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

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.

Is decision making really impaired in eating disorders?

OBJECTIVE: Decision making has been reported to be reduced in eating disorders. However, studies are sparse and have been carried out in various selected populations. In the current study we arranged to confirm previous observations and to assess the relationship between decision making and dimensions relevant to eating disorders. METHOD: Patients suffering from anorexia nervosa (n = 49), bulimia nervosa (n = 38), and healthy controls (n = 83) were assessed using the Iowa Gambling Task (IGT). All patients were euthymic and free of psychotropic medication. Self-questionnaires (Eating Disorder Inventory-2; Gardner, 1991; and Eating Attitude Test; Garner & Garfinkel, 1979) were used to assess clinical dimensions relevant to eating disorders. RESULTS: No significant differences in IGT performance were observed between patients and healthy controls or between restrictive and purging types of anorexia nervosa. No correlations were found between IGT performance and eating disorder questionnaires. CONCLUSION: These results do not support reduced decision making in patients with eating disorders, and suggest that previously reported alterations could be related to other clinical characteristics. This should stimulate new topic-related studies designed to reach a firm conclusion.