Abstract

The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance.

Highlights

  • The prospects of artificial intelligence (AI) and machine learning (ML) applications in biomedical research are exciting because they promise to unlock new information from the everexpanding datasets being generated through high-throughput approaches in various areas of research

  • We investigated in this report whether it is possible to discriminate among transplant recipients that either rejected or tolerated their allogeneic pancreatic islet grafts using an ML/AI approach based solely on EPGs generated by MEKC-LIFD analysis in small samples of the immediate graft microenvironment

  • Aqueous humor samples were collected under conditions of immune-mediated rejection or tolerance of the intraocular islet allografts, and MEKC-LIFD analyses were performed to assess their biochemical composition and for comparison of the corresponding EPGs (Fig 2) [18, 22, 26, 27]

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Summary

Introduction

The prospects of artificial intelligence (AI) and machine learning (ML) applications in biomedical research are exciting because they promise to unlock new information from the everexpanding datasets being generated through high-throughput approaches in various areas of research. The Food and Drug Administration (FDA) recently recognized in a public statement on Software as a Medical Device (SaMD) released on January 28th, 2020 that “AI/ML technologies have the potential to transform healthcare by deriving new and important insights from the vast amounts of data generated during the delivery of healthcare every day”. Learning from incremental small amounts of data is closer to the way humans learn. The analysis presented in the current report demonstrates this notion in the context of a relatively small dataset generated using a focused metabolomics approach in pancreatic islet transplant mouse recipients

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