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Çubukçu HC, Topcu Dİ, Yenice S. Machine learning-based clinical decision support using laboratory data. Clin Chem Lab Med 2024; 62:793-823. [PMID: 38015744 DOI: 10.1515/cclm-2023-1037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 11/17/2023] [Indexed: 11/30/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.
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Affiliation(s)
- Hikmet Can Çubukçu
- General Directorate of Health Services, Rare Diseases Department, Turkish Ministry of Health, Ankara, Türkiye
- Hacettepe University Institute of Informatics, Ankara, Türkiye
| | - Deniz İlhan Topcu
- Health Sciences University İzmir Tepecik Education and Research Hospital, Medical Biochemistry, İzmir, Türkiye
| | - Sedef Yenice
- Florence Nightingale Hospital, Istanbul, Türkiye
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2
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Faurø KK, Antonsen S, Nybo M. Critical test result management at Danish hospital laboratories: a national survey. Scand J Clin Lab Invest 2024:1-9. [PMID: 38648389 DOI: 10.1080/00365513.2024.2343906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
Critical test results in clinical laboratories are crucial for timely patient care, serving as indicators of potentially life-threatening conditions. Despite their importance, a notable heterogeneity in management practices exists globally. This study investigates the current practices of managing critical results at Danish clinical biochemistry laboratories and identifies areas prone for improvement. A comprehensive online survey was distributed to all 21 Danish clinical biochemistry laboratories regarding their critical result management, including documentation practices, critical limit selection, and quality assurance measures. A total of 17 laboratories (81%) responded. The answers revealed a generally uniform approach to managing critical results, with all laboratories having 24-h reporting, local instructions and using the telephone as communication channel. However, variations were noted in documentation practices and critical limit selection. Notably, 23.5% of the laboratories reported that one out of every ten critical results was not reported, indicating a significant risk of delayed critical results. This is further complicated by the limited use of predefined timeframes for reporting and also, only few laboratories actively monitored response times. The findings emphasize the need for more standardized documentation and evaluation practices to align with international standards and to enhance patient safety. While the laboratories showed a commitment to standardized procedures, the study emphasizes the necessity of a National or Nordic guideline to supplement the ISO 15189:2022. This study is a step towards optimizing critical result management, not only in Danish clinical biochemistry laboratories but also across various laboratory specialties, thereby improving overall laboratory quality, efficiency, and patient safety.
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Affiliation(s)
- Kaja Kastberg Faurø
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark
| | - Steen Antonsen
- Department of Clinical Biochemistry, Odense University Hospital Svendborg, Svendborg, Denmark
| | - Mads Nybo
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
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3
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Cheng FM, Lo SC, Lin CC, Lo WJ, Chien SY, Sun TH, Hsu KC. Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube. Sci Rep 2024; 14:8350. [PMID: 38594383 PMCID: PMC11004172 DOI: 10.1038/s41598-024-58580-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 04/01/2024] [Indexed: 04/11/2024] Open
Abstract
This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cytometry efficiently identifies various forms of acute leukemia. However, the analysis of flow cytometry can be time-consuming work. This retrospective study included 241 patients who underwent flow cytometry examination using ALOT between 2017 and 2022. The collected flow cytometry data were used to train an artificial intelligence using deep learning. The trained AI demonstrated a 94.6% sensitivity in detecting acute myeloid leukemia (AML) patients and a 98.2% sensitivity for B-lymphoblastic leukemia (B-ALL) patients. The sensitivities of physiological cells were at least 80%, with variable performance for pathological cells. In conclusion, the AI, trained with ResNet-50 and EverFlow, shows promising results in identifying patients with AML and B-ALL, as well as classifying physiological cells.
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Affiliation(s)
- Fu-Ming Cheng
- Division of Hematology and Oncology, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan
| | - Shih-Chang Lo
- Artificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan
| | - Ching-Chan Lin
- Division of Hematology and Oncology, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan
| | - Wen-Jyi Lo
- Division of Hematology and Oncology, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan
| | - Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan.
- School of Medicine, China Medical University, Taichung, 404, Taiwan.
- Neuroscience and Brain Disease Center, China Medical University, Taichung, 404, Taiwan.
- Department of Neurology, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan.
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Lu Z, Morita M, Yeager TS, Lyu Y, Wang SY, Wang Z, Fan G. Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders. Diagnostics (Basel) 2024; 14:420. [PMID: 38396459 PMCID: PMC10888253 DOI: 10.3390/diagnostics14040420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density-phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4-/CD8- double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.
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Affiliation(s)
- Zhengchun Lu
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Mayu Morita
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Tyler S. Yeager
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Yunpeng Lyu
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Sophia Y. Wang
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | | | - Guang Fan
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
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Wang YF, Li JL, Lee CC, Wallace PK, Ko BS. Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring. Methods Mol Biol 2024; 2779:353-367. [PMID: 38526794 DOI: 10.1007/978-1-0716-3738-8_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current analytic software and methods rely on experienced analysts to perform labor-intensive manual inspection and interpretation on a series of 2-dimensional plots via a complex, sequential gating process. With an aggravating shortage of professionals and growing demands, it is very challenging to provide the FC analysis results in a fast, accurate, and reproducible way. Artificial intelligence has been widely used in many sectors to develop automated detection or classification tools. Here we describe a type of machine learning method for developing automated disease classification and residual disease monitoring on clinical flow datasets.
