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Kim J, Lee SJ. Digital in-line holographic microscopy for label-free identification and tracking of biological cells. Mil Med Res 2024; 11:38. [PMID: 38867274 PMCID: PMC11170804 DOI: 10.1186/s40779-024-00541-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 05/31/2024] [Indexed: 06/14/2024] Open
Abstract
Digital in-line holographic microscopy (DIHM) is a non-invasive, real-time, label-free technique that captures three-dimensional (3D) positional, orientational, and morphological information from digital holographic images of living biological cells. Unlike conventional microscopies, the DIHM technique enables precise measurements of dynamic behaviors exhibited by living cells within a 3D volume. This review outlines the fundamental principles and comprehensive digital image processing procedures employed in DIHM-based cell tracking methods. In addition, recent applications of DIHM technique for label-free identification and digital tracking of various motile biological cells, including human blood cells, spermatozoa, diseased cells, and unicellular microorganisms, are thoroughly examined. Leveraging artificial intelligence has significantly enhanced both the speed and accuracy of digital image processing for cell tracking and identification. The quantitative data on cell morphology and dynamics captured by DIHM can effectively elucidate the underlying mechanisms governing various microbial behaviors and contribute to the accumulation of diagnostic databases and the development of clinical treatments.
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Affiliation(s)
- Jihwan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, 37673, Republic of Korea
| | - Sang Joon Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, 37673, Republic of Korea.
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2
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Park J, Bai B, Ryu D, Liu T, Lee C, Luo Y, Lee MJ, Huang L, Shin J, Zhang Y, Ryu D, Li Y, Kim G, Min HS, Ozcan A, Park Y. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat Methods 2023; 20:1645-1660. [PMID: 37872244 DOI: 10.1038/s41592-023-02041-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/11/2023] [Indexed: 10/25/2023]
Abstract
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.
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Affiliation(s)
- Juyeon Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Bijie Bai
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chungha Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Yi Luo
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jeongwon Shin
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | | | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
- Tomocube, Daejeon, Republic of Korea.
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Kang CC, Lee TY, Lim WF, Yeo WWY. Opportunities and challenges of 5G network technology toward precision medicine. Clin Transl Sci 2023; 16:2078-2094. [PMID: 37702288 PMCID: PMC10651640 DOI: 10.1111/cts.13640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/14/2023] Open
Abstract
Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.
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Affiliation(s)
- Chia Chao Kang
- School of Electrical Engineering and Artificial IntelligenceXiamen University MalaysiaSepangSelangorMalaysia
| | - Tze Yan Lee
- School of Liberal Arts, Science and Technology (PUScLST)Perdana UniversityKuala LumpurMalaysia
| | - Wai Feng Lim
- Sunway Medical CentreSubang JayaSelangor Darul EhsanMalaysia
| | - Wendy Wai Yeng Yeo
- School of PharmacyMonash University MalaysiaBandar SunwaySelangor Darul EhsanMalaysia
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4
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Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS, Al Harbi S, Albekairy AM. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC MEDICAL EDUCATION 2023; 23:689. [PMID: 37740191 PMCID: PMC10517477 DOI: 10.1186/s12909-023-04698-z] [Citation(s) in RCA: 121] [Impact Index Per Article: 121.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/19/2023] [Indexed: 09/24/2023]
Abstract
INTRODUCTION Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. RESEARCH SIGNIFICANCE This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. MATERIALS AND METHODS The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. RESULTS Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. CONCLUSION AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.
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Affiliation(s)
- Shuroug A Alowais
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia.
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia.
| | - Sahar S Alghamdi
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Nada Alsuhebany
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Tariq Alqahtani
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrahman I Alshaya
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Sumaya N Almohareb
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Atheer Aldairem
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mohammed Alrashed
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Khalid Bin Saleh
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Hisham A Badreldin
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Majed S Al Yami
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Shmeylan Al Harbi
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulkareem M Albekairy
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
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Ryu D, Bak T, Ahn D, Kang H, Oh S, Min HS, Lee S, Lee J. Deep learning-based label-free hematology analysis framework using optical diffraction tomography. Heliyon 2023; 9:e18297. [PMID: 37576294 PMCID: PMC10412892 DOI: 10.1016/j.heliyon.2023.e18297] [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: 03/04/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 08/15/2023] Open
Abstract
Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.
