AUTOMATED SCORING FOR SCALINESS OF PSORIASIS LESIONS USING EDGE DETECTION

Skin diseases affect 20-30% of the population at any one time, interfering with activities in 10%. Psoriasis, an Mamniatory skin condition and currently incurable is one of the most common skin diseases. About 80% of people who develop psoriasis have plaque psoriasis, which appears as patches of raised, reddish skiii covered by silvery-white scale. The Psoriasis Area and Severity Index (PASI) is the most widely Used tool to assess psoriasis disease severity in clinical trials, although it can be exceedingly cumbersome for use in daily clinical practice. It is proven to be extremely effective in assessing Psoriasis. When Using the PASI, psoriatic plaques are graded based on three criteria: redness, thickness, and scaliness. For the time being, the PASI-scoring are subjective since the assessments are done Visually by the dermatologist. The assessment will result in inter-individual variation between estimates due to different level of experiences and visual acuity. The aim of this project is to develop an automated scoring for scaliness of Psoriasis lesions program Using MATLAB. This project will be Using 2-D Psoriasis images obtained from General Hospital, Kuala Lumpur and medical database online. The MATLAB software will be Used to develop algorithms that are capable to read images of Psoriasis and grade the scaliness scores using the PASI-score texture analysis. The targeted system will include subsystem for acquiring the images, image processing, segmentation, texture analysis for scaliness score and severity based on PASI system

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

AN IMAGE BASED SYSTEM TO OBJECTIVEI,Y SCORE THE DEGREE OF REDNESS IN PSORIASIS LESIONS

Nowadays, many skin diseases exist, ranging from hannless such as benign tumors to highly cancerous ones such as malignant melanoma. The visual resemblance of skin lesions requires experienced dennatologists for diagnosis and treatment of skin diseases. One of the most common types of the skin diseases is psoriasis which is chronic inflammatory skin condition, characterized by localized, widespread welldemarcated red plaques often topped by silvery scales. The basic characteristics of psoriasis lesions namely redness, thickness, and scaliness provide a mean of assessing the severity of psoriasis. Dennatologists are using Psoriasis Area and Severity Index (P ASI) score, which takes into account signs such as redness, plaque thickness and scaling in order to assess psoriasis disease severity. The objective of this project is to generate the score of the redness and score of the area covered by psoriasis in order to build automated imaging system capable of classifYing the severity of the disease. This system would assist dennatologists to give the suitable treatment to the different levels of psoriasis severity based on the PASI score. The psoriasis lesion images will be analyzed to classifY the severity based on color, shape, size, and other features by using the Digital Image Processing Tools in MATLAB7 soflware. The entire infonnation obtained through the computer vision and image processing as well as MATLAB7 soflware is applied towards the development of this project. The project will be implemented in two stages .. The first stage (semester 1) involves literature review, research, data gathering, learning and training of the soflware or program and the second stage (semester 2) is analysis of core features, design, testing and analysis of results

Objective Assessment of Area and Erythema of Psoriasis Lesion Using Digital Imaging and Colourimetry

Psoriasis is a non-contagious skin disease which typically consists of red plaques covered by silvery-white scales. It affects about 3% of world population. During treatment, dermatologists monitor the extent of psoriasis continuously to ascertain treatment efficacy. Psoriasis Area and Severity Index (PAS!) is the current gold standard method used to assess the extent of psoriasis. In PAS!, there are four parameters to be scored i.e., the surface area affected, erythema (redness), thickness and scaliness of the plaques. Determining PAS! score is a tedious task and thus it is not used in daily clinical practice. In addition, the PAS! parameters are visually determined and may result in intra-observer and inter-observer variations, even by experienced dermatologists. Objective methods in assessing area and erythema of psoriasis lesion have been developed in this thesis. Psoriasis lesion can be recognized by its colour dissimilarity with normal skin. Colour dissimilarity is represented by colour difference in CIELAB colour space, a widely used colour space to measure colour dissimilarity. Each pixel in CIELAB colour space can be represented by its lightness (L'), hue (hob), and chroma (Cab). Colour difference between psoriasis lesion and normal skin is analyzed in hue-chroma plane of CIELAB colour space. Centroids of normal skin and lesion in hue-chroma space are obtained from selected samples. Euclidean distances between all pixels with these two centroids are then calculated. Each pixel is assigned to the class of the nearest centroid. The erythema of psoriasis lesion is affected by degree of severity and skin pigmentation. In order to assess the erythema objectively, patients are grouped according to their skin pigmentation level. The L* value of normal skin which represents skin pigmentation level is utilized to group the patient into the three skin types namely fair, brown and dark skin types. Light difference (t.L*), hue difference (t.hab), and chroma difference (t.C'ab) of CIELAB colour space between reference lesions and the surrounding normal skin are analyzed. It is found that the erythema score of a lesion can be determined by their hue difference (t.hab) value within a particular skin type group. Out of 30 body regions, the proposed method is able to give the same PAS! area score as reference for 28 body regions. The proposed method is able to determine PAS! erythema score of 82 lesions obtained from 22 patients objectively without being influenced by other characteristic of the lesion such as area, pattern, and boundary

