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Enhancing Lupus Anticoagulant Testing with Machine Learning: Deep Neural Networks Match Expert Performance without

Jeffrey Wang¹, Rachel Leger², Dong Chen²

¹Biology Department, Carleton College, Northfield, MN, United States.

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|April 16, 2025

Summary

Deep neural networks can automate lupus anticoagulant (LAC) interpretation, a key diagnostic criterion for antiphospholipid antibody syndrome. A multioutput DNN demonstrated comparable or superior performance to traditional methods, offering a versatile solution for clinical laboratories.

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Area of Science:

Medical Diagnostics
Artificial Intelligence in Medicine
Computational Biology

Background:

Lupus anticoagulant (LAC) is crucial for diagnosing antiphospholipid antibody syndrome.
Current LAC testing involves complex interpretation of up to 13 tests by physicians.
Automating LAC interpretation is a significant goal for clinical laboratories.

Purpose of the Study:

To explore the feasibility of using deep neural network (DNN) architectures for multilabel classification of LAC profiles.
To assess the potential of DNNs in automating the interpretation of LAC testing.
To compare different DNN architectures for accuracy and efficiency.

Main Methods:

A dataset of 7,202 retrospective cases was used, randomly split for training, validation, and testing.
Two DNN architectures were evaluated: single-output DNNs with feature selection and a multioutput DNN using all 13 inputs.
LAC positivity (DRVVT, APTT) and anticoagulant presence (warfarin, heparin) were adjudicated by an expert.

Main Results:

The domain-knowledge-naïve multioutput DNN showed comparable or improved performance across all four prediction tasks.
Achieved high F1 scores: 0.977 for LAC-DRVVT, 0.954 for LAC-APTT, 0.961 for HEP, and 0.995 for WAR.
Demonstrated the DNN's ability to learn feature importance without explicit selection.

Conclusions:

Multioutput DNNs offer a versatile and potentially simpler approach to LAC interpretation compared to traditional methods.
The comparable performance suggests DNNs can effectively standardize LAC diagnosis in clinical settings.
The multioutput DNN is recommended for implementation to enhance efficiency and consistency in LAC testing.