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Affiliation(s)
- Dongmin Ryu
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Taeyoung Bak
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Daewoong Ahn
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Hayoung Kang
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Sanggeun Oh
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | | | - Sumin Lee
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Jimin Lee
- Department of Nuclear Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
- Graduate School of Artificial Intelligence (AIGS), Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
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6
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Kim J, Kim J, Kim Y, Go T, Lee SJ. Accelerated settling velocity of airborne particulate matter on hairy plant leaves. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 332:117313. [PMID: 36716541 DOI: 10.1016/j.jenvman.2023.117313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/31/2022] [Accepted: 01/14/2023] [Indexed: 06/18/2023]
Abstract
Phytoremediation has emerged as an ecofriendly technique to reduce hazardous particulate matter (PM) in the air. Although previous studies have conducted statistical analyses to reveal PM removal capabilities of various plant species according to their leaf characteristics, the underlying physical mechanism of PM adsorption of plants remains unclear. Conventional methodologies for measuring PM accumulation usually require long-term field tests and provide limited understanding on PM removal effects of individual leaf traits of various plants. In this study, we propose a novel methodology which can compare the electrostatic interactions between PMs and plant leaves according to their trichome structures by using digital in-line holographic microscopy (DIHM). Surface characteristics of Perilla frutescens and Capsicum annuum leaves are measured to examine electrostatic effects according to the morphological features of trichomes. 3D settling motions of PMs near the microstructures of trichomes of the two plant species are compared in detail. To validate the PM removal effect of the hairy microstructures, a polydimethylsiloxane (PDMS) replica model of a P. frutescens leaf is fabricated to demonstrate accelerated settling velocities of PMs near trichome-like microstructures. The size and electric charge distributions of PMs with irregular shapes are analyzed using DIHM. Numerical simulation of the PM deposition near a trichome-like structure is conducted to verify the empirical results. As a result, the settling velocities of PMs on P. frutescens leaves and a PDMS replica sample are 12.11 ± 1.88% and 34.06 ± 4.19% faster than those on C. annuum leaves and a flat PDMS sample, respectively. These findings indicate that the curved microstructures of hairy trichomes of plant leaves increase the ability to capture PMs by enhancing the electric field intensity just near trichomes. Compared with conventional methods, the proposed methodology can quantitatively evaluate the settling velocity of PMs on various plant leaves according to the morphological structure and density of trichomes within a short period of time. The present research findings would be widely utilized in the selection of suitable air-purifying plants for sustainable removal of harmful air pollutants in urban and indoor environments.
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Affiliation(s)
- Jihwan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Jeongju Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Youngdo Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Taesik Go
- Division of Biomedical Engineering, College of Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do, 54896, Republic of Korea
| | - Sang Joon Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea.
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7
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Kim Y, Kim J, Seo E, Lee SJ. AI-based analysis of 3D position and orientation of red blood cells using a digital in-line holographic microscopy. Biosens Bioelectron 2023; 229:115232. [PMID: 36963327 DOI: 10.1016/j.bios.2023.115232] [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: 12/23/2022] [Revised: 02/23/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
The morphological and mechanical characteristics of red blood cells (RBCs) largely vary depending on the occurrence of hematologic disorders. Variations in the rheological properties of RBCs affect the dynamic motions of RBCs, especially their rotational behavior. However, conventional techniques for measuring the orientation of biconcave-shaped RBCs still have some technical limitations, including complicated optical setups, complex post data processing, and low throughput. In this study, we propose a novel image-based technique for measuring 3D position and orientation of normal RBCs using digital in-line holographic microscopy (DIHM) and artificial intelligence (AI). Formaldehyde-fixed RBCs are immobilized in coagulated polydimethylsiloxane (PDMS). Holographic images of RBCs positioned at various out-of-plane angles are acquired by precisely manipulating the PDMS-trapped RBC sample attached to a 4-axis optical stage. With the aid of deep learning algorithms for data augmentation and regression analysis, the out-of-plane angle of RBCs is directly predicted from the captured holographic images. The 3D position and in-plane angle of RBCs are acquired by employing numerical reconstruction and ellipse detection methods. Combining these digital image processing techniques, the 3D positional and orientational information of each RBC recorded in a single holographic image is measured within 23.5 and 3.07 s, respectively. The proposed AI-based DIHM technique that can extract the 3D position, orientation, and morphology of individual RBCs would be utilized to analyze the dynamic translational and rotational motions of abnormal RBCs with hematologic disorders in shear flows through further research.
