Kankanet: An artificial neural network-based object detection smartphone application and mobile microscope as a point-of-care diagnostic aid for soil-transmitted helminthiases

• Published in: PLoS neglected tropical diseases Vol. 13; no. 8; p. e0007577
• Main Authors: Yang, Ariel, Bakhtari, Nahid, Langdon-Embry, Liana, Redwood, Emile, Grandjean Lapierre, Simon, Rakotomanga, Patricia, Rafalimanantsoa, Armand, De Dios Santos, Juan, Vigan-Womas, Inès, Knoblauch, Astrid M., Marcos, Luis A.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.08.2019; Public Library of Science (PLoS)
• Subjects: Ancylostomatoidea - isolation & purification; Animals; Artificial intelligence; Artificial neural networks; Ascaris lumbricoides - isolation & purification; Biochemistry, Molecular Biology; Biology and Life Sciences; Care and treatment; Cellular Biology; Computer and Information Sciences; Data buses; Datasets; Detection; Diagnosis; Diagnostic systems; Eggs; Engineering and Technology; Feces - parasitology; Formaldehyde; Helminthiasis; Helminthiasis - diagnosis; Hookworm Infections - diagnosis; Humans; Image processing; Image Processing, Computer-Assisted; Image quality; Image resolution; Imaging systems; Imaging techniques; Immunology; Iodine; Learning algorithms; Learning theory; Life Sciences; Machine learning; Madagascar; Medical imaging; Medicine and Health Sciences; Microbiology and Parasitology; Microscopy; Molecular biology; Neural networks; Neural Networks, Computer; Object recognition; Parasite Egg Count - instrumentation; Parasite Egg Count - methods; People and Places; Pilots; Point of care testing; Point-of-Care Systems; Santé publique et épidémiologie; Sedimentation; Sensitivity; Sensitivity and Specificity; Smart phones; Smartphone; Smartphones; Software; Soil; Soil - parasitology; Soils; Specificity; Technology; Technology application; Thimerosal; Trichuris - isolation & purification; Tropical diseases; USB (Universal serial bus); Madagascar; United States; New York; Ivory Coast; United States > US
• ISSN: 1935-2735; 1935-2727; 1935-2735
• DOI: 10.1371/journal.pntd.0007577

Endemic areas for soil-transmitted helminthiases often lack the tools and trained personnel necessary for point-of-care diagnosis. This study pilots the use of smartphone microscopy and an artificial neural network-based (ANN) object detection application named Kankanet to address those two needs. A smartphone was equipped with a USB Video Class (UVC) microscope attachment and Kankanet, which was trained to recognize eggs of Ascaris lumbricoides, Trichuris trichiura, and hookworm using a dataset of 2,078 images. It was evaluated for interpretive accuracy based on 185 new images. Fecal samples were processed using Kato-Katz (KK), spontaneous sedimentation technique in tube (SSTT), and Merthiolate-Iodine-Formaldehyde (MIF) techniques. UVC imaging and ANN interpretation of these slides was compared to parasitologist interpretation of standard microscopy.Relative to a gold standard defined as any positive result from parasitologist reading of KK, SSTT, and MIF preparations through standard microscopy, parasitologists reading UVC imaging of SSTT achieved a comparable sensitivity (82.9%) and specificity (97.1%) in A. lumbricoides to standard KK interpretation (97.0% sensitivity, 96.0% specificity). The UVC could not accurately image T. trichiura or hookworm. Though Kankanet interpretation was not quite as sensitive as parasitologist interpretation, it still achieved high sensitivity for A. lumbricoides and hookworm (69.6% and 71.4%, respectively). Kankanet showed high sensitivity for T. trichiura in microscope images (100.0%), but low in UVC images (50.0%). The UVC achieved comparable sensitivity to standard microscopy with only A. lumbricoides. With further improvement of image resolution and magnification, UVC shows promise as a point-of-care imaging tool. In addition to smartphone microscopy, ANN-based object detection can be developed as a diagnostic aid. Though trained with a limited dataset, Kankanet accurately interprets both standard microscope and low-quality UVC images. Kankanet may achieve sensitivity comparable to parasitologists with continued expansion of the image database and improvement of machine learning technology.