Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis

Carrera-Escalé, Laura; Benali, Anass; Rathert, Ann-Christin; Martín-Pinardel, Ruben; Bernal-Morales, Carolina; Alé-Chilet, Anibal; Barraso, Marina; Marin-Martínez, S.; Feu-Basilio, Silvia; Rosinés-Fonoll, Josep; Hernandez, Teresa; Vilá, Irene; Castro-Domínguez, Rafael; Oliva, Cristian; Vinagre, Irene; Ortega, Emilio; Giménez, Marga; Vellido, Alfredo; Romero, Enrique; Zarranz‐Ventura, Javier

doi:10.1016/j.xops.2022.100259

Cited by 11 publications

(3 citation statements)

References 47 publications

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“…The final prediction is made by taking the mode of all the individual tree predictions. The random forest algorithm has been shown to be effective at classifying OCT images with high accuracy and low false positive rates [ 11 , 12 , 13 ]. The consecutive steps can be written as follows: (i) begin by randomly selecting a subset of features from the OCT dataset; (ii) create multiple decision trees using the selected features; (iii) for each decision tree, randomly select a subset of training examples from the OCT dataset; (iv) train each decision tree using the selected training examples and features; (V) for each new example in the OCT dataset, make a prediction by having each decision tree make a prediction, and then take the majority vote of all predictions made by all decision trees; and (Vi) the final prediction is then assigned to one of the four classes (CNV, DME, drusen, or normal).…”

Section: Enhanced Optical Coherence Tomography (Eoct) Modelmentioning

confidence: 99%

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images

Hassan

Elmougy

Ibraheem

et al. 2023

Sensors

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Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study’s training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew’s correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively.

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Section: Enhanced Optical Coherence Tomography (Eoct) Modelmentioning

confidence: 99%

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images

Hassan

Elmougy

Ibraheem

et al. 2023

Sensors

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“…Sandhu et al demonstrated that combining parameters extracted from both OCTA and OCT images improves the discriminatory power of random forest (RF) classification for the automated diagnosis of NPDR. 10 Carrera-Escalé et al demonstrated that radiomics extracted from OCT and OCTA images can be used as predictors in logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and RF models to identify patients with DM, DR, and referable DR. 11 However, there has been limited research into the decision process of the models. Interpreting ML models is challenging given their complexity.…”

Section: Introductionmentioning

confidence: 99%

Ocular microvascular complications in diabetic retinopathy: insights from machine learning

Ahmed,

Shah,

Zhen

et al. 2024

BMJ Open Diab Res Care

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IntroductionDiabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults, primarily driven by ocular microvascular complications from chronic hyperglycemia. Comprehending the complex relationship between microvascular changes in the eye and disease progression poses challenges, traditional methods assuming linear or logistical relationships may not adequately capture the intricate interactions between these changes and disease advances. Hence, the aim of this study was to evaluate the microvascular involvement of diabetes mellitus (DM) and non-proliferative DR with the implementation of non-parametric machine learning methods.Research design and methodsWe conducted a retrospective cohort study that included optical coherence tomography angiography (OCTA) images collected from a healthy group (196 eyes), a DM no DR group (120 eyes), a mild DR group (71 eyes), and a moderate DR group (66 eyes). We implemented a non-parametric machine learning method for four classification tasks that used parameters extracted from the OCTA images as predictors: DM no DR versus healthy, mild DR versus DM no DR, moderate DR versus mild DR, and any DR versus no DR. SHapley Additive exPlanations values were used to determine the importance of these parameters in the classification.ResultsWe found large choriocapillaris flow deficits were the most important for healthy versus DM no DR, and became less important in eyes with mild or moderate DR. The superficial microvasculature was important for the healthy versus DM no DR and mild DR versus moderate DR tasks, but not for the DM no DR versus mild DR task—the stage when deep microvasculature plays an important role. Foveal avascular zone metric was in general less affected, but its involvement increased with worsening DR.ConclusionsThe findings from this study provide valuable insights into the microvascular involvement of DM and DR, facilitating the development of early detection methods and intervention strategies.

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“…With the accumulation of medical images and the development of artificial intelligence, deep learning (DL) algorithms have achieved unprecedented performance in the automatic diagnosis and lesion localization of various ophthalmic diseases, including eyelid tumors [ 7 , 8 ], keratitis [ 9 ], cataract [ 10 , 11 ], glaucoma [ 12 , 13 ], and diabetic retinopathy (DR) [ 14 , 15 ]. Among them, automatic diagnosis of eyelid tumors has received widespread attention from scholars and medical professionals due to their life-threatening potential and increasing frequency of incidence.…”

Section: Introductionmentioning

confidence: 99%

HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images

Jiang,

Liu,

et al. 2024

BioMed Eng OnLine

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Background The accurate detection of eyelid tumors is essential for effective treatment, but it can be challenging due to small and unevenly distributed lesions surrounded by irrelevant noise. Moreover, early symptoms of eyelid tumors are atypical, and some categories of eyelid tumors exhibit similar color and texture features, making it difficult to distinguish between benign and malignant eyelid tumors, particularly for ophthalmologists with limited clinical experience. Methods We propose a hybrid model, HM_ADET, for automatic detection of eyelid tumors, including YOLOv7_CNFG to locate eyelid tumors and vision transformer (ViT) to classify benign and malignant eyelid tumors. First, the ConvNeXt module with an inverted bottleneck layer in the backbone of YOLOv7_CNFG is employed to prevent information loss of small eyelid tumors. Then, the flexible rectified linear unit (FReLU) is applied to capture multi-scale features such as texture, edge, and shape, thereby improving the localization accuracy of eyelid tumors. In addition, considering the geometric center and area difference between the predicted box (PB) and the ground truth box (GT), the GIoU_loss was utilized to handle cases of eyelid tumors with varying shapes and irregular boundaries. Finally, the multi-head attention (MHA) module is applied in ViT to extract discriminative features of eyelid tumors for benign and malignant classification. Results Experimental results demonstrate that the HM_ADET model achieves excellent performance in the detection of eyelid tumors. In specific, YOLOv7_CNFG outperforms YOLOv7, with AP increasing from 0.763 to 0.893 on the internal test set and from 0.647 to 0.765 on the external test set. ViT achieves AUCs of 0.945 (95% CI 0.894-0.981) and 0.915 (95% CI 0.860-0.955) for the classification of benign and malignant tumors on the internal and external test sets, respectively. Conclusions Our study provides a promising strategy for the automatic diagnosis of eyelid tumors, which could potentially improve patient outcomes and reduce healthcare costs.

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Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis

Cited by 11 publications

References 47 publications

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images

Ocular microvascular complications in diabetic retinopathy: insights from machine learning

HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images

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