paralysis score chart which were correlated with the House-Brackmann score and validated by a medical professional with 91.30% of accuracy. In lesion assessment, the combined features of Gabor and CMLG on the forehead region depicted a greater performance in distinguishing the UMN and LMN lesion of the patient with an accuracy of 89.03% compared to Gabor alone, which was 78.07%. In conclusion, the proposed facial nerve assessment approach consisting of both regional assessment and lesion assessment is capable of determining the level of facial paralysis severity and recognising the type of facial lesion, whether it is a UMN or LMN lesion

Les mixtures de Dirichlet et leurs apports pour la classification et la recherche d'images par le contenu

Le développement de la médecine moderne dans le domaine des techniques de diagnostic comme la radiologie, l'histopathologie et la tomographie avait comme résultat l'explosion du nombre et de l'importance des images médicales sauvegardées par la majorité des hôpitaux. Afin d'aider les médecins à confirmer leurs diagnostics, plusieurs systèmes de recherche d'images médicales ont vu le jour. La conception de ces systèmes présente plusieurs étapes. Nous pensons que le résumé des bases de données d'images est une étape importante dans chaque système de recherche. En effet, la catégorisation d'une base de données d'images facilite énormément la recherche et permet de localiser les images voulues en un minimum de temps. Dans ce mémoire, nous étudions en un premier temps, les différents problèmes communs à tous les systèmes de recherche d'images à savoir l'indexation, l'extraction des caractéristiques, la définition des mesures de similarités et le retour de pertinence. Nous étudions aussi d'autres catégories de problèmes spécifiques à la recherche d'images. Cette étude est complétée par une analyse des systèmes existants les plus connus. Dans la deuxième partie du mémoire, nous nous intéressons aux mixtures de Dirichlet et comment on peut les exploiter pour la classification, en particulier le résumé des bases de données d'images. Contrairement aux approches classiques qui considèrent la loi normale comme densité, nous utilisons une généralisation de la Dirichlet pour l'adapter plus aux problèmes réels. Notre approche est traduite par un modèle mathématique basé sur le maximum de vraisemblance et la méthode de Fisher. Une interprétation très intéressante de notre méthode, basée sur la statistique géométrique, est donnée. Finalement, nous présentons des évaluations contextuelles et non-contextuelles, qui prouvent la validité de notre méthode