Helminth Egg Automatic Detector (HEAD): Improvements in development for digital identification and quantification of Helminth eggs and its application online

Jiménez, B.; Maya, C.; Velásquez, G Vilma Florisa; Barrios, J.A.; Pérez, Mónica; Román, Angélica

doi:10.1016/j.mex.2020.101158

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…However, artificial intelligence-based methods, utilising algorithms for the identification of captured helminth eggs, recently emerged and have the potential to eliminate examiner-dependent inconsistency. Jiménez et al (2020) established a Helminth Egg Automatic Detector (HEAD) that is capable of differentiating seven helminth species derived from wastewater, sludge, biosolids, faeces and soils. Furthermore, Lee et al (2021) developed a Helminth Egg Analysis Platform (HEAP) with the ability to discriminate between helminth eggs of 17 species and simultaneous quantification of the faecal egg count.…”

Section: Detection Methods For Egg Contaminationmentioning

confidence: 99%

Potentials and challenges in the isolation and detection of ascarid eggs in complex environmental matrices

Waindok

Raulf

Strübe

2022

Food and Waterborne Parasitology

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Section: Detection Methods For Egg Contaminationmentioning

confidence: 99%

Potentials and challenges in the isolation and detection of ascarid eggs in complex environmental matrices

Waindok

Raulf

Strübe

2022

Food and Waterborne Parasitology

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“…Thus, the development and application of simple and sensitive diagnostic methods to detect patients, especially low worm burden individuals, are urgently needed. In the era of artificial intelligence, deep-learning-based egg recognition may be an alternative method to differentiate C. sinensis eggs from the eggs of other intestinal trematodes (Jiménez et al ., 2020 ). Collaboration between several disciplines and biotechnology to develop simple, easily adaptable on-site and reliable diagnostic methods may contribute to mass surveys (Ju et al ., 2016 ; Yoo et al ., 2020 ).…”

Section: Interdisciplinary One Health Approach For Eradication Of ...mentioning

confidence: 99%

Current status of Clonorchis sinensis and clonorchiasis in Korea: epidemiological perspectives integrating the data from human and intermediate hosts

Yoo

Sohn²,

Na³

2022

Parasitology

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Clonorchis sinensis is a carcinogenic liver fluke that causes clonorchiasis in humans. Clonorchiasis is prevalent in East Asian countries, and approximately 15–20 million individuals are estimated to be infected with this fluke globally. This review highlights the current status of C. sinensis and clonorchiasis in Korea from the epidemiological perspective involving the analysis of humans and intermediate hosts. Despite the recent decline in C. sinensis infection rate in Korea, C. sinensis infections remain endemic in 5 major river basins (Han-gang, Geum-gang, Seomjin-gang, Yeongsan-gang and Nakdong-gang; gang means river) with a high incidence of cholangiocarcinoma. A noticeable pattern involves increasing mild infections among patients diagnosed positive for C. sinensis eggs. The infection rate of C. sinensis metacercariae in the second intermediate host, freshwater fish, is also maintained at a substantial level. Thus, the One Health approach integrating different sectors and disciplines is recommended to accelerate and sustain control of C. sinensis, thereby leading to successful eradication. Health promotion via information dissemination and health education should be extended to prevent the consumption of raw freshwater fish by residents living in high-risk areas.

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Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networks

Thanchomnang,

Chaibutr,

Maleewong

et al. 2024

PeerJ

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Background Human opisthorchiasis is a dangerous infectious chronic disease distributed in many Asian areas in the water-basins of large rivers, Siberia, and Europe. The gold standard for human opisthorchiasis laboratory diagnosis is the routine examination of Opisthorchis spp. eggs under a microscope. Manual detection is laborious, time-consuming, and dependent on the microscopist’s abilities and expertise. Automatic screening of Opisthorchis spp. eggs with deep learning techniques is a useful diagnostic aid. Methods Herein, we propose a convolutional neural network (CNN) for classifying and automatically detecting O. viverrini eggs from digitized images. The image data acquisition was acquired from infected human feces and was processed using the gold standard formalin ethyl acetate concentration technique, and then captured under the microscope digital camera at 400x. Microscopic images containing artifacts and O.viverrini egg were augmented using image rotation, filtering, noising, and sharpening techniques. This augmentation increased the image dataset from 1 time to 36 times in preparation for the training and validation step. Furthermore, the overall dataset was subdivided into a training-validation and test set at an 80:20 ratio, trained with a five-fold cross-validation to test model stability. For model training, we customized a CNN for image classification. An object detection method was proposed using a patch search algorithm to detect eggs and their locations. A performance matrix was used to evaluate model efficiency after training and IoU analysis for object detection. Results The proposed model, initially trained on non-augmented data of artifacts (class 0) and O. viverrini eggs (class 1), showed limited performance with 50.0% accuracy, 25.0% precision, 50.0% recall, and a 33.0% F1-score. After implementing data augmentation, the model significantly improved, reaching 100% accuracy, precision, recall, and F1-score. Stability assessments using 5-fold cross-validation indicated better stability with augmented data, evidenced by an ROC-AUC metric improvement from 0.5 to 1.00. Compared to other models such as ResNet50, InceptionV3, VGG16, DenseNet121, and Xception, the proposed model, with a smaller file size of 2.7 MB, showed comparable perfect performance. In object detection, the augmented data-trained model achieved an IoU score over 0.5 in 139 out of 148 images, with an average IoU of 0.6947. Conclusion This study demonstrated the successful application of CNN in classifying and automating the detection of O. viverrini eggs in human stool samples. Our CNN model’s performance metrics and true positive detection rates were outstanding. This innovative application of deep learning can automate and improve diagnostic precision, speed, and efficiency, particularly in regions where O. viverrini infections are prevalent, thereby possibly improving infection sustainable control and treatment program.

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Helminth Egg Automatic Detector (HEAD): Improvements in development for digital identification and quantification of Helminth eggs and its application online

Cited by 3 publications

References 11 publications

Potentials and challenges in the isolation and detection of ascarid eggs in complex environmental matrices

Potentials and challenges in the isolation and detection of ascarid eggs in complex environmental matrices

Current status of Clonorchis sinensis and clonorchiasis in Korea: epidemiological perspectives integrating the data from human and intermediate hosts

Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networks

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