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Affiliation(s)
- Youngdo Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Jihwan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Eunseok Seo
- Department of Mechanical Engineering, Sogang University, Seoul, 04107, Republic of Korea
| | - Sang Joon Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea.
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Běhal J, Pirone D, Sirico D, Bianco V, Mugnano M, Del Giudice D, Cavina B, Kurelac I, Memmolo P, Miccio L, Ferraro P. On monocytes and lymphocytes biolens clustering by in flow holographic microscopy. Cytometry A 2023; 103:251-259. [PMID: 36028475 DOI: 10.1002/cyto.a.24685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/29/2022] [Accepted: 08/23/2022] [Indexed: 11/09/2022]
Abstract
Live cells act as biological lenses and can be employed as real-world optical components in bio-hybrid systems. Imaging at nanoscale, optical tweezers, lithography and also photonic waveguiding are some of the already proven functionalities, boosted by the advantage that cells are fully biocompatible for intra-body applications. So far, various cell types have been studied for this purpose, such as red blood cells, bacterial cells, stem cells and yeast cells. White Blood Cells (WBCs) play a very important role in the regulation of the human body activities and are usually monitored for assessing its health. WBCs can be considered bio-lenses but, to the best of our knowledge, characterization of their optical properties have not been investigated yet. Here, we report for the first time an accurate study of two model classes of WBCs (i.e., monocytes and lymphocytes) by means of a digital holographic microscope coupled with a microfluidic system, assuming WBCs bio-lens characteristics. Thus, quantitative phase maps for many WBCs have been retrieved in flow-cytometry (FC) by achieving a significant statistical analysis to prove the enhancement in differentiation among sphere-like bio-lenses according to their sizes (i.e., diameter d) exploiting intensity parameters of the modulated light in proximity of the cell optical axis. We show that the measure of the low intensity area (S: I z < I th z ) in a fixed plane, is a feasible parameter for cell clustering, while achieving robustness against experimental misalignments and allowing to adjust the measurement sensitivity in post-processing. 2D scatterplots of the identified parameters (d-S) show better differentiation respect to the 1D case. The results show that the optical focusing properties of WBCs allow the clustering of the two populations by means of a mere morphological analysis, thus leading to the new concept of cell-optical-fingerprint avoiding fluorescent dyes. This perspective can open new routes in biomedical sciences, such as the chance to find optical-biomarkers at single cell level for label-free diagnosis.
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Affiliation(s)
- Jaromír Běhal
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- Department of Optics, Palacký University, Olomouc, Czech Republic
| | - Daniele Pirone
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- DIETI, Department of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy
| | - Daniele Sirico
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- Department of Chemical, Materials and Production Engineering of the University of Naples Federico II, Naples, Italy
| | - Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Martina Mugnano
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Danila Del Giudice
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- Department of Mathematics and Physics, University of Campania "L. Vanvitelli", Caserta, Italy
| | - Beatrice Cavina
- Department of Medical and Surgical Sciences (DIMEC), Centro di Studio e Ricerca sulle Neoplasie (CSR) Ginecologiche, Alma Mater Studiorum-University of Bologna, Bologna, Italy
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
| | - Ivana Kurelac
- Department of Medical and Surgical Sciences (DIMEC), Centro di Studio e Ricerca sulle Neoplasie (CSR) Ginecologiche, Alma Mater Studiorum-University of Bologna, Bologna, Italy
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
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Nguyen TL, Pradeep S, Judson-Torres RL, Reed J, Teitell MA, Zangle TA. Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine. ACS NANO 2022; 16:11516-11544. [PMID: 35916417 PMCID: PMC10112851 DOI: 10.1021/acsnano.1c11507] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.