Towards Novel Nonparametric Statistical Methods and Bioinformatics Tools for Clinical and Translational Sciences

As the field of functional genetics and genomics is beginning to mature, we become confronted with new challenges. The constant drop in price for sequencing and gene expression profiling as well as the increasing number of genetic and genomic variables that can be measured makes it feasible to address more complex questions. The success with rare diseases caused by single loci or genes has provided us with a proof-of-concept that new therapies can be developed based on functional genomics and genetics. Common diseases, however, typically involve genetic epistasis, genomic pathways, and proteomic pattern. Moreover, to better understand the underlying biologi-cal systems, we often need to integrate information from several of these sources. Thus, as the field of clinical research moves toward complex diseases, the demand for modern data base systems and advanced statistical methods increases. The traditional statistical methods implemented in most of the bioinformatics tools currently used in the novel field of genetics and functional genomics are based on the linear model and, thus, have shortcomings when applied to nonlinear biological systems. The previous work on partially ordered data (Wittkowski 1988; 1992), when combined with theoretical results (Hoeffding 1948) and computational strategies (Deuchler 1914) has opened a new field of nonparametric statistics. With grid technology, new tools are now feasible when screening for interactions between genetics (Wittkowski, Liu 2002) and functional genomics (Wittkowski, Lee 2004). Having more complex study designs and more specific methods available increases the demand for decision support when selecting appropriate bioinformatics tools. With the advent of rapid prototyping systems for Web based database application, we have recently begun to complement previous work on knowledge based systems with graphical Web-based tools for acquisition of DESIGN and MODEL knowledge.Biostatistics Bioinformatics NIH NCRR ROADMAP

AUTOMATED SCORING FOR SCALINESS OF PSORIASIS LESIONS USING EDGE DETECTION

Skin diseases affect 20-30% of the population at any one time, interfering with activities in 10%. Psoriasis, an Mamniatory skin condition and currently incurable is one of the most common skin diseases. About 80% of people who develop psoriasis have plaque psoriasis, which appears as patches of raised, reddish skiii covered by silvery-white scale. The Psoriasis Area and Severity Index (PASI) is the most widely Used tool to assess psoriasis disease severity in clinical trials, although it can be exceedingly cumbersome for use in daily clinical practice. It is proven to be extremely effective in assessing Psoriasis. When Using the PASI, psoriatic plaques are graded based on three criteria: redness, thickness, and scaliness. For the time being, the PASI-scoring are subjective since the assessments are done Visually by the dermatologist. The assessment will result in inter-individual variation between estimates due to different level of experiences and visual acuity. The aim of this project is to develop an automated scoring for scaliness of Psoriasis lesions program Using MATLAB. This project will be Using 2-D Psoriasis images obtained from General Hospital, Kuala Lumpur and medical database online. The MATLAB software will be Used to develop algorithms that are capable to read images of Psoriasis and grade the scaliness scores using the PASI-score texture analysis. The targeted system will include subsystem for acquiring the images, image processing, segmentation, texture analysis for scaliness score and severity based on PASI system

Development and evaluation of a microservice-based virtual assistant for chronic patients support

