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

Abstract

Background 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. Methodology/Principal findings 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%). Conclusions/Significance 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.

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

“…Taking into account this limited resources together with the highly elevated number of patients in these areas, it is clear that the use of digital microscopes together with artificial intelligence algorithms for remote and automatic diagnosis of STH could constitute an advantageous tool. Although some systems have been previously proposed for the digitalization of images of STH, they require special hardware that has not been specifically designed for acquiring microscopy images (17) or might disrupt the usual laboratory workflow since they do not leverage conventional microscopes making it more complex to follow standard microscope diagnostics protocols (16). Our 3D-printed low-cost digitalization and image acquisition device was specially designed to not alter the daily routine in microscopy diagnosis laboratories.…”

“…proposed to acquire microscopy images with a portable scan connected to a laptop and used a two stage sequential algorithm where candidates previously proposed by the first algorithm are classified as any type of helminth egg, and obtained promising results despite their limited number of training samples (16) .The use of deep learning-based object detection methods for the automatic analysis and detection of helminth eggs on images acquired with smartphone-compatible microscopy attachments has been already tested (17).This work achieved comparable sensitivity to standard microscopy when detecting Ascaris spp. but showed a low performance in the identification of Trichuris spp.…”

Mobile health applications for disease screening and treatment support in low-and middle-income countries: A narrative review

“…reported that mHealth applications were also utilized to accurately diagnose or detect Malignant Melanoma cases among individuals in Poland [ 136 ]. A study in Madagascar has shown that mHealth devices were deployed to accurately diagnose soil-transmitted helminth infections to promote healthcare delivery [ 137 ].…”

Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

“…Therefore, due to the complex learning processes involved in the model development, it may be impossible to figure out what parameters exist in the hidden layers (the so-called black box) or to reproduce the same ANN model ( 12 , 18 ). Although the dropout method was applied to avoid bias and overfitting, and our network was validated for stability, there is risk when applying this network to a larger data set from multiple institutions ( 25 ). Therefore, future validation and adjustment with integral and stable data from multiple institutions would further verify the predictive ability of this ANN model and its application in BA diagnosis.…”