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10
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Akcakır O, Celebi LK, Kamil M, Aly ASI. Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears. BIOMEDICAL OPTICS EXPRESS 2022; 13:3904-3921. [PMID: 35991917 PMCID: PMC9352279 DOI: 10.1364/boe.448099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/28/2022] [Accepted: 01/28/2022] [Indexed: 06/15/2023]
Abstract
Diagnosis of malaria in endemic areas is hampered by the lack of a rapid, stain-free and sensitive method to directly identify parasites in peripheral blood. Herein, we report the use of Fourier ptychography to generate wide-field high-resolution quantitative phase images of erythrocytes infected with malaria parasites, from a whole blood sample. We are able to image thousands of erythrocytes (red blood cells) in a single field of view and make a determination of infection status of the quantitative phase image of each segmented cell based on machine learning (random forest) and deep learning (VGG16) models. Our random forest model makes use of morphology and texture based features of the quantitative phase images. In order to label the quantitative images of the cells as either infected or uninfected before training the models, we make use of a Plasmodium berghei strain expressing GFP (green fluorescent protein) in all life cycle stages. By overlaying the fluorescence image with the quantitative phase image we could identify the infected subpopulation of erythrocytes for labelling purposes. Our machine learning model (random forest) achieved 91% specificity and 72% sensitivity while our deep learning model (VGG16) achieved 98% specificity and 57% sensitivity. These results highlight the potential for quantitative phase imaging coupled with artificial intelligence to develop an easy to use platform for the rapid and sensitive diagnosis of malaria.
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Affiliation(s)
- Osman Akcakır
- Beykoz Institute of Life Sciences and Biotechnology (BILSAB), Bezmialem Vakif University, 34820 Istanbul, Turkey
| | - Lutfi Kadir Celebi
- Beykoz Institute of Life Sciences and Biotechnology (BILSAB), Bezmialem Vakif University, 34820 Istanbul, Turkey
- Istanbul Technical University (ITU), Electronics and Communication Engineering Department, Biomedical Engineering Program, 34467 Istanbul, Turkey
| | - Mohd Kamil
- Beykoz Institute of Life Sciences and Biotechnology (BILSAB), Bezmialem Vakif University, 34820 Istanbul, Turkey
| | - Ahmed S. I. Aly
- Beykoz Institute of Life Sciences and Biotechnology (BILSAB), Bezmialem Vakif University, 34820 Istanbul, Turkey
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11
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Khan SH, Shah NS, Nuzhat R, Majid A, Alquhayz H, Khan A. Malaria Parasite Classification Framework using a Novel Channel Squeezed and Boosted CNN. Microscopy (Oxf) 2022; 71:271-282. [PMID: 35640304 DOI: 10.1093/jmicro/dfac027] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/21/2022] [Accepted: 05/30/2022] [Indexed: 11/14/2022] Open
Abstract
Malaria is a life-threatening infection that infects the red blood cells (RBCs) that gradually grows throughout the body. The plasmodium parasite is caused by a female anopheles mosquito bite and severely affects numerous individuals within the world every year. Therefore, early detection tests are required to predict infected parasitic cells. The proposed technique exploits deep convolutional neural network (CNN) learning capability to detect the thin-blood smear parasitic patients from healthy individuals. In this regard, the detection is accomplished using a novel STM-SB-RENet block-based CNN that employs the idea of split-transform-merge (STM) and channel Squeezing-Boosting (SB) in a modified fashion. In this connection, a new convolutional block-based STM is developed, which systematically implements region and edge operations to explore the parasitic malaria pattern related to region-homogeneity, structural obstruction, and boundary-defining features. Moreover, the diverse boosted feature maps are achieved by incorporating the new channel SB and Transfer Learning (TL) idea in each STM block at abstract, intermediate, and target levels to capture minor contrast and texture variation between parasitic and normal artifacts. The malaria input images to the proposed models are initially transformed using discrete wavelet transform to generate enhanced and reduced feature space. The proposed architectures are validated using hold-out cross-validation on the National Institute of Health Malaria dataset. The proposed methods outperform the train from scratch, and TL-based fine-tuned existing techniques. The considerable performance (accuracy: 97.98%, sensitivity: 0.988, F-score: 0.980, and AUC: 0.996) of STM-SB-RENet suggests that it can be utilized to screen parasitic malaria patients.
