Cotton Wool Spots in Eye Fundus Scope

Diabetes mellitus é uma doença com um impacto significativo na saúde pública. Trata-se de uma alteração do metabolismo de hidratos de carbono, gorduras e proteínas que são resultado de uma deficiência ou ausência total de secreção/resistência à insulina por parte das células beta do pâncreas. Existem 3 tipos de diabetes, o denominado tipo 1 em que o doente é dependente de insulina, o tipo 2 em que o doente é dependente de insulina e a diabetes gestacional que aparece durante a fase de gravidez. A retinopatia diabética é uma complicação que pode resultar em cegueira. Se for detetada numa fase inicial, pode ser tratada por cirurgia a laser. No entanto, é dificil deteta-la numa fase inicial, uma vez que progride sem sintomas até ocorrer perda de visão de forma irreversível. Assim, se podermos detetar / encontrar exudados algodonosos no fundo de olho utilizando reconhecimento de imagem, anotação automática, sistemas de apoio à decisão de avaliação do risco, conjugados com uma aplicação móvel que permita a aquisição de imagens de fundo de olho, poderemos detetar mais cedo e tratar, evitando o risco cegueira do paciente. Este projeto tem como objetivo desenvolver uma aplicação smartphone baseada em algoritmos de baixo custo, que podem ser altamente eficientes nas imagens de baixa qualidade provenientes da câmara de um smartphone, que pode ser usada como um sistema de apoio à decisão. Este sistema também pode ser extendido a outras doenças oculares, como uma ferramenta útil para o rastreio de saúde ocular nos países em desenvolvimento, reforçar a proximidade dos programas de rastreio para a população. Os principais objetivos são desenvolver sistema fiável de apoio à decisão, considerando exudados algodonosos, juntamente com pontos vermelhos, em vez do sistema actualmente em uso em Portugal, que considera apenas os pontos vermelhos. O número casos Retinopatia Diabética em todo o mundo justifica o desenvolvimento de um sistema de suporte à decisão automatizado para triagem rápida e de baixo custo da Retinopatia Diabética.Diabetes mellitus é uma doença com um impacto significativo na saúde pública. Trata-se de uma alteração do metabolismo de hidratos de carbono, gorduras e proteínas que são resultado de uma deficiência ou ausência total de secreção/resistência à insulina por parte das células beta do pâncreas. Existem 3 tipos de diabetes, o denominado tipo 1 em que o doente é dependente de insulina, o tipo 2 em que o doente é dependente de insulina e a diabetes gestacional que aparece durante a fase de gravidez. A retinopatia diabética é uma complicação que pode resultar em cegueira. Se for detetada numa fase inicial, pode ser tratada por cirurgia a laser. No entanto, é dificil deteta-la numa fase inicial, uma vez que progride sem sintomas até ocorrer perda de visão de forma irreversível. Assim, se podermos detetar / encontrar exudados algodonosos no fundo de olho utilizando reconhecimento de imagem, anotação automática, sistemas de apoio à decisão de avaliação do risco, conjugados com uma aplicação móvel que permita a aquisição de imagens de fundo de olho, poderemos detetar mais cedo e tratar, evitando o risco cegueira do paciente. Este projeto tem como objetivo desenvolver uma aplicação smartphone baseada em algoritmos de baixo custo, que podem ser altamente eficientes nas imagens de baixa qualidade provenientes da câmara de um smartphone, que pode ser usada como um sistema de apoio à decisão. Este sistema também pode ser extendido a outras doenças oculares, como uma ferramenta útil para o rastreio de saúde ocular nos países em desenvolvimento, reforçar a proximidade dos programas de rastreio para a população. Os principais objetivos são desenvolver sistema fiável de apoio à decisão, considerando exudados algodonosos, juntamente com pontos vermelhos, em vez do sistema actualmente em uso em Portugal, que considera apenas os pontos vermelhos. O número casos Retinopatia Diabética em todo o mundo justifica o desenvolvimento de um sistema de suporte à decisão automatizado para triagem rápida e de baixo custo da Retinopatia Diabética.Diabetes mellitus is a disease with significant impact in public health. It is a complex disorder of carbohydrate, fat and protein metabolism that is a result of a deficiency, or complete lack of insulin secretion by the Beta cells of pancreas, or resistance to Insulin. There are 3 types of diabetes, namely type 1 where the patient is insulin-dependent, type 2 where the patient is non insulin-dependent and gestational diabetes that appears during the pregnancy phase.Retinopathy is a diabetes complication that can result in blindness. If detected in an early stage, it can be treated by laser surgery. However its early detection is frequently missed, since it progresses without symptoms until irreversible vision loss occurs.So if we can detect/find cotton wool spots in eye fundus scope by using image recognition, automatic annotation, decision-support systems for risk assessment, conjugate with a mobile app acquiring eye fundus images, we might detect early and treat avoiding patient blindness risk.This project aims to develop a smartphone-based on low computational-cost algorithms, which can be highly efficient in the lower quality images of the smartphone camera, that can be used as a decision-support system. This system may also be extended to other eye diseases, as an useful tool for eye health screening in developing countries and enhance the proximity of screening programs to the population.The main expected contribution is to develop a good decision-support system, considering cotton wool spots, together with red dots, instead of the actual system in use in Portugal which only considers red dots. The number of Diabetic Retinopathy cases worldwide justifies the development of an automated decision-support system for quick and cost effective screening of Diabetic Retinopathy

