Machine-Based Detection and Classification for Bone Marrow Aspirate Differential Counts: Initial Development Focusing on Non-Neoplastic Cells
| Autor: | David A. Gutman, Bradley Drumheller, Mohamed Amgad, David L. Jaye, Ahmed A Aljudi, Lee Cooper, Ramraj Chandradevan, Nilakshan Kunananthaseelan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Computer science Datasets as Topic Bone Marrow Cells Cell Count Machine learning computer.software_genre Article Pathology and Forensic Medicine Machine Learning 03 medical and health sciences 0302 clinical medicine Bone marrow aspirate Hematologic disorders Pathology Humans Molecular Biology business.industry Digital pathology Objective method Cell Biology Gold standard (test) 3. Good health 030104 developmental biology Early results 030220 oncology & carcinogenesis Artificial intelligence business computer Automated method |
| Zdroj: | Laboratory investigation; a journal of technical methods and pathology |
| ISSN: | 1530-0307 0023-6837 |
| Popis: | Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system that we developed for annotating and managing digital pathology images, over 10,000 cells from scanned whole slide images of BMA smears were manually annotated, including all classes that comprise the standard clinical DCC. We implemented a two-stage, detection and classification approach that allows design flexibility and improved classification accuracy. In a sixfold cross-validation, our algorithms achieved high overall accuracy in detection (0.959 ± 0.008 precision-recall AUC) and classification (0.982 ± 0.03 ROC AUC) using nonneoplastic samples. Testing on a small set of acute myeloid leukemia and multiple myeloma samples demonstrated similar detection and classification performance. In summary, our algorithms showed promising early results and represent an important initial step in the effort to devise a reliable, objective method to automate DCCs. With further development to include formal clinical validation, such a system has the potential to assist in disease diagnosis and prognosis, and significantly impact clinical practice. Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for classification of hematologic disorders. Manual DCCs are still considered the gold standard as a reliable automated method is yet to be developed. Digital pathology and machine learning represent a highly promising technology for this purpose. The authors report their experience developing machine learning algorithms to detect and classify BMA cells. Promising early results signify an important initial step in the effort to devise a reliable, objective method to automate DCCs. |
| Databáze: | OpenAIRE |
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