Journal Article

Examination of blood samples using deep learning and mobile microscopy

Pfeil, Juliane, Frohme, Marcus, Nechyporenko, Alina, Schulze, Katja, Hufert, Frank T.

2022

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Algorithms / Automation / Bioinformatics / Biomedical and Life Sciences / Blood / Blood cell detection / Blood cells / Cameras / Care and treatment / Classification / Communicable diseases / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Datasets / Deep Learning / Diagnosis / Evaluation / Humans / Instance segmentation / Laboratories / Life Sciences / Machine learning / Medical examination / Methods / Microarrays / Microscope and microscopy / Microscopy / Mobile microscopy / Morphology / Neural Networks, Computer / Object recognition / Optimization / Patients / Risk factors / Segmentation / Semantics / Smart phones / Smartphone / Smartphones / Training