Psychiatric Case Record

Bipolar Disorder-Mania: Patient was apparently normal one-month back, Then all of a sudden he developed sleep disturbances –mainly difficult in initiation of sleep. He also started abusing his family members for unwanted things. Subsequently, he started talking excessively and irritable. Sometimes he sings film songs and dances. He used to say that God Supreme exists in himself and so he has all the powers of Almighty. With that superior power he says that he can solve all the problems in this world. He also says that he has invented herbs to keep people young. For the past one week, he talks excessively without having an hour of sleep & wanders here and there & found excessively smoking. He becomes excessively spiritual and goes to near by villages for offering prayers to God. He takes only a little food everyday and he is very much keen in personal cleanliness. Paranoid Schizophrenia: She was apparently normal 8 months back, then she developed sleep disturbances in the form of difficult in falling asleep. She was found talking & smiling to herself at night & day with mirror gazing. She started saying that her neighbour & relatives are planning to kill herself by poisoning. In this context she had frequent quarrels with them and she refused to take food prepared by her mother in law. She left the home at night without informing any one and started wandering in the road side near her home. She was complaining that she hears voices as if her neighbour & relatives were talking about her among themselves She was not doing house hold activities for past 6 months and she was not taking care of her child. Her personal hygiene was very much deteriorated slowly as she used to take bath & brush, only if she was asked to do so. She started abusing & assaulting the strangers and family members. Generalised Anxiety Disorder: Six months back he was apparently normal. He is working as a system analyst in a private bank . He had once, made a mistake in his bank work for which he was given charges by his employer, followed this event he becomes very tense and afraid whenever his boss called him. He is very cautious that he should not commit any mistakes. Even though he is not doing so, he fears that he may commit some mistake in his work. At that moment he develops palpitation, giddiness, breathlessness, excessive sweating over palms and soles. Slowly these symptoms present through out the day even when he was not in his office, and he could not control his fearfulness. For the past 6 months he didn’t sleep well. His sleep is disturbed by bad dreams. Recurrent Depressive Disorder: Patient was apparently alright 2 months back. Then she developed sleep disturbances particularly early morning awakening, she use to wake up by 3.00 am and use to brood about herself and started crying. She was not doing her domestic work as before, as she felt excess tiredness and use to take frequent rests. She developed poor communication. She had lost her interest in pleasurable activities and was not interested in watching TV, and attending family gatherings. She stayed aloof most of the time & calm, quiet and withdrawn. She was expressing her helplessness and hopelessness about the future. She started to have decline in maintaining self care. 15 days back, she frequently expressed suicidal ideas and she had attempted suicide by hanging herself and was rescued by neighbours. 5 days back, she started talking in an irrelevant manner. She was smiling to self. She was assaulting her family members. She was suspicious that her neighbour had done black magic on her and also saying that people are talking about her. She reported hearing the voice of her neighbour scolding and threatening her. Organic Brain Syndrome – Dementia: Ten months back he was apparently alright. Then his relatives noticed himself frequently misplaces things inside his home. Then he started behaving aggressively. He was beating his wife without reason. He was roaming here and there, running out of home and wandering aimlessly. He was not able to come back home when he goes out. He was brought back to home by his relatives. Slowly he developed fearfulness and tremulousness while he was staying alone. He also started saying that his family members & neighbours were talking about himself, in this context he would make frequent quarrels with them. He also started hearing voices of known male voices abusing himself in third person. He sleeps for few hour only. He is passing urine and motion inside the house. He is asking about his brother and mother-in-law who were expired long back. He behaves abnormally such as pouring water in the plate while eating. And his relatives found the symptoms were worsened by evening. All these symptoms started insidiously, increased in severity through time and attained the present state. No history of loss of appetite / crying spells / suicidal tendencies / convulsions / fever / head injury

