A Comparison of Deep Learning Techniques for Glaucoma Diagnosis on Retinal Fundus Images

Glaucoma is one of the serious disorders which cause permanent vision loss if it left undetected. The primary cause of the disease is elevated intraocular pressure, impacting the optic nerve head (ONH) that originates from the optic disc. The variation in optic disc to optic cup ratio helps in early detection of the disease. Manual calculation of Cup to Disc Ratio (CDR) consumes more time and the prediction is also not accurate. Utilizing deep learning for the automatic detection of glaucoma facilitates precise and early identification, significantly enhancing the accuracy of glaucoma detection. The deep learning technique initiates the process by initially pre-processing the image to achieve data augmentation, followed by the segmentation of the optic disc and optic cup from the retinal fundus image. From the segmented Optic Disc (OD)and Optic Cup (OC) feature are selected and CDR calculated. Based on the CDR value the Glaucoma classification is performed. Various deep learning techniques like CNN, transfer learning, algorithm was proposed in early detection of glaucoma. From the comparative analysis glaucoma diagnosis, the proposed deep learning artifact Convolutional Neural Network outperform in early diagnosis of glaucoma providing accuracy of 99.3 8%

Structural measurements and vessel density of spectral-domain optic coherence tomography in early, moderate, and severe primary angle-closure glaucoma

AIM: To compare the macular ganglion cell-inner plexiform layer (GCIPL) thickness, retinal nerve fiber layer (RNFL) thickness, optic nerve head (ONH) parameters, and retinal vessel density (VD) measured by spectral-domain optical coherence tomography (SD-OCT) and analyze the correlations between them in the early, moderate, severe primary angle-closure glaucoma (PACG) and normal eyes. METHODS: Totally 70 PACG eyes and 20 normal eyes were recruited for this retrospective analysis. PACG eyes were further separated into early, moderate, or severe PACG eyes using the Enhanced Glaucoma Staging System (GSS2). The GCIPL thickness, RNFL thickness, ONH parameters, and retinal VD were measured by SD-OCT, differences among the groups and correlations within the same group were calculated. RESULTS: The inferior and superotemporal sectors of the GCIPL thickness, rim area of ONH, average and inferior sector of the retinal VD were significantly reduced (all P<0.05) in the early PACG eyes compared to the normal and the optic disc area, cup to disc ratio (C/D), and cup volume were significantly higher (all P<0.05); but the RNFL was not significant changes in early and moderate PACG. In severe group, the GCIPL and RNFL thickness were obvious thinning with retinal VD were decreasing as well as C/D and cup volume increasing than other three groups (all P<0.01). In the early PACG subgroup, there were significant positive correlations between retinal VD and GCIPL thickness (except superonasal and inferonasal sectors, r=0.573 to 0.641, all P<0.05), superior sectors of RNFL thickness (r=0.055, P=0.049). More obvious significant positive correlations were existed in moderate PACG eyes between retinal VD and superior sectors of RNFL thickness (r=0.650, P=0.022), and temporal sectors of RNFL thickness (r=0.740, P=0.006). In the severe PACG eyes, neither GCIPL nor RNFL thickness was associated with retinal VD. CONCLUSION: The ONH damage and retinal VD loss appears earlier than RNFL thickness loss in PACG eyes. As the PACG disease progressed from the early to the moderate stage, the correlations between the retinal VD and RNFL thickness increases

Predicting the Clinical Management of Skin Lesions Using Deep Learning

Automated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine learning approaches that suggest management decisions rely on predicting the underlying skin condition to infer a management decision without considering the variability of management decisions that may exist within a single condition. We present the first work to explore image-based prediction of clinical management decisions directly without explicitly predicting the diagnosis. In particular, we use clinical and dermoscopic images of skin lesions along with patient metadata from the Interactive Atlas of Dermoscopy dataset (1011 cases; 20 disease labels; 3 management decisions) and demonstrate that predicting management labels directly is more accurate than predicting the diagnosis and then inferring the management decision (13.73±3.93% and 6.59±2.86% improvement in overall accuracy and AUROC respectively), statistically significant at p&lt;0.001. Directly predicting management decisions also considerably reduces the over-excision rate as compared to management decisions inferred from diagnosis predictions (24.56% fewer cases wrongly predicted to be excised). Furthermore, we show that training a model to also simultaneously predict the seven-point criteria and the diagnosis of skin lesions yields an even higher accuracy (improvements of 4.68±1.89% and 2.24±2.04% in overall accuracy and AUROC respectively) of management predictions. Finally, we demonstrate our model’s generalizability by evaluating on the publicly available MClass-D dataset and show that our model agrees with the clinical management recommendations of 157 dermatologists as much as they agree amongst each other

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease’s development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers

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