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Affiliation(s)
- Saddam Hussain Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan.,PIEAS Artificial Intelligence Center (PAIC), PIEAS, Nilore, Islamabad 45650, Pakistan.,Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat 19060, Pakistan
| | - Najmus Saher Shah
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan.,PIEAS Artificial Intelligence Center (PAIC), PIEAS, Nilore, Islamabad 45650, Pakistan
| | - Rabia Nuzhat
- Department of Software Engineering, University of Engineering and Technology (UET), Taxila 47080, Pakistan
| | - Abdul Majid
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan
| | - Hani Alquhayz
- Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
| | - Asifullah Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Nilore, Islamabad 45650, Pakistan.,PIEAS Artificial Intelligence Center (PAIC), PIEAS, Nilore, Islamabad 45650, Pakistan.,Center for Mathematical Sciences, PIEAS, Nilore, Islamabad 45650, Pakistan
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12
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Kim J, Go T, Lee SJ. Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning. JOURNAL OF HAZARDOUS MATERIALS 2021; 418:126351. [PMID: 34329034 DOI: 10.1016/j.jhazmat.2021.126351] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 05/21/2021] [Accepted: 06/04/2021] [Indexed: 06/13/2023]
Abstract
Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome to handle for accurate and high throughput measurements. In addition, commercial particle counters have technical limitations in high-concentration measurement, and data fluctuations are induced during air sampling. In this study, a novel smartphone-based technique for monitoring airborne PM concentrations was developed using smartphone-based digital holographic microscopy (S-DHM) and deep learning network called Holo-SpeckleNet. Holographic speckle images of various PM concentrations were recorded by the S-DHM system. The recorded speckle images and the corresponding ground truth PM concentrations were used to train deep learning algorithms consisting of a deep autoencoder and regression layers. The performance of the proposed smartphone-based PM monitoring technique was validated through hyperparameter optimization. The developed S-DHM integrated with Holo-SpeckleNet can be smartly and effectively utilized for portable PM monitoring and safety alarm provision under perilous environmental conditions.
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Affiliation(s)
- Jihwan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
| | - Taesik Go
- Division of Biomedical Engineering, College of Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Republic of Korea
| | - Sang Joon Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.
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13
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Chattopadhyay AK, Chattopadhyay S. VIRDOCD
: A
VIRtual DOCtor
to predict dengue fatality. EXPERT SYSTEMS 2021. [DOI: 10.1111/exsy.12796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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14
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Kaur I, Behl T, Aleya L, Rahman H, Kumar A, Arora S, Bulbul IJ. Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:40515-40532. [PMID: 34036497 PMCID: PMC8148397 DOI: 10.1007/s11356-021-13823-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 04/05/2021] [Indexed: 04/15/2023]
Abstract
The world has never been prepared for global pandemics like the COVID-19, currently posing an immense threat to the public and consistent pressure on the global healthcare systems to navigate optimized tools, equipments, medicines, and techno-driven approaches to retard the infection spread. The synergized outcome of artificial intelligence paradigms and human-driven control measures elicit a significant impact on screening, analysis, prediction, and tracking the currently infected individuals, and likely the future patients, with precision and accuracy, generating regular international and national data on confirmed, recovered, and death cases, as the current status of 3,820,869 infected patients worldwide. Artificial intelligence is a frontline concept, with time-saving, cost-effective, and productive access to disease management, rendering positive results in physician assistance in high workload conditions, radiology imaging, computational tomography, and database formulations, to facilitate availability of information accessible to researchers all over the globe. The review tends to elaborate the role of industry 4.0 technology, fast diagnostic procedures, and convolutional neural networks, as artificial intelligence aspects, in potentiating the COVID-19 management criteria and differentiating infection in SARS-CoV-2 positive and negative groups. Therefore, the review successfully supplements the processes of vaccine development, disease management, diagnosis, patient records, transmission inhibition, social distancing, and future pandemic predictions, with artificial intelligence revolution and smart techno processes to ensure that the human race wins this battle with COVID-19 and many more combats in the future.
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Affiliation(s)
- Ishnoor Kaur
- Chitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, India
| | - Tapan Behl
- Chitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, India.