Diabetic Reinopathy Classification using Deep Learning

With diabetes growing at an alarming rate, changes in the retina of diabetic patients causes a condition called diabetic retinopathy which eventually leads to blindness. Early detection of diabetic retinopathy is the best way to provide good timely treatment and thus prevent blindness. Many developed countries have put forward well-structured screening programs which screens every person diagnosed with diabetes at regular intervals. However, the cost of running these programs is increasing with ever increasing disease burden. These screening programs require well trained opticians or ophthalmologist which are expensive especially in developing countries. A global shortage of health care professionals is putting a pressing need to develop fast and efficient screening methods. Using artificial intelligent screening tools will help process and generate a plan for the patients thus skipping the health care provider needed to just classify the disease and will lower the burden on health care professional’s shortage significantly. A plethora of research exists to classify severity of diabetic retinopathy using traditional and end to end methods. In this thesis, we first trained and compared the performance of lightweight architecture MobileNetV2 with other classifiers like DenseNet121 and VGG16 using the Retinal fundus APTOS 2019 Kaggle dataset. We experimented with different image reprocessing techniques and employed various hyperparameter tuning techniques, and found the lightweight architecture MobileNetV2 to give better results in terms of AUC score which defines the ability of the classifier to separate between the classes. We then trained MobileNetV2 using handpicked custom dataset which was an amalgamation of 3 different publicly available datasets viz. the EyePacs Kaggle dataset, the APTOS 2019 Blindness detection dataset and the Messidor2 dataset. We enhanced the retinal features using bio-inspired retinal filters and tuned the hyper-parameters to achieve an accuracy of 91.68% and AUC score of 0.9 when tested on unseen data. The macro precision, recall, and f1-scores are 77.6%, 83.1%, and 80.1% respectively. Our results demonstrate that our computational efficient light weight model achieves promising results and can be deployed as a mobile application for clinical testing

Mobile-Based risk assessment of diabetic retinopathy by image processing

Abry Christian, Abry-Deffayet Dominique. Pour une évaluation de la spécificité lexicale d 'une région : la Savoie. In: Le Monde alpin et rhodanien. Revue régionale d'ethnologie, n°1/1981. Les régions de la France. Colloque de la Société d'Ethnologie Française. Grenoble 7-8 décembre 1978. pp. 111-126

Eye Disease Detection Using Computer Vision

Glaucoma and Diabetic Retinopathy(DR) are among the leading causes of blindness. Belated handling of Cataract can impact the vision causing blindness. Often the scarcity of experts can lead to delayed diagnosis, resulting in untreatable conditions. But detection of these diseases at earliest stage and treatment can aid patient in avoiding vision loss. An automatic disease detection system can help this by providing accurate and early diagnosis. In proposed system, diagnosis will be obtained using image processing and mining techniques on fundus image. Feature extraction using DCT. K-NN classification algorithm will be used to classify the image in a specific class (Normal,Glaucoma,DR or Cataract)

