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Moradi N, Haji Mohamad Hoseyni F, Hajghassem H, Yarahmadi N, Niknam Shirvan H, Safaie E, Kalantar M, Sefidbakht S, Amini A, Eeltink S. Comprehensive quantitative analysis of erythrocytes and leukocytes using trace volume of human blood using microfluidic-image cytometry and machine learning. LAB ON A CHIP 2023; 23:4868-4875. [PMID: 37867384 DOI: 10.1039/d3lc00692a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
A diagnostic test based on microfluidic image cytometry and machine learning has been designed and applied for accurate classification of erythrocytes and leukocytes, including a unique fully-automated 5-part quantitative differentiation into neutrophils, lymphocytes, monocytes, eosinophils, and basophils, using minute amounts of whole blood in a single counting chamber. A low-cost disposable multilayer microdevice for microfluidic image cytometry was developed that comprises a 1 mm × 22 mm × 70 μm (w × l × h) rectangular microchannel, allowing the analysis of trace volume of blood (20 μL) for each assay. Automated analysis of digitized binary images applying a border following algorithm was performed allowing the qualitative analysis of erythrocytes. Bright-field imaging was used for the detection of erythrocytes and fluorescence imaging for 5-part differentiation of leukocytes after acridine orange staining, applying a convolutional neural network enabling unparalleled speed for identification and automated morphology classification yielding 98.57% accuracy. Blood samples were obtained from 30 volunteers and count values did not significantly differ from data obtained using a commercial automated hematology analyzer.
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Affiliation(s)
- Nima Moradi
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | | | - Hassan Hajghassem
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | - Navid Yarahmadi
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | - Hadi Niknam Shirvan
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | - Erfan Safaie
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | - Mahsa Kalantar
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | | | - Ali Amini
- Vrije Universiteit Brussel, Department of Chemical Engineering, Brussels, Belgium
| | - Sebastiaan Eeltink
- Vrije Universiteit Brussel, Department of Chemical Engineering, Brussels, Belgium
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Cheung HC, De Louche C, Komorowski M. Artificial Intelligence Applications in Space Medicine. Aerosp Med Hum Perform 2023; 94:610-622. [PMID: 37501303 DOI: 10.3357/amhp.6178.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
INTRODUCTION:During future interplanetary space missions, a number of health conditions may arise, owing to the hostile environment of space and the myriad of stressors experienced by the crew. When managing these conditions, crews will be required to make accurate, timely clinical decisions at a high level of autonomy, as telecommunication delays and increasing distances restrict real-time support from the ground. On Earth, artificial intelligence (AI) has proven successful in healthcare, augmenting expert clinical decision-making or enhancing medical knowledge where it is lacking. Similarly, deploying AI tools in the context of a space mission could improve crew self-reliance and healthcare delivery.METHODS: We conducted a narrative review to discuss existing AI applications that could improve the prevention, recognition, evaluation, and management of the most mission-critical conditions, including psychological and mental health, acute radiation sickness, surgical emergencies, spaceflight-associated neuro-ocular syndrome, infections, and cardiovascular deconditioning.RESULTS: Some examples of the applications we identified include AI chatbots designed to prevent and mitigate psychological and mental health conditions, automated medical imaging analysis, and closed-loop systems for hemodynamic optimization. We also discuss at length gaps in current technologies, as well as the key challenges and limitations of developing and deploying AI for space medicine to inform future research and innovation. Indeed, shifts in patient cohorts, space-induced physiological changes, limited size and breadth of space biomedical datasets, and changes in disease characteristics may render the models invalid when transferred from ground settings into space.Cheung HC, De Louche C, Komorowski M. Artificial intelligence applications in space medicine. Aerosp Med Hum Perform. 2023; 94(8):610-622.
