Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology

doi:10.22608/apo.2018479

Cited by 94 publications

(71 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[20], ocular tumors [21], inflammatory diseases [22], radiation maculopathy [23], and multiple sclerosis [24]. Glaucoma is a neurodegenerative optic neuropathy characterized by loss of retinal ganglion cells (RGCs) and their axons, which may result in atrophy of the optic nerve, leading to progressive VF defects and blindness [25]. Early diagnosis and treatment of glaucoma are crucial to protect and sustain VF [25].…”

Section: Resultsmentioning

confidence: 99%

“…Glaucoma is a neurodegenerative optic neuropathy characterized by loss of retinal ganglion cells (RGCs) and their axons, which may result in atrophy of the optic nerve, leading to progressive VF defects and blindness [25]. Early diagnosis and treatment of glaucoma are crucial to protect and sustain VF [25]. The loss of RGCs is irreversible and can be detected with special devices before VF defects [26].…”

Section: Resultsmentioning

confidence: 99%

“…It has shown that retinal and optic nerve head vessel densities and blood flow indices were decreased in numerous studies in patients with glaucoma measured by OCTA [27]. OCT is a routine tool to aid in the detection of glaucoma and its progression [25]. OCT is unhelpful in advanced glaucoma and myopic tilt.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Optical coherence tomography angiography for glaucoma diagnosis and observation

2020

Ann Clin Anal Med

View full text Add to dashboard Cite

Glaucoma is a neurodegenerative optic neuropathy characterized by loss of retinal ganglion cells (RGCs) and RGCs axons, resulting in atrophy of the optic nerve. Optical coherence tomography angiography (OCTA) is the most recent modality in ophthalmic imaging field for diagnosis of different ophthalmic disorders, providing a high-resolution view of the vascular structures in the retina and optic nerve head. OCTA provides three-dimensional (3D) vascular information with the detection of motion contrast from the blood flow and an advanced approach to visualize retinal vasculature without the need of any dye injections that are non-invasive, fast and reproducible. In contrast to fluorescein angiography (FA) and indocyanine green angiography (ICGA), OCTA is a fast, safe, non-invasive and cost-effective diagnostic method. Another substantial superiority of the OCTA over FA and ICGA methods is that it provides high-resolution, depth-resolved blood flow data and segments the vascular layers in slabs of varying thickness in a few seconds. In addition, OCTA is a reliable and objective imaging technique with high reproducibility compared to the visual field (VF) testing. OCTA is also faster than VF testing and is less dependent on patient collaboration. Given the current population growth trends and hence the increase in glaucoma prevalence, the analysis of FA and ICGA images or VA testing is more likely to be time-consuming, costly, and prone to adverse effects. Therefore, these challenges can only be solved with the help of potentially promising diagnostic analyzes.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Optical coherence tomography angiography for glaucoma diagnosis and observation

2020

Ann Clin Anal Med

View full text Add to dashboard Cite

show abstract

“…The recent progress involves research on automated computer-aided diagnosis using Deep Learning approaches to detect and grade Diabetic Retinopathy in advance, from the digital fundus images. Balyen and Peto (2019) discuss the general influence of AI, ML and DL on the field of ophthalmology. Cheung et al (2019).…”

Section: Literature Surveymentioning

confidence: 99%

Diabetic Retinopathy Detection using Deep Learning Techniques

Thiagarajan¹,

Adikesavan²,

Balachandran³

et al. 2020

Journal of Computer Science

View full text Add to dashboard Cite

Diabetic Retinopathy is a type of eye condition induced by diabetes, which damages the blood vessels in the retinal region and the area covered with lesions of varying magnitude determines the severity of the disease. It is one of the most leading causes of blindness amongst the employed community. A variety of factors are observed to play a role in a person to get this disease. Stress and prolonged diabetes are two of the most critical factors to top the list. This disease, if not predicted early, can lead to a permanent impairment of vision. If predicted in advance, the rate of impairment can be brought down or averted. However, it is not easy to detect the presence of this disease, given the time-consuming and tedious process of diagnosis. Presently, digital color photographs are evaluated manually by trained clinicians to observe the presence of lesions caused due to vascular abnormalities, which is the major effect of Diabetic Retinopathy. This method, although it is pretty accurate, proves to be costly. The delay brings out the need to automate the diagnosing, which will, in turn, have a significant positive impact on the health sector. In recent times, the adoption of AI in disease diagnosis has ensured promising and reliable results and this serves as the motivation for this journal. The paper employs Deep learning methodologies for automatic detection of Diabetic Retinopathy, resulting in a maximum accuracy of 80%, as compared to traditional Machine learning approaches giving only a maximum accuracy of 48% on the same IRDiR Disease Grading Dataset (413 images with 5 levels of DR-Training set; 103 images with 5 levels of DR-Test set). The data set contains digital fundus images of different levels of Diabetic Retinopathy in discrete frequency distributions.

show abstract

“…Modelos de inteligência artificial são utilizados em diversas áreas da medicina. Existem diversas publicações com aplicações na área de detecção de câncer de mama (McKinney et al, 2020), radiologia e medicina nuclear (Hosny et al, 2018;Syed & Zoga, 2018;Nensa, Demircioglu & Rischpler, 2019), alvos terapêuticos (Theofilatos et al, 2014), cardiologia (Krittanawong et al, 2017;Johnson et al, 2018;Dey et al, 2020), medicina reprodutiva (WANG et al, 2019), medicina personalizada (Schork, 2019), medicina de emergência (Stewart, Sprivulis & Dwivedi, 2018), nefrologia (Niel & Bastard, 2019), oftalmologia (Kapoor, Walter & Al-Aswad, 2018;Ting et al, 2019;Balyen, Peto, 2019), psiquiatria (Meyer-Lindenberg, 2018), dermatologia (Aggarwal, 2019), urologia (Suarez-Ibarrola et al, 2019) e oncologia (Shimizu & Nakayama, 2020).…”

Section: Introductionunclassified

Uso da inteligência artificial para predição de acidentes de trabalho com materiais biológicos em profissionais da saúde

Groto

Perlin

Andrade

et al. 2021

RSD

View full text Add to dashboard Cite

Este estudo buscou desenvolver um software que calcula a chance de o profissional de saúde ter zero, um, dois, três ou quatro ou mais acidentes com riscos biológicos. Para tal foram utilizados dados de 111 questionários de trabalhadores da saúde da atenção primária e pronto atendimento. O programa atingiu 95% de acurácia no conjunto de treinamento (n=88) e 74% no conjunto de teste (n=23). As associações estatisticamente significantes, que contaram também com dados de 1.094 Comunicações de Acidente de Trabalho, foram maior abandono do acompanhamento por médicos após acidente com materiais biológicos na comparação com outros profissionais (p=0.02), técnicos em enfermagem e maior prevalência de acidentes com materiais biológicos que outros profissionais (p<0.001), trabalhadores de pronto atendimento apresentam mais acidentes com materiais biológicos que profissionais da atenção primária (p<0.001) e aumento do abandono após acidente com materiais biológicos no triênio 2016-2018 na comparação com 2007-2009 (p<0.001).

show abstract

Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology

Cited by 94 publications

References 53 publications

Optical coherence tomography angiography for glaucoma diagnosis and observation

Optical coherence tomography angiography for glaucoma diagnosis and observation

Diabetic Retinopathy Detection using Deep Learning Techniques

Uso da inteligência artificial para predição de acidentes de trabalho com materiais biológicos em profissionais da saúde

Contact Info

Product

Resources

About