Development of a virtual reality ophthalmoscope prototype

El examen visual es un procedimiento importante que proporciona información acerca de la condición del fondo de ojo, permitiendo la observación e identificación de anomalías, como ceguera, diabetes, hipertensión, sangrados resultado de traumas, entre otros. Un apropiado examen permite identificar condiciones que pueden comprometer la visión, sin embargo, éste es desafiante porque requiere de una práctica extensiva para desarrollar las habilidades para una adecuada interpretación que permiten la identificación exitosa de anomalías en el fondo de ojo con un oftalmoscopio. Para ayudar a los practicantes a desarrollar sus habilidades para la examinación ocular, los dispositivos de simulación médica están ofreciendo oportunidades de entrenamiento para explorar numerosos casos del ojo en escenarios simulados, controlados y monitoreados. Sin embargo, los avances en la simulación del ojo han llevado a costosos simuladores con acceso limitado ya que la práctica se mantiene con interacciones para un aprendiz y en algunos casos, ofreciendo al entrenador la visión para la interacción del practicante. Gracias a los costos asociados a la simulación médica, hay varias alternativas reportadas en la revisión de la literatura, presentando aproximaciones efectividad-costo y nivel de consumo para maximizar la efectividad del entrenamiento para el examen de ojo. En este trabajo se presenta el desarrollo de una aplicación con realidad aumentada inmersiva y no-inmersiva, para dispositivos móviles Android con interacciones a través de un controlador impreso en 3D con componentes electrónicos embebidos que imitan a un oftalmoscopio real. La aplicación presenta a los usuarios un paciente virtual visitando al doctor para un examen ocular, y requiere que el aprendiz ejecute el examen de fondo de ojo haciendo diagnosticando sus hallazgos. La versión inmersiva de la aplicación requiere del uso de un casco de realidad virtual, además del prototipo 3D de oftalmoscopio, mientras que la no inmersiva, requiere únicamente del marcador dentro del campo de visión del dispositivo móvil.The eye examination is an important procedure that provides information about the condition of the eye by observing its fundus, thus allowing the observation and identification of abnormalities, such as blindness, diabetes, hypertension, and bleeding resulting from traumas among others. A proper eye fundus examination allows identifying conditions that may compromise the sight; however, the eye examination is challenging because it requires extensive practice to develop adequate interpretation skills that allows successfully identifying abnormalities at the back of the eye seen through an ophthalmoscope. To assist trainees in developing the eye examination skills, medical simulation devices are providing training opportunities to explore numerous eye cases in simulated, controlled, and monitored scenarios. However, advances in eye simulation have led to expensive simulators with limited access as practice remain conducted on a one trainee basis in some cases offering the instructor a view of the trainee interactions. Because of the costs associated with medical simulation, there various alternatives reported in the literature review presenting cost-effective and consumerlevel approaches to maximize the effectiveness of the eye examination training. In this work, we present the development an immersive and non-immersive augmented reality application for Android mobile devices with interactions through a 3D printed controller with embedded electronic components that mimics a real ophthalmoscope. The application presents users with a virtual patient visiting the doctor for an eye examination, and requires the trainees to perform the eye fundus examination and diagnose their findings. The immersive version of the application requires the trainees to wear a mobile VR headset and hold the 3D printed ophthalmoscope, while the non-immersive version requires them to hold the marker within the field of view of the mobile device.Pregrad

RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation

Retinal vessel segmentation is generally grounded in image-based datasets collected with bench-top devices. The static images naturally lose the dynamic characteristics of retina fluctuation, resulting in diminished dataset richness, and the usage of bench-top devices further restricts dataset scalability due to its limited accessibility. Considering these limitations, we introduce the first video-based retinal dataset by employing handheld devices for data acquisition. The dataset comprises 635 smartphone-based fundus videos collected from four different clinics, involving 415 patients from 50 to 75 years old. It delivers comprehensive and precise annotations of retinal structures in both spatial and temporal dimensions, aiming to advance the landscape of vasculature segmentation. Specifically, the dataset provides three levels of spatial annotations: binary vessel masks for overall retinal structure delineation, general vein-artery masks for distinguishing the vein and artery, and fine-grained vein-artery masks for further characterizing the granularities of each artery and vein. In addition, the dataset offers temporal annotations that capture the vessel pulsation characteristics, assisting in detecting ocular diseases that require fine-grained recognition of hemodynamic fluctuation. In application, our dataset exhibits a significant domain shift with respect to data captured by bench-top devices, thus posing great challenges to existing methods. In the experiments, we provide evaluation metrics and benchmark results on our dataset, reflecting both the potential and challenges it offers for vessel segmentation tasks. We hope this challenging dataset would significantly contribute to the development of eye disease diagnosis and early prevention