Abstract
High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.
Highlights
This study explored the validity of using Deep Learning (DL) algorithms for cytopathological research by classifying three major unlabeled, unstained cell lines of leukemia (MOLT, HL60, and K562)
Smartphone-based experiments have surmounted this problem by providing portable cameras without the need for the direct contact of the camera and the sample during the image taking process. Another issue with wet or liquid samples is the reflection of light from their surface, which can be misleading data for an algorithm trained on dry samples for the same experiment, namely pH detection 101
Since Machine Learning (ML) uses NNs that resemble the human neural system, algorithms may learn more efficaciously from a certain format of representing data compared to other formats
Summary
Unsupervised learning does not employ labeled or supervised output data In this case, the aim is to find structures and regularities in the input data. The needed training input data for supervised ML methods can be obtained from the output of unsupervised ML approaches 7. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are widely-used terms in modern data-driven research These terms are closely related, it is critical to distinguish their differences for better use of methods belonging to each for a particular application. ANNs can perform complex tasks such as decision making, cognition, patterns generating, and learning In this regard, training aims to learn certain parameters of the ANN on a given learning task, which makes the feature selection process a part of the learning process. A combination of supervised and unsupervised ML methods for training DNNs is reported 9, 13-16
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