Abstract Background Laboratory identification of lupus anticoagulant (LAC) is critical for diagnosing antiphospholipid antibody syndrome, a common cause of unprovoked thromboembolic events and pregnancy morbidity. Commonly used clot-based assays for LAC include the dilute Russell’s viper venom time (DRVVT) and activated partial thromboplastin time (APTT), but these are susceptible to anticoagulant interferences. Despite standardization efforts, LAC interpretation remains challenging due to pre- and post-analytical complexity and limited expertise. This study explored the use of deep neural networks (DNN) to facilitate LAC diagnosis. Methods Retrospective data for LAC profiling with 13 features performed at Mayo Clinic between 2021 and 2022 was collected. The profiles of 7,202 patients were randomly divided into training (n=4608), validation (n=1153), and test cohorts (n=1441). Diagnostic outputs including LAC positivity (determined by DRVVT or APTT) and the presence of warfarin (WAR) and heparin (HEP), were confirmed by expert assessment. Three DNN architectures were developed and evaluated (Figure 1): (1) four single output DNN models with a domain knowledge driven selection of input features, (2) four single output DNN models using the same 13 inputs across all models, and (3) a multi-output DNN model. Model performance was evaluated using the F1 score and the index of balanced accuracy (IBA) for each output label. Results Architecture 1 achieved high diagnostic accuracy with IBA values of 0.989-1.000 for all four outputs and F1 scores of 0.945-0.991. Architecture 2 exhibited F1 scores of 0.902-0.973 and IBA values of 0.935-0.992. Architecture 3 achieved F1 scores of 0.933-0.967 and IBA values of 0.941-0.983. Conclusions All three architectures accurately classify LAC and common anticoagulation effects. The comparable performance of architectures 1 and 2 suggests that manual feature extraction is unnecessary. Given its relative simplicity and versatility, architecture 3 is the preferred choice for implementation in a clinical laboratory to standardize LAC diagnosis.
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