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Affiliation(s)
- Yu-Fen Wang
- AHEAD Medicine Corporation, San Jose, CA, USA.
- AHEAD Intelligence Ltd, Taipei, Taiwan.
| | - Jeng-Lin Li
- Department of Electrical Engineering, National Tsing Hua University, Hsin-Chu, Taiwan
| | - Chi-Chun Lee
- Department of Electrical Engineering, National Tsing Hua University, Hsin-Chu, Taiwan
| | - Paul K Wallace
- Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Bor-Sheng Ko
- AHEAD Medicine Corporation, San Jose, CA, USA
- AHEAD Intelligence Ltd, Taipei, Taiwan
- Department of Hematological Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
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Mushtaq AH, Shafqat A, Salah HT, Hashmi SK, Muhsen IN. Machine learning applications and challenges in graft-versus-host disease: a scoping review. Curr Opin Oncol 2023; 35:594-600. [PMID: 37820094 DOI: 10.1097/cco.0000000000000996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
PURPOSE OF REVIEW This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. RECENT FINDINGS Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, "snapshot" assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. SUMMARY To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.
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Affiliation(s)
- Ali Hassan Mushtaq
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Areez Shafqat
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Haneen T Salah
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, Texas
| | - Shahrukh K Hashmi
- Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Department of Medicine, Sheikh Shakbout Medical City
- Medical Affairs, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Ibrahim N Muhsen
- Section of Hematology and Oncology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
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Robles EE, Jin Y, Smyth P, Scheuermann RH, Bui JD, Wang HY, Oak J, Qian Y. A cell-level discriminative neural network model for diagnosis of blood cancers. Bioinformatics 2023; 39:btad585. [PMID: 37756695 PMCID: PMC10563151 DOI: 10.1093/bioinformatics/btad585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/12/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
MOTIVATION Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. RESULTS We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes sample-level training data and predicts the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. AVAILABILITY AND IMPLEMENTATION The source code of CSNN and datasets used in the experiments are publicly available on GitHub (http://github.com/erobl/csnn). Raw FCS files can be downloaded from FlowRepository (ID: FR-FCM-Z6YK).
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Affiliation(s)
- Edgar E Robles
- Department of Computer Science, University of California, Irvine, CA 92697, United States
| | - Ye Jin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Padhraic Smyth
- Department of Computer Science, University of California, Irvine, CA 92697, United States
| | - Richard H Scheuermann
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, United States
- Department of Pathology, University of California, San Diego, CA 92093, United States
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, United States
| | - Jack D Bui
- Department of Pathology, University of California, San Diego, CA 92093, United States
| | - Huan-You Wang
- Department of Pathology, University of California, San Diego, CA 92093, United States
| | - Jean Oak
- Department of Pathology, Stanford University, Stanford, CA 94305, United States
| | - Yu Qian
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, United States
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Seifert RP, Gorlin DA, Borkowski AA. Artificial Intelligence for Clinical Flow Cytometry. Clin Lab Med 2023; 43:485-505. [PMID: 37481325 DOI: 10.1016/j.cll.2023.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
In this review, the authors discuss the fundamental principles of machine learning. They explore recent studies and approaches in implementing machine learning into flow cytometry workflows. These applications are promising but not without their shortcomings. Explainability may be the biggest barrier to adoption, as they contain "black boxes" in which a complex network of mathematical processes learns features of data that are not translatable into real language. The authors discuss the current limitations of machine learning models and the possibility that, without a multiinstitutional development process, these applications could have poor generalizability. They also discuss widespread deployment of augmented decision-making.
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Affiliation(s)
- Robert P Seifert
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, College of Medicine, 4800 Southwest 35th Drive, Gainesville, FL 32608, USA.
| | - David A Gorlin
- University of Florida, College of Medicine, 1600 Southwest Archer Road, Gainesville, FL 32610, USA
| | - Andrew A Borkowski
- National Artificial Intelligence Institute, Washington, DC, USA; Artificial Intelligence Service, James A. Haley Veterans' Hospital, 13000 Bruce B Downs Boulevard, Tampa, FL 33647, USA; University of South Florida Morsani School of Medicine, Tampa, FL, USA
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9
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Robles EE, Jin Y, Smyth P, Scheuermann RH, Bui JD, Wang HY, Oak J, Qian Y. A cell-level discriminative neural network model for diagnosis of blood cancers. medRxiv 2023:2023.02.07.23285606. [PMID: 36798344 PMCID: PMC9934808 DOI: 10.1101/2023.02.07.23285606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Motivation Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. Results We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes the available sample-level training data and predicts both the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. Availability The source code of CSNN and datasets used in the experiments are publicly available on GitHub and FlowRepository. Contact Edgar E. Robles: roblesee@uci.edu and Yu Qian: mqian@jcvi.org. Supplementary information Supplementary data are available on GitHub and at Bioinformatics online.
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10
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Obstfeld AE. Hematology and Machine Learning. J Appl Lab Med 2023; 8:129-144. [PMID: 36610431 DOI: 10.1093/jalm/jfac108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/18/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Substantial improvements in computational power and machine learning (ML) algorithm development have vastly increased the limits of what autonomous machines are capable of. Since its beