6 research outputs found
Una revisión sistemática de métodos de aprendizaje profundo aplicados a imágenes oculares
Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology has not been the exception. In particular, deep learning methods have been applied successfully to the detection of clinical signs and the classification of ocular diseases. This represents a great potential to increase the number of people correctly diagnosed. In ophthalmology, deep learning methods have primarily been applied to eye fundus images and optical coherence tomography. On the one hand, these methods have achieved an outstanding performance in the detection of ocular diseases such as: diabetic retinopathy, glaucoma, diabetic macular degeneration and age-related macular degeneration. On the other hand, several worldwide challenges have shared big eye imaging datasets with segmentation of part of the eyes, clinical signs and the ocular diagnostic performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivering of interpretable clinically information. This review provides an overview of the state-of-the-art deep learning methods used in ophthalmic images, databases and potential challenges for ocular diagnosisLa inteligencia artificial está teniendo un importante impacto en diversas áreas de la medicina y a la oftalmología no ha sido la excepción. En particular, los métodos de aprendizaje profundo han sido aplicados con éxito en la detección de signos clínicos y la clasificación de enfermedades oculares. Esto representa un potencial impacto en el incremento de pacientes correctamente y oportunamente diagnosticados. En oftalmología, los métodos de aprendizaje profundo se han aplicado principalmente a imágenes de fondo de ojo y tomografía de coherencia óptica. Por un lado, estos métodos han logrado un rendimiento sobresaliente en la detección de enfermedades oculares tales como: retinopatía diabética, glaucoma, degeneración macular diabética y degeneración macular relacionada con la edad. Por otro lado, varios desafíos mundiales han compartido grandes conjuntos de datos con segmentación de parte de los ojos, signos clínicos y el diagnóstico ocular realizado por expertos. Adicionalmente, estos métodos están rompiendo el estigma de los modelos de caja negra, con la entrega de información clínica interpretable. Esta revisión proporciona una visión general de los métodos de aprendizaje profundo de última generación utilizados en imágenes oftálmicas, bases de datos y posibles desafíos para los diagnósticos oculare
Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation
International audienc
Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review.
Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is non-invasive and low-cost; however, the image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is: "Can artificial intelligence mimic glaucoma assessments made by experts?". Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy? We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011-2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modelling based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research
Aspectos do rastreamento do glaucoma auxiliados por técnicas automatizadas em imagens com menor qualidade do disco óptico
O glaucoma é uma neuropatia óptica cuja progressão pode levar a cegueira. Representa
a principal causa de perda visual de caráter irreversível em todo o mundo para homens
e mulheres. A detecção precoce através de programas de rastreamento feita por
especialistas é baseada nas características do nervo óptico, em biomarcadores
oftalmológicos (destacando-se a pressão ocular) e exames subsidiários, com destaque
ao campo visual e OCT. Após o reconhecimento dos casos é feito o tratamento com
finalidade de estacionar a progressão da doença e melhorar a qualidade de vida dos
pacientes. Contudo, estes programas têm limitações, principalmente em locais mais
distantes dos grandes centros de tratamento especializado, insuficiência de
equipamentos básicos e pessoal especializado para oferecer o rastreamento a toda a
população, faltam meios para locomoção a estes centros, desinformação e
desconhecimento da doença, além de características de progressão assintomática da
doença.
Esta tese aborda soluções inovadoras que podem contribuir para a automação do
rastreamento do glaucoma utilizando aparelhos portáteis e mais baratos, considerando
as necessidades reais dos clínicos durante o rastreamento.
Para isso foram realizadas revisões sistemáticas sobre os métodos e equipamentos para
apoio à triagem automática do glaucoma e os métodos de aprendizado profundo para
a segmentação e classificação aplicáveis. Também foi feito um levantamento de
questões médicas relativas à triagem do glaucoma e associá-las ao campo da inteligência
artificial, para dar mais sentido as metodologias automatizadas. Além disso, foi criado
um banco de dados privado, com vídeos e imagens de retina adquiridos por um
smartphone acoplado a lente de baixo custo para o rastreamento do glaucoma e
avaliado com métodos do estado da arte. Foram avaliados e analisados métodos de
detecção automática de glaucoma utilizando métodos de aprendizado profundo de
segmentação do disco e do copo óptico em banco de dados públicos de imagens de
retina. Finalmente, foram avaliadas técnicas de mosaico e de detecção da cabeça do
nervo óptico em imagens de baixa qualidade obtidas para pré-processamento de
imagens adquiridas por smartphones acoplados a lente de baixo custo.Glaucoma is an optic neuropathy whose progression can lead to blindness. It represents
the leading cause of irreversible visual loss worldwide for men and women. Early
detection through screening programs carried out by specialists is based on the
characteristics of the optic papilla, ophthalmic biomarkers (especially eye pressure), and
subsidiary exams, emphasizing the visual field and optical coherence tomography (OCT).
After recognizing the cases, the treatment is carried out to stop the progression of the
disease and improve the quality of patients’ life. However, these screening programs
have limitations, particularly in places further away from the sizeable, specialized
treatment centers, due to the lack of essential equipment and technical personnel to
offer screening to the entire population, due to the lack of means of transport to these
centers, due to lack of information and lack of knowledge about the disease, considering
the characteristics of asymptomatic progression of the disease.
This thesis aims to develop innovative approaches to contribute to the automation of
glaucoma screening using portable and cheaper devices, considering the real needs of
clinicians during screening.
For this, systematic reviews were carried out on the methods and equipment to support
automatic glaucoma screening, and the applicable deep learning methods for
segmentation and classification. A survey of medical issues related to glaucoma
screening was carried out and associated with the field of artificial intelligence to make
automated methodologies more effective. In addition, a private dataset was created,
with videos and retina images acquired using a low-cost lens-coupled cell phone, for
glaucoma screening and evaluated with state-of-the-art methods. Methods of
automatic detection of glaucoma using deep learning methods of segmentation of the
disc and optic cup were evaluated and analyzed in a public database of retinal images.
In the case of deep learning classification methods, these were evaluated in public
databases of retina images and in a private database with low-cost images. Finally,
mosaic and object detection techniques were evaluated in low-quality images obtained
for pre-processing images acquired by cell phones coupled with low-cost lenses