Los asistentes virtuales (también conocidos como chatbots) son programas que interactúan con los usuarios simulando una conversación humana a través de mensajes de texto o de voz. Los asistentes virtuales destinados al cuidado de la salud ofrecen servicios, herramientas, asesoramiento, ayuda, soporte y gestión de diferentes enfermedades. Los usuarios de este tipo de asistente virtual pueden ser, por ejemplo, pacientes, cuidadores y profesionales sanitarios, los cuales poseen diferentes necesidades y requerimientos. Los pacientes con enfermedades crónicas podrían beneficiarse de los asistentes virtuales que se encargan de realizar seguimientos de su condición, proporcionar información específica, fomentar la adherencia a la medicación, etc. Para realizar estas funciones, los asistentes virtuales necesitan una arquitectura de software adecuada. Esta tesis doctoral propone el diseño de una arquitectura específica para el desarrollo de asistentes virtuales destinados a proporcionar soporte a pacientes crónicos. Hoy en día, las personas interactúan entre sí diariamente utilizando plataformas de mensajería. Para alinear este tipo de interacción con la arquitectura del asistente virtual, proponemos el uso de plataformas de mensajería para la interacción asistente virtual-paciente, prestando especial atención a las cuestiones de seguridad y privacidad (es decir, el uso de plataformas de mensajería seguras con cifrado de extremo a extremo).Los asistentes virtuales pueden implementar sistemas conversacionales para que la interacción con los pacientes sea más natural. Los sistemas conversacionales en escenarios de atención médica complejos, como la gestión de enfermedades, deben ser capaces de poder comprender oraciones complejas utilizadas durante la interacción. La adaptación de nuevos métodos con el procesamiento de lenguaje natural (NLP, por su nombre en inglés, Natural Language Processing) puede aportar una mejora a la arquitectura del asistente virtual. Los word embeddings (incrustación de palabras) se han utilizado ampliamente en NLP como entrada en las redes neuronales. Tales word embeddings pueden ayudar a comprender el objetivo final y las palabras clave en una oración. Por ello, en esta tesis estudiamos el impacto de diferentes word embeddings entrenados con corpus generales y específicos utilizando el entendimiento del lenguaje natural conjunto (Joint NLU, por su nombre en inglés, Joint Natural Language Understanding) en el dominio de la medicación en español. Los datos para entrenar el modelo NLU conjunto se generan usando plantillas. Dicho modelo se utiliza para la detección de intenciones, así como para el slot filling (llenado de ranuras). En este estudio comparamos word2vec y fastText como word embeddings y ELMo y BERT como modelos de lenguaje. Para entrenar los embeddings utilizamos tres corpus diferentes: los datos de entrenamiento generados para este escenario, la Wikipedia en español como dominio general y la base de datos de medicamentos en español como datos especializados. El mejor resultado se obtuvo con el modelo ELMo entrenado con Wikipedia en español.Dotamos al asistente virtual de capacidades de gestión de medicamentos basadas en NLP. En consecuencia, se analiza el impacto del etiquetado de slots y la longitud de los datos de entrenamiento en modelos NLU conjuntos para escenarios de gestión de medicamentos utilizando asistentes virtuales en español. En este estudio definimos las intenciones (propósitos de las oraciones) para escenarios centrados en la administración de medicamentos y dos tipos de etiquetas de slots. Para entrenar el modelo, generamos cuatro conjuntos de datos, combinando oraciones largas o cortas con slots largos o cortos. Para el análisis comparativo, elegimos seis modelos NLU conjuntos (SlotRefine, stack-propagation framework, SF-ID network, capsule-NLU, slot-gated modeling y joint SLU-LM) de la literatura existente. Tras el análisis competitivo, se observa que el mejor resultado se obtuvo utilizando oraciones y slots cortos. Nuestros resultados sugirieron que los modelos NLU conjuntos entrenados con slots cortos produjeron mejores resultados que aquellos entrenados con slots largos para la tarea de slot filling.En definitiva, proponemos una arquitectura de microservicios genérica válida para cualquier tipo de gestión de enfermedades crónicas. El prototipo genérico ofrece un asistente virtual operativo para gestionar información básica y servir de base para futuras ampliaciones. Además, en esta tesis presentamos dos prototipos especializados con el objetivo de mostrar cómo esta nueva arquitectura permite cambiar, añadir o mejorar diferentes partes del asistente virtual de forma dinámica y flexible. El primer prototipo especializado tiene como objetivo ayudar en la gestión de la medicación del paciente. Este prototipo se encargará de recordar la ingesta de medicamentos a través de la creación de una comunidad de apoyo donde los pacientes, cuidadores y profesionales sanitarios interactúen con herramientas y servicios útiles ofrecidos por el asistente virtual. La implementación del segundo prototipo especializado está diseñada para una enfermedad crónica específica, la psoriasis. Este prototipo ofrece teleconsulta y almacenamiento de fotografías.Por último, esta tesis tiene como objetivo validar la eficacia del asistente virtual integrado en las plataformas de mensajería, destinado al cuidado de la salud. Por ello, esta tesis incluye la evaluación de los dos prototipos especializados. El primer estudio tiene como objetivo mejorar la adherencia a la medicación en pacientes con diabetes mellitus tipo 2 comórbida y trastorno depresivo. Para ello, se diseñó y posteriormente se realizó un estudio piloto de nueve meses. En el estudio analizamos la Tasa de Posesión de Medicamentos (MPR, por su nombre en inglés, Medication Possession Ratio), obtuvimos la puntuación del Cuestionario sobre la Salud del Paciente (PHQ-9, por su nombre en inglés, Patient Health Questionnaire) y medimos el nivel de hemoglobina glicosilada (HbA1c), en los pacientes antes y después del estudio. También realizamos entrevistas a todos los participantes. Un total de trece pacientes y cinco enfermeras utilizaron y evaluaron el asistente virtual propuesto. Los resultados mostraron que, en promedio, la adherencia a la medicación de los pacientes mejoró. El segundo estudio tiene como objetivo evaluar un año de uso entre el asistente virtual y pacientes con psoriasis y dermatólogos, y el impacto en su calidad de vida. Para ello se diseñó y realizó un estudio prospectivo de un año de duración con pacientes con psoriasis y dermatólogos. Para medir la mejora en la calidad de vida, en este estudio analizamos los cuestionarios de Calidad de Vida de los Pacientes con Psoriasis (PSOLIFE, por su nombre en inglés, Psoriasis Quality of Life) y el Índice de Calidad de Vida en Dermatología (DLQI, por su nombre en inglés, Dermatology Life Quality Index). Además, realizamos encuestas a todos los participantes y obtuvimos el número de consultas médicas realizadas a través del asistente virtual. Se incluyeron en el estudio un total de 34 participantes (30 pacientes diagnosticados con psoriasis moderada-grave y cuatro profesionales sanitarios). Los resultados mostraron que, en promedio, la calidad de vida mejoró.<br /

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors