Dataset of fundus images for the diagnosis of ocular toxoplasmosis

Cardozo, Olivia; Ojeda, Verena; Parra, Rodrigo; Román, Julio César Mello; Noguera, José Luis Vázquez; García-Torres, Miguel; Divina, Federico; Villalba, Cynthia; Facon, Jacques; Benítez, Verónica Elisa Castillo; Matto, Ingrid Castro; Aquino-Brítez, Diego

doi:10.1016/j.dib.2023.109056

Cited by 7 publications

(1 citation statement)

References 6 publications

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“…A comprehensive overview of previous work has been presented in Table 1. In the recent studies [42] and [43], datasets from Hospital de Clínicas and Hospital General Pedriático Acosta Ñu medical centers in Asunción, Paraguay [44] were utilized for the diagnosis of OT. The former study employed a pre-trained RestNet18 network, while the latter used a trust-based method, achieving accuracies of 0.938 and 0.969, respectively.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation

Alam,

Shuvo,

Ali

et al. 2024

IEEE Access

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Diagnosis of Ocular toxoplasmosis (OT) usually involves clinical examination and imaging, which can be expensive and require specialized personnel. The use of artificial intelligence (AI) to analyze fundus images for diagnosing ocular diseases is gaining traction. Despite that, there has not been much work done focusing on the detection of OT. To address this issue, we conducted a benchmark study that evaluates the effectiveness of existing pre-trained networks using transfer learning techniques to detect and segment OT lesions from fundus images. The goal of this study is to provide insights for future researchers interested in harnessing deep learning (DL) techniques for automated, easy-to-use, and precise diagnostic approaches of OT using retinal fundus images. Along with that, we have performed an in-depth analysis of different feature extraction techniques to find the most optimal one for the classification and segmentation of lesions. For classification tasks, we have evaluated pre-trained models such as VGG16, MobileNetV2, InceptionV3, ResNet50, and DenseNet121 models. Among them, MobileNetV2 outperformed all other models in terms of Accuracy (Acc.), Recall, and F1-Score outperforming the second-best InceptionV3 by 0.7% higher Acc. However, DenseNet121 achieved the best result in terms of Precision, which was 0.1% higher than MobileNetV2. For the segmentation task, we replaced the encoder block of the U-Net with pretrained MobileNetV2, InceptionV3, ResNet34, and VGG16 and trained with two different loss functions (Dice loss and Jaccard loss). The MobileNetV2/U-Net outperformed ResNet34 by 0.5% and 2.1% in terms of Acc. and Dice Score, respectively when the most optimum Jaccard loss function is employed during the training. The results mentioned in this study verify the effectiveness of the DL techniques in the diagnosis of OT.

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Section: Introductionmentioning

confidence: 99%

Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation

Alam,

Shuvo,

Ali

et al. 2024

IEEE Access

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Is Automated Machine Learning useful for ocular toxoplasmosis identification and classification of the inflammatory activity?

Cifuentes-González,

Rojas-Carabali,

Mejía-Salgado

et al. 2024

AJO International

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Ocular outcomes of toxoplasmosis in the Amazon–Cerrado transition zone, Brazil: a population-based study

Salamoni,

da Costa,

Giachini

et al. 2024

Transactions of the Royal Society of Tropical Medicine and Hygiene

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Background Eye damage represents one of the gravest outcomes of toxoplasmosis, potentially leading to varying degrees of vision impairment, up to and including blindness. Monitoring ophthalmological indicators in individuals affected by toxoplasmosis, alongside understanding its impact on visual acuity, can motivate the scientific community to advocate for strategies to prevent vision loss associated with toxoplasmosis. Methods This is a retrospective study analysing medical records and clinical documentation from patients at an ophthalmological clinic in a city located at the intersection of the Cerrado and Amazon Forest biomes. Results It was noted that >20% of patients exhibited visual acuity ≤20/200, a condition medically recognized as blindness for the affected eye. Our study highlights a significant portion of individuals experiencing lasting visual impairments due to toxoplasmosis. Conclusions These findings remain obscured by the existing national guidelines, which only mandate reporting for congenital and gestational toxoplasmosis cases, thereby overlooking sporadic manifestations and their consequential effects. The effort to identify ophthalmologic consequences is crucial both for personalized clinical care and for shaping national guidelines for managing toxoplasmosis.

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Dataset of fundus images for the diagnosis of ocular toxoplasmosis

Cited by 7 publications

References 6 publications

Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation

Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation

Is Automated Machine Learning useful for ocular toxoplasmosis identification and classification of the inflammatory activity?

Ocular outcomes of toxoplasmosis in the Amazon–Cerrado transition zone, Brazil: a population-based study

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