| | - Lotfi Aleya
- Chrono-Environment Laboratory, UMR CNRS 6249, Bourgogne Franche-Comté University, Besançon, France
| | - Habibur Rahman
- Department of Global Medical Science, Wonju College of Medicine, Yonsei University, Seoul, South Korea
- Department of Pharmacy, Southeast University, Banani, Dhaka, 1213, Bangladesh
| | - Arun Kumar
- Chitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, India
| | - Sandeep Arora
- Chitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, India
| | - Israt Jahan Bulbul
- Department of Pharmacy, Southeast University, Banani, Dhaka, 1213, Bangladesh
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15
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Go T, Kim J, Lee SJ. Three-dimensional volumetric monitoring of settling particulate matters on a leaf using digital in-line holographic microscopy. JOURNAL OF HAZARDOUS MATERIALS 2021; 404:124116. [PMID: 33049638 DOI: 10.1016/j.jhazmat.2020.124116] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 09/17/2020] [Accepted: 09/24/2020] [Indexed: 06/11/2023]
Abstract
Plants are considered as a possible modality to reduce particulate matter (PM) particles from ambient air in an ecofriendly manner. A new precise monitoring technique that can explore interactions between individual PM particles and a leaf surface is necessary to understand the underlying mechanisms of PM removal of plant leaves. In this study, a digital in-line holographic microscopy (DIHM) was employed to experimentally investigate the settling motions of PM particles over the leaf surface. The in-plane positions and sizes of opaque PMs with irregular shapes were obtained from the projection images of numerically reconstructed holographic images. The depth positions of PMs were determined by using proper selection of an autofocusing criterion with automatic segmentation method. The edge of a hairy Perilla frutescens leaf was detected by adopting several digital imaging processing techniques. The DIHM technique was applied in this study to accurately detect 3D settling trajectories of PMs with velocity information of PMs in the midair and near leaf surface, simultaneously.
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Affiliation(s)
- Taesik Go
- Division of Biomedical Engineering, College of Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Republic of Korea
| | - Jihwan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
| | - Sang Joon Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.
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16
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Mejía Morales J, Hammarström B, Lippi GL, Vassalli M, Glynne-Jones P. Acoustofluidic phase microscopy in a tilted segmentation-free configuration. BIOMICROFLUIDICS 2021; 15:014102. [PMID: 33456640 PMCID: PMC7787693 DOI: 10.1063/5.0036585] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/17/2020] [Indexed: 06/12/2023]
Abstract
A low-cost device for registration-free quantitative phase microscopy (QPM) based on the transport of intensity equation of cells in continuous flow is presented. The method uses acoustic focusing to align cells into a single plane where all cells move at a constant speed. The acoustic focusing plane is tilted with respect to the microscope's focal plane in order to obtain cell images at multiple focal positions. As the cells are displaced at constant speed, phase maps can be generated without the need to segment and register individual objects. The proposed inclined geometry allows for the acquisition of a vertical stack without the need for any moving part, and it enables a cost-effective and robust implementation of QPM. The suitability of the solution for biological imaging is tested on blood samples, demonstrating the ability to recover the phase map of single red blood cells flowing through the microchip.
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Affiliation(s)
| | | | - Gian Luca Lippi
- Institut de Physique de Nice, Université Côte d’Azur, CNRS, 06560 Valbonne, France
| | - Massimo Vassalli
- James Watt School of Engineering, University of Glasgow, G12 8LT Glasgow, United Kingdom
| | - Peter Glynne-Jones
- Engineering Sciences, University of Southampton, SO17 1BJ Southampton, United Kingdom
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17
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Malik YS, Sircar S, Bhat S, Ansari MI, Pande T, Kumar P, Mathapati B, Balasubramanian G, Kaushik R, Natesan S, Ezzikouri S, El Zowalaty ME, Dhama K. How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future. Rev Med Virol 2020; 31:1-11. [PMID: 33476063 PMCID: PMC7883226 DOI: 10.1002/rmv.2205] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/27/2020] [Accepted: 11/29/2020] [Indexed: 12/16/2022]
Abstract
The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid‐19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI‐based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid‐19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI‐based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid‐19 cases. AI‐based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid‐19 pandemic.
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Affiliation(s)
- Yashpal Singh Malik
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India.,College of Animal Biotechnology, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, India
| | - Shubhankar Sircar
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
| | - Sudipta Bhat
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
| | - Mohd Ikram Ansari
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
| | - Tripti Pande
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
| | - Prashant Kumar
- Amity Institute of Virology and Immunology, Amity University, Noida, Uttar Pradesh, India
| | - Basavaraj Mathapati
- Polio Virus Group, Microbial Containment Complex, I.C.M.R. National Institute of Virology, Pune, Maharashtra, India
| | - Ganesh Balasubramanian
- Laboratory Division, Indian Council of Medical Research -National Institute of Epidemiology, Ministry of Health & Family Welfare, Chennai, Tamil Nadu, India
| | - Rahul Kaushik
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa, Japan
| | | | - Sayeh Ezzikouri
- Viral Hepatitis Laboratory, Virology Unit, Institut Pasteur du Maroc, Casablanca, Morocco
| | - Mohamed E El Zowalaty
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah, UAE.,Zoonosis Science Center, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
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18
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Miccio L, Memmolo P, Merola F, Mugnano M, Ferraro P. Optobiology: live cells in optics and photonics. JPHYS PHOTONICS 2020. [DOI: 10.1088/2515-7647/abac19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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19
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De Bruyne S, Speeckaert MM, Van Biesen W, Delanghe JR. Recent evolutions of machine learning applications in clinical laboratory medicine. Crit Rev Clin Lab Sci 2020; 58:131-152. [PMID: 33045173 DOI: 10.1080/10408363.2020.1828811] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.
