Andrea Acevedo1, Anna Merino2, Laura Boldú3, Ángel Molina3, Santiago Alférez4, José Rodellar5. 1. Haematology and Cytology Unit, Core Laboratory, Biochemical and Molecular Genetics Department, CDB. Hospital Clínic of Barcelona-IDIBAPS, Barcelona, Spain; Department of Mathematics, Technical University of Catalonia, Barcelona East Engineering School, Barcelona, Spain. 2. Haematology and Cytology Unit, Core Laboratory, Biochemical and Molecular Genetics Department, CDB. Hospital Clínic of Barcelona-IDIBAPS, Barcelona, Spain. Electronic address: amerino@clinic.cat. 3. Haematology and Cytology Unit, Core Laboratory, Biochemical and Molecular Genetics Department, CDB. Hospital Clínic of Barcelona-IDIBAPS, Barcelona, Spain. 4. Department of Applied Mathematics and Computer Science, Universidad del Rosario, Bogotá, Colombia. 5. Department of Mathematics, Technical University of Catalonia, Barcelona East Engineering School, Barcelona, Spain.
Abstract
BACKGROUND: Dysplastic neutrophils commonly show at least 2/3 reduction of the content of cytoplasmic granules by morphologic examination. Recognition of less granulated dysplastic neutrophils by human eyes is difficult and prone to inter-observer variability. To tackle this problem, we proposed a new deep learning model (DysplasiaNet) able to automatically recognize the presence of hypogranulated dysplastic neutrophils in peripheral blood. METHODS: Eight models were generated by varying convolutional blocks, number of layer nodes and fully connected layers. Each model was trained for 20 epochs. The five most accurate models were selected for a second stage, being trained again from scratch for 100 epochs. After training, cut-off values were calculated for a granularity score that discerns between normal and dysplastic neutrophils. Furthermore, a threshold value was obtained to quantify the minimum proportion of dysplastic neutrophils in the smear to consider that the patient might have a myelodysplastic syndrome (MDS). The final selected model was the one with the highest accuracy (95.5%). RESULTS: We performed a final proof of concept with new patients not involved in previous steps. We reported 95.5% sensitivity, 94.3% specificity, 94% precision, and a global accuracy of 94.85%. CONCLUSIONS: The primary contribution of this work is a predictive model for the automatic recognition in an objective way of hypogranulated neutrophils in peripheral blood smears. We envision the utility of the model implemented as an evaluation tool for MDS diagnosis integrated in the clinical laboratory workflow.
BACKGROUND:Dysplastic neutrophils commonly show at least 2/3 reduction of the content of cytoplasmic granules by morphologic examination. Recognition of less granulated dysplastic neutrophils by human eyes is difficult and prone to inter-observer variability. To tackle this problem, we proposed a new deep learning model (DysplasiaNet) able to automatically recognize the presence of hypogranulated dysplastic neutrophils in peripheral blood. METHODS: Eight models were generated by varying convolutional blocks, number of layer nodes and fully connected layers. Each model was trained for 20 epochs. The five most accurate models were selected for a second stage, being trained again from scratch for 100 epochs. After training, cut-off values were calculated for a granularity score that discerns between normal and dysplastic neutrophils. Furthermore, a threshold value was obtained to quantify the minimum proportion of dysplastic neutrophils in the smear to consider that the patient might have a myelodysplastic syndrome (MDS). The final selected model was the one with the highest accuracy (95.5%). RESULTS: We performed a final proof of concept with new patients not involved in previous steps. We reported 95.5% sensitivity, 94.3% specificity, 94% precision, and a global accuracy of 94.85%. CONCLUSIONS: The primary contribution of this work is a predictive model for the automatic recognition in an objective way of hypogranulated neutrophils in peripheral blood smears. We envision the utility of the model implemented as an evaluation tool for MDS diagnosis integrated in the clinical laboratory workflow.
Authors: José Rodellar; Kevin Barrera; Santiago Alférez; Laura Boldú; Javier Laguna; Angel Molina; Anna Merino Journal: Bioengineering (Basel) Date: 2022-05-23