Identification of Snails and Schistosoma of Medical Importance via Convolutional Neural Networks: A Proof-of-Concept Application for Human Schistosomiasis

Abstract

In recent decades, computer vision has proven remarkably effective in addressing diverse issues in public health, from determining the diagnosis, prognosis, and treatment of diseases in humans to predicting infectious disease outbreaks. Here, we investigate whether convolutional neural networks (CNNs) can also demonstrate effectiveness in classifying the environmental stages of parasites of public health importance and their invertebrate hosts. We used schistosomiasis as a reference model. Schistosomiasis is a debilitating parasitic disease transmitted to humans via snail intermediate hosts. The parasite affects more than 200 million people in tropical and subtropical regions. We trained our CNN, a feed-forward neural network, on a limited dataset of 5,500 images of snails and 5,100 images of cercariae obtained from schistosomiasis transmission sites in the Senegal River Basin, a region in western Africa that is hyper-endemic for the disease. The image set included both images of two snail genera that are relevant to schistosomiasis transmission - that is, Bulinus spp. and Biomphalaria pfeifferi - as well as snail images that are non-component hosts for human schistosomiasis. Cercariae shed from Bi. pfeifferi and Bulinus spp. snails were classified into 11 categories, of which only two, S. haematobium and S. mansoni, are major etiological agents of human schistosomiasis. The algorithms, trained on 80% of the snail and parasite dataset, achieved 99% and 91% accuracy for snail and parasite classification, respectively, when used on the hold-out validation dataset - a performance comparable to that of experienced parasitologists. The promising results of this proof-of-concept study suggests that this CNN model, and potentially similar replicable models, have the potential to support the classification of snails and parasite of medical importance. In remote field settings where machine learning algorithms can be deployed on cost-effective and widely used mobile devices, such as smartphones, these models can be a valuable complement to laboratory identification by trained technicians. Future efforts must be dedicated to increasing dataset sizes for model training and validation, as well as testing these algorithms in diverse transmission settings and geographies.

Keywords: computer vision & image processing; deep learning - artificial neural network; image classification; neglected tropical disease; schistosomiais.

Figure 1

The life cycle of Schistosoma spp. The adult worms live and reproduce sexually within the human host and their eggs are released in the feces. In the environment, eggs must reach the water, and under appropriate conditions miracidia hatch and seek an intermediate freshwater snail host in the surroundings. The larval stages of the worms develop via asexual reproduction. Cercariae are the free-swimming larvae that are released from the snails and seek human hosts, completing the lifecycle.

Figure 2

Image examples for snail and parasite categories. For snail categories, (A-1,A-2): Biomphalaria. (B-1,B-2): Bulinus. (C-1,C-2): Radix natalensis. (D-1,D-2): Melanoides spp. For parasite categories, HS, Human-schisto; NHS1, Nonhuman-schisto forktail type I; NHS2, Nonhuman-schisto forktail type II; AM, Amphistome cercariae; BO, Schistosoma bovis; EC, Echinostome cercariae; GY, Gymnocephalus cercariae; ME, Metacercaria; PP, Parapleurolophocercous cercariae; PT, Parthenitae; XI, Xiphidiocercariae.

Figure 3

Confusion matrix of classification on test set. (A) results of snail image set, labels: 0-Biomphalaria spp., 1: Bulinus spp., 2: Radix natalensis, 3: Melanoides spp. (B) results of parasite image set, labels: 0: Amphistome cercariae, 1: Schistosoma bovis, 2: Echinostome cercariae, 3: Gymnocephalus cercariae, 4: Human-schisto, 5: Metacercaria, 6: Parapleurolophocercous cercariae, 7: Parthenitae, 8: Non-human- schisto forktail type I, 9: Non-human- schisto forktail type II, 10: Xiphidiocercariae. (C) Combining other trematodes as one category, labels: 0: Schisto, 1: Non-human forktail type I, type II, and bovis, 2: Other trematodes.

Figure 4

Comparison of classification performance with CNN and parasitologist. CNN's performance represented by the ROC curve (in blue) exceeds that of trained parasitologists when their sensitivity and specificity points (in red) fall below the ROC curve. The green points represent the average of the parasitologists (average sensitivity and specificity of all red points), with error bars denoting one standard deviation. We simplify the 11 categories of parasite to only three categories of interest for schistosomiasis environmental risk mapping: (A) human schistosomes, (B) non-human forktail cercariae, and (C) other trematode morphotypes. The area under the curve (AUC) for each case is over 95%.