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Affiliation(s)
- Sander De Bruyne
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | | | - Wim Van Biesen
- Department of Nephrology, Ghent University Hospital, Ghent, Belgium
| | - Joris R Delanghe
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
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20
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Deep learning-based hologram generation using a white light source. Sci Rep 2020; 10:8977. [PMID: 32488035 PMCID: PMC7265409 DOI: 10.1038/s41598-020-65716-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/04/2020] [Indexed: 01/10/2023] Open
Abstract
Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3-5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.
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21
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Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet 2020; 395:1579-1586. [PMID: 32416782 PMCID: PMC7255280 DOI: 10.1016/s0140-6736(20)30226-9] [Citation(s) in RCA: 206] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 01/21/2020] [Accepted: 01/22/2020] [Indexed: 02/07/2023]
Abstract
Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.
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Affiliation(s)
- Nina Schwalbe
- Heilbrunn Department of Population and Family Health, Columbia Mailman School of Public Health, New York, NY, USA; Spark Street Advisors, New York, NY, USA.
| | - Brian Wahl
- Spark Street Advisors, New York, NY, USA; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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22
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Belashov AV, Zhikhoreva AA, Belyaeva TN, Kornilova ES, Salova AV, Semenova IV, Vasyutinskii OS. In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2020; 37:346-352. [PMID: 32118916 DOI: 10.1364/josaa.382135] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/03/2020] [Indexed: 06/10/2023]
Abstract
Digital holographic microscopy supplemented with the developed cell segmentation and machine learning and classification algorithms is implemented for quantitative description of the dynamics of cellular necrosis induced by photodynamic treatment in vitro. It is demonstrated that the developed algorithms operating with a set of optical, morphological, and physiological parameters of cells, obtained from their phase images, can be used for automatic distinction between live and necrotic cells. The developed classifier provides high accuracy of about 95.5% and allows for calculation of survival rates in the course of cell death.
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23
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Abstract
Infectious diseases are caused by microorganisms belonging to the class of bacteria, viruses, fungi, or parasites. These pathogens are transmitted, directly or indirectly, and can lead to epidemics or even pandemics. The resulting infection may lead to mild-to-severe symptoms such as life-threatening fever or diarrhea. Infectious diseases may be asymptomatic in some individuals but may lead to disastrous effects in others. Despite the advances in medicine, infectious diseases are a leading cause of death worldwide, especially in low-income countries. With the advent of mathematical tools, scientists are now able to better predict epidemics, understand the specificity of each pathogen, and identify potential targets for drug development. Artificial intelligence and its components have been widely publicized for their ability to better diagnose certain types of cancer from imaging data. This chapter aims at identifying potential applications of machine learning in the field of infectious diseases. We are deliberately focusing on key aspects of infection: diagnosis, transmission, response to treatment, and resistance. We are proposing the use of extreme values as an avenue of interest for future developments in the field of infectious diseases. This chapter covers a series of applications selectively chosen to showcase how artificial intelligence is moving the field of infectious disease further and how it helps institutions to better tackles them, especially in low-income countries.
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Affiliation(s)
- Said Agrebi
- Yobitrust, Technopark El Gazala, Ariana, Tunisia
| | - Anis Larbi
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore,Department of Microbiology & Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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24
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Rubin M, Stein O, Turko NA, Nygate Y, Roitshtain D, Karako L, Barnea I, Giryes R, Shaked NT. TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set. Med Image Anal 2019; 57:176-185. [DOI: 10.1016/j.media.2019.06.014] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 05/18/2019] [Accepted: 06/25/2019] [Indexed: 01/01/2023]
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25
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Go T, Yoon GY, Lee SJ. Learning-based automatic sensing and size classification of microparticles using smartphone holographic microscopy. Analyst 2019; 144:1751-1760. [DOI: 10.1039/c8an02157k] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A microparticle classifier is established by synergetic integration of smartphone-based digital in-line holographic microscopy and supervised machine learning.
