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Examination of blood samples using deep learning and mobile microscopy
BACKGROUND: Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-ca...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8832798/ https://www.ncbi.nlm.nih.gov/pubmed/35148679 http://dx.doi.org/10.1186/s12859-022-04602-4 |
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author | Pfeil, Juliane Nechyporenko, Alina Frohme, Marcus Hufert, Frank T. Schulze, Katja |
author_facet | Pfeil, Juliane Nechyporenko, Alina Frohme, Marcus Hufert, Frank T. Schulze, Katja |
author_sort | Pfeil, Juliane |
collection | PubMed |
description | BACKGROUND: Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards. RESULTS: Instance segmentation models such as Mask R-CNN, Mask Scoring R-CNN, D2Det and YOLACT were trained and optimised for the detection and classification of all blood cell types. These networks were not designed to detect very small objects in large numbers, so extensive modifications were necessary. Thus, segmentation of all blood cell types and their classification was feasible with great accuracy: qualitatively evaluated, mean average precision of 0.57 and mean average recall of 0.61 are achieved for all blood cell types. Quantitatively, 93% of ground truth blood cells can be detected. CONCLUSIONS: Mobile blood testing as a point-of-care system can be performed with diagnostic accuracy using deep learning methods. In the future, this application could enable very fast, cheap, location- and knowledge-independent patient care. |
format | Online Article Text |
id | pubmed-8832798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88327982022-02-15 Examination of blood samples using deep learning and mobile microscopy Pfeil, Juliane Nechyporenko, Alina Frohme, Marcus Hufert, Frank T. Schulze, Katja BMC Bioinformatics Research BACKGROUND: Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards. RESULTS: Instance segmentation models such as Mask R-CNN, Mask Scoring R-CNN, D2Det and YOLACT were trained and optimised for the detection and classification of all blood cell types. These networks were not designed to detect very small objects in large numbers, so extensive modifications were necessary. Thus, segmentation of all blood cell types and their classification was feasible with great accuracy: qualitatively evaluated, mean average precision of 0.57 and mean average recall of 0.61 are achieved for all blood cell types. Quantitatively, 93% of ground truth blood cells can be detected. CONCLUSIONS: Mobile blood testing as a point-of-care system can be performed with diagnostic accuracy using deep learning methods. In the future, this application could enable very fast, cheap, location- and knowledge-independent patient care. BioMed Central 2022-02-11 /pmc/articles/PMC8832798/ /pubmed/35148679 http://dx.doi.org/10.1186/s12859-022-04602-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Pfeil, Juliane Nechyporenko, Alina Frohme, Marcus Hufert, Frank T. Schulze, Katja Examination of blood samples using deep learning and mobile microscopy |
title | Examination of blood samples using deep learning and mobile microscopy |
title_full | Examination of blood samples using deep learning and mobile microscopy |
title_fullStr | Examination of blood samples using deep learning and mobile microscopy |
title_full_unstemmed | Examination of blood samples using deep learning and mobile microscopy |
title_short | Examination of blood samples using deep learning and mobile microscopy |
title_sort | examination of blood samples using deep learning and mobile microscopy |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8832798/ https://www.ncbi.nlm.nih.gov/pubmed/35148679 http://dx.doi.org/10.1186/s12859-022-04602-4 |
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