Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis - Breakthrough Device Designation and Validation Results

Aug 4, 2026

Machine learning models are developed to address unmet clinical needs for rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. The system pairs a Myrna™ Instrument with embedded TriVerity™ classifiers that use blood messenger RNA abundances as features. Outputs provide test reports with likelihoods for bacterial infection, viral infection, and severity requiring ICU-level care. Internal validation reports AUROC 0.83 for three-class diagnosis and 0.77 for binary severity prognosis, with FDA breakthrough device designation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Abstract

Introduction

Development data and classifier development

Data gathering and adjudication

Batch effects and platform transfer

Test report structure and scoring bands

Bacterial, Viral, and Severity outputs

System validation and performance

AUROC results and ground truth definitions

Translation from academic prototype to clinical product

arXiv :2407 .02737v1 [ q-bio .QM] 3 Jul 2024 Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis Ljubomir Buturovi∗ Michael Mayhew Roland Luethy Kirindi Choi Uroš Midi Nandita Damaraju Yehudit Hasin-Brumshtein Amitesh Pratap Rhys M. Adams Joao Fonseca Ambika Srinath Paul Fleming Claudia Pereira Oliver Liesenfeld Purvesh Khatri Timothy Sweeney Abstract We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. Our solution consists of a Myrna™ Instrument and embedded TriVerity™ classifiers. The instrument measures abundances of 29 messenger RNAs in patient’s blood, subsequently used as features for machine learning. The classifiers convert the input features to an intuitive test report comprising the separate likelihoods of (1) a bacterial infection (2) a viral infection, and (3) severity (need for Intensive Care Unit-level care) . In internal validation, the system achieved AUROC = 0.83 on the three-class disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on binary prognosis of disease severity. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration (FDA) . This engineering manuscript teaches the standard and novel machine learning methods used to translate an academic research concept to a clinical product aimed at improving patient care, and discusses lessons learned. 1 Introduction New advances on research in applications of machine learning (ML) and artificial intelligence in medicine are published on a regular basis. However, there is lack of literature on translation of these innovations to clinical practice, in particular for tests based on molecular data. To fill the gap, wereport on the development of classifiers for detecting type and severity of infections in patients who present in emergency departments (ED) with symptoms of acute infection and sepsis, an unmet medical need [1] . Current modalities for diagnosing infections mostly rely on detection and identification of pathogens. However, this is inadequate because in the majority of cases, pathogens are not found in blood or anywhere else in the body [2] . A more recent approach relies on response of the immune system to the infection (host response) . This could provide diagnostic and prognostic information regardless of whether a pathogen is eventually identified. From the ML perspective, diagnosing infections in the ED can be reduced to two classification problems: infection type classification (diagnosis) and illness severity classification (prognosis) . The ∗[lbuturovic@inflammatix.com. Authors are at Inflammatix Inc](lbuturovic@inflammatix.com. Authors are at Inflammatix Inc)., Sunnyvale, CA, United States. Preprint. Under review. classifiers use gene expression (abundance of mRNAs) of cells from whole blood as input features. The diagnostic classifier estimates the probability of the patient having bacterial, viral or no infection (BVN), whereas the prognostic binary classifier estimates probability of severe outcome in the given time window (SEV) . We used clinical adjudication [3] as the ground truth for the BVN classifier, and 30-day survival as the ground truth for the SEV classifier. We used 29 mRNAs as input numerical features, measured using a variety of measurement platforms during training and validation, comprising both commerciallyavailable (microarrays, RNA-Seq and molecular barcoding technology (NanoString® ), which are established technologies for measuring gene expression) and our in-development platform Myrna™ , which uses a rapid method called Loop-Mediated Isothermal Amplification (LAMP) . The LAMP experiments were performed using two approaches: 1 . “benchtop LAMP”, which is not fullyautomated and uses a commercially available instrument. It was utilized

FAQs

What clinical problems do the proposed machine learning classifiers target?

They target rapid and accurate diagnosis and prognosis of acute infections and sepsis for patients presenting in emergency departments.

How does the Myrna™ Instrument feed data into the ML classifiers?

It measures the abundance of 29 messenger RNAs in patient blood, which are then used as numerical features for the classifiers.

What outputs does the TriVerity™ system provide in the test report?

It produces three scores—Bacterial, Viral, and Severity—derived from underlying classifier probabilities and presented with likelihood bands.

Aug 4, 2026

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