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Affiliation(s)
- Taesik Go
- Department of Mechanical Engineering
- Pohang University of Science and Technology
- Pohang
- Republic of Korea
| | - Gun Young Yoon
- Department of Mechanical Engineering
- Pohang University of Science and Technology
- Pohang
- Republic of Korea
| | - Sang Joon Lee
- Department of Mechanical Engineering
- Pohang University of Science and Technology
- Pohang
- Republic of Korea
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26
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Kim G, Jo Y, Cho H, Min HS, Park Y. Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells. Biosens Bioelectron 2019; 123:69-76. [DOI: 10.1016/j.bios.2018.09.068] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/18/2018] [Accepted: 09/19/2018] [Indexed: 10/28/2022]
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Ugele M, Weniger M, Stanzel M, Bassler M, Krause SW, Friedrich O, Hayden O, Richter L. Label-Free High-Throughput Leukemia Detection by Holographic Microscopy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2018; 5:1800761. [PMID: 30581697 PMCID: PMC6299719 DOI: 10.1002/advs.201800761] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 07/18/2018] [Indexed: 05/12/2023]
Abstract
Complete blood count and differentiation of leukocytes (DIFF) belong to the most frequently performed laboratory diagnostic tests. Here, a flow cytometry-based method for label-free DIFF of untouched leukocytes by digital holographic microscopy on the rich phase contrast of peripheral leukocyte images, using highly controlled 2D hydrodynamic focusing conditions is reported. Principal component analysis of morphological characteristics of the reconstructed images allows classification of nine leukocyte types, in addition to different types of leukemia and demonstrates disappearance of acute myeloid leukemia cells in remission. To exclude confounding effects, the classification strategy is tested by the analysis of 20 blinded clinical samples. Here, 70% of the specimens are correctly classified with further 20% classifications close to a correct diagnosis. Taken together, the findings indicate a broad clinical applicability of the cytometry method for automated and reagent-free diagnosis of hematological disorders.
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Affiliation(s)
- Matthias Ugele
- In‐Vitro DX and BioscienceDepartment of Strategy and InnovationSiemens Healthcare GmbHGünther‐Scharowsky‐Str. 191058ErlangenGermany
- Department of Chemical and Biological EngineeringInstitute of Medical BiotechnologyFriedrich‐Alexander‐University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
| | - Markus Weniger
- In‐Vitro DX and BioscienceDepartment of Strategy and InnovationSiemens Healthcare GmbHGünther‐Scharowsky‐Str. 191058ErlangenGermany
| | - Manfred Stanzel
- In‐Vitro DX and BioscienceDepartment of Strategy and InnovationSiemens Healthcare GmbHGünther‐Scharowsky‐Str. 191058ErlangenGermany
| | - Michael Bassler
- Analysesysteme und SensorikFraunhofer IMMCarl‐Zeiss‐Str. 18‐2055129MainzGermany
| | - Stefan W. Krause
- Medizinische Klinik 5Hämatologie and Internistische OnkologieUlmenweg 1891054ErlangenGermany
| | - Oliver Friedrich
- Department of Chemical and Biological EngineeringInstitute of Medical BiotechnologyFriedrich‐Alexander‐University Erlangen‐NurembergPaul‐Gordan‐Str. 391052ErlangenGermany
| | - Oliver Hayden
- In‐Vitro DX and BioscienceDepartment of Strategy and InnovationSiemens Healthcare GmbHGünther‐Scharowsky‐Str. 191058ErlangenGermany
- Heinz‐Nixdorf‐Chair of Biomedical ElectronicsDepartment of Electrical and Computer EngineeringTranslaTUMCampus Klinikum rechts der IsarTechnical University of MunichIsmaningerstr. 2281675MunichGermany
| | - Lukas Richter
- In‐Vitro DX and BioscienceDepartment of Strategy and InnovationSiemens Healthcare GmbHGünther‐Scharowsky‐Str. 191058ErlangenGermany
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