02008nas a2200193   4500000000100000008004100001260003400042653003900076653002800115653001400143100001300157700001300170700001500183245005400198300001200252520150700264022002501771020001801796       2024                            d  bSpringer Fachmedien Wiesbaden10aNeglected tropical diseases (NTDs)10aArtificial Intelligence10adetection1 aBerger V1 aChircu A1 aSultanow E00aAI-Based Detection of Neglected Tropical Diseases  a177-1983 a<p>The diagnosis of parasitic infectious diseases—often called “neglected tropical diseases”—usually requires highly trained medical personnel to analyze microscopy images for evidence of the disease. These skills are very scarce in the affected areas, and diagnosis can be costly and time-consuming. This creates significant challenges for promptly diagnosing, treating, controlling or even eliminating parasitic diseases for more than one billion people who live mostly in poor, vulnerable and marginalized communities in tropical and sub-tropical areas. Two such diseases with significant health impacts are onchocerciasis and schistosomiasis. This paper discusses a system based on artificial intelligence deep learning to support the automated identification of parasites in microscopic images of samples taken from infected individuals. The system highlights areas within an image that contain parasites using semantic segmentation and different visualizations. The convolutional neural network (CNN) architectures E-Net and U-Net are trained using available image data sets and evaluated in order to determine the optimal parameters for the final system. Finally, an inference is implemented that saves the final result as a digital image highlighting the areas containing parasites. The paper demonstrates that a basic implementation of microscope image analysis for parasitic disease detection is possible using the proposed CNN architectures and the available limited data sets.</p>
  a2731-8826, 2731-8834  a9783658448516