Combining imaging flow cytometry and machine learning for high-throughput schistocyte quantification: A SVM classifier development and external validation cohort.
EBioMedicine
; 83: 104209, 2022 Sep.
Article
em En
| MEDLINE
| ID: mdl-35986949
ABSTRACT
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® 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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Máquina de Vetores de Suporte
Tipo de estudo:
Guideline
Limite:
Humans
Idioma:
En
Revista:
EBioMedicine
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
França