Smartphone camera‐based analysis of ELISA using artificial neural network

Abstract

The proposed method indicates an inexpensive, portable, and easily accessible method for the quantitative analysis of medical samples for the detection of disease in the enzyme-linked immunosorbent assay (ELISA). The procedure follows a point-of-care diagnostic model and attends to the several challenges in healthcare system in rural settings. The proposed technique will alleviate the inconveniences faced by the average citizen of a country with insufficient resources to implement an affordable healthcare administration for its entire population. A smartphone is used to procure images of an ELISA containing para-nitrophenol samples which is then fed into a machine learning algorithm, specifically artificial neural network. The introduction of two relatively new technologies in medical aid-the smartphone and machine learning not only reduces cost and time of detection, but also presents ample possibility for further development. The predictions result in highly accurate diagnostic labels. The same method can be used for blood samples for the prediction of presence of any disease, provided adequate training set has been deployed.

Pixel-Based Machine Learning and Image Reconstitution for Dot-ELISA Pathogen Diagnosis in Biological Samples

“…Concerning previous work in the field, only a few attempts have been made mainly based on deep learning with most of them serving to complement other procedures. Thus, a deep learning approach has been employed to predict positives in ELISA microplates ( Nath et al, 2018 ) based on the training of an artificial neural network with microplate images of known sample status and the application of the trained algorithm on microplate images of unknown samples. In another report, machine learning was used as an additional step in a procedure used to evaluate transformed ELISA microplate images of a Cellphone-Based Hand-Held Microplate Reader ( Berg et al, 2015 ).…”

Smartphone-based mobile biosensors for the point-of-care testing of human metabolites

“…With the characteristics of strong specificity, high sensitivity, and simple operation, fluorescent analysis is commonly used to detect antibodies, nucleic acids, and metabolites biomarkers in clinical diagnosis [ [60] , [61] , [62] , [63] , [64] ]. Since the generation of fluorescence requires radiative excitation, smartphone-based fluorescent detection typically requires external accessories, such as an emission filter, an excitation filter, an excitation source or a black box.…”