Combining Imaging Flow Cytometry and Machine Learning for High-Throughput Schistocyte Quantification: A SVM Classifier Development and External Validation Cohort. - Université de Picardie Jules Verne Accéder directement au contenu
Article Dans Une Revue EBioMedicine Année : 2022

Combining Imaging Flow Cytometry and Machine Learning for High-Throughput Schistocyte Quantification: A SVM Classifier Development and External Validation Cohort.

Résumé

BACKGROUND: Schistocyte counts are a cornerstone of the diagnosis of thrombotic microangiopathy syndrome (TMA). Their manual quantification is complex and alternative automated methods suffer from pitfalls that limit their use. We report a method combining imaging flow cytometry (IFC) and artificial intelligence for the direct label-free and operator-independent quantification of schistocytes in whole blood. METHODS: We used 135,045 IFC images from blood acquisition among 14 patients to extract 188 features with IDEAS\textregistered software and 128 features from a convolutional neural network (CNN) with Keras framework in order to train a support vector machine (SVM) blood elements' classifier used for schistocytes quantification. FINDING: Keras features showed better accuracy (94.03%, CI: 93.75-94.31%) than ideas features (91.54%, CI: 91.21-91.87%) in recognising whole-blood elements, and together they showed the best accuracy (95.64%, CI: 95.39-95.88%). We obtained an excellent correlation (0.93, CI: 0.90-0.96) between three haematologists and our method on a cohort of 102 patient samples. All patients with schistocytosis (>1% schistocytes) were detected with excellent specificity (91.3%, CI: 82.0-96.7%) and sensitivity (100%, CI: 89.4-100.0%). We confirmed these results with a similar specificity (91.1%, CI: 78.8-97.5%) and sensitivity (100%, CI: 88.1-100.0%) on a validation cohort (n=74) analysed in an independent healthcare centre. Simultaneous analysis of 16 samples in both study centres showed a very good correlation between the 2 imaging flow cytometers (Y=1.001x). INTERPRETATION: We demonstrate that IFC can represent a reliable tool for operator-independent schistocyte quantification with no pre-analytical processing which is of most importance in emergency situations such as TMA. FUNDING: None.

Dates et versions

hal-03760905 , version 1 (25-08-2022)

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Citer

Julien Demagny, Camille Roussel, Maïlys Le Guyader, Eric Guiheneuf, Véronique Harrivel, et al.. Combining Imaging Flow Cytometry and Machine Learning for High-Throughput Schistocyte Quantification: A SVM Classifier Development and External Validation Cohort.. EBioMedicine, 2022, 83, pp.104209. ⟨10.1016/j.ebiom.2022.104209⟩. ⟨hal-03760905⟩
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