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Gasparin AT, Araujo CIF, Cardoso MR, Schmitt P, Godoy JB, Reichert ES, Pimenta ME, Gonçalves CB, Santiago EB, Silva ILR, Gaideski BDP, Cardoso MA, Silva FD, Sommer VDR, Hartmann LF, Perazzoli CRDA, Farias JSDH, Beltrame OC, Winter N, Nicollete DRP, Lopes SNB, Predebon JV, Almeida BMMD, Rogal Júnior SR, Figueredo MVM. Hilab System Device in an Oncological Hospital: A New Clinical Approach for Point of Care CBC Test, Supported by the Internet of Things and Machine Learning. Diagnostics (Basel) 2023; 13:diagnostics13101695. [PMID: 37238184 DOI: 10.3390/diagnostics13101695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/05/2023] [Accepted: 05/05/2023] [Indexed: 05/28/2023] Open
Abstract
The complete blood count (CBC) is a highly requested test that is generally restricted to centralized laboratories, which are limited by high cost, being maintenance-demanding, and requiring costly equipment. The Hilab System (HS) is a small, handheld hematological platform that uses microscopy and chromatography techniques, combined with machine learning (ML) and artificial intelligence (AI), to perform a CBC test. This platform uses ML and AI techniques to add higher accuracy and reliability to the results besides allowing for faster reporting. For clinical and flagging capability evaluation of the handheld device, the study analyzed 550 blood samples of patients from a reference institution for oncological diseases. The clinical analysis encompassed the data comparison between the Hilab System and a conventional hematological analyzer (Sysmex XE-2100) for all CBC analytes. The flagging capability study compared the microscopic findings from the Hilab System and the standard blood smear evaluation method. The study also assessed the sample collection source (venous or capillary) influences. The Pearson correlation, Student t-test, Bland-Altman, and Passing-Bablok plot of analytes were calculated and are shown. Data from both methodologies were similar (p > 0.05; r ≥ 0.9 for most parameters) for all CBC analytes and flagging parameters. Venous and capillary samples did not differ statistically (p > 0.05). The study indicates that the Hilab System provides humanized blood collection associated with fast and accurate data, essential features for patient wellbeing and quick physician decision making.
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Affiliation(s)
- Aléxia Thamara Gasparin
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | | | - Mônica Ribas Cardoso
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Patricia Schmitt
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Juliana Beker Godoy
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Eduarda Silva Reichert
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Maria Eduarda Pimenta
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Caroline Bretas Gonçalves
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Erika Bergamo Santiago
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Ivan Lucas Reis Silva
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Bruno de Paula Gaideski
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Milena Andreuzo Cardoso
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Fernanda D'Amico Silva
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Viviane da Rosa Sommer
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | - Luis Felipe Hartmann
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | | | | | | | - Nicole Winter
- Erasto Gaertner Hospital, Curitiba 81520-060, PR, Brazil
| | | | | | - João Victor Predebon
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
| | | | - Sérgio Renato Rogal Júnior
- Department of Research and Development, Hilab, Jose Altair Possebom, 800, Curitiba 81270-185, PR, Brazil
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Swanberg KM, Kurada AV, Prinsen H, Juchem C. Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles. Sci Rep 2022; 12:13888. [PMID: 35974117 PMCID: PMC9381573 DOI: 10.1038/s41598-022-17741-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 07/29/2022] [Indexed: 12/04/2022] Open
Abstract
Multiple sclerosis (MS) is a heterogeneous autoimmune disease for which diagnosis continues to rely on subjective clinical judgment over a battery of tests. Proton magnetic resonance spectroscopy (1H MRS) enables the noninvasive in vivo detection of multiple small-molecule metabolites and is therefore in principle a promising means of gathering information sufficient for multiple sclerosis diagnosis and subtype classification. Here we show that supervised classification using 1H-MRS-visible normal-appearing frontal cortex small-molecule metabolites alone can indeed differentiate individuals with progressive MS from control (held-out validation sensitivity 79% and specificity 68%), as well as between relapsing and progressive MS phenotypes (held-out validation sensitivity 84% and specificity 74%). Post hoc assessment demonstrated the disproportionate contributions of glutamate and glutamine to identifying MS status and phenotype, respectively. Our finding establishes 1H MRS as a viable means of characterizing progressive multiple sclerosis disease status and paves the way for continued refinement of this method as an auxiliary or mainstay of multiple sclerosis diagnostics.
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Affiliation(s)
- Kelley M. Swanberg
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA ,grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA
| | - Abhinav V. Kurada
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA
| | - Hetty Prinsen
- grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA
| | - Christoph Juchem
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA ,grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA ,grid.21729.3f0000000419368729Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY USA ,grid.47100.320000000419368710Department of Neurology, Yale University School of Medicine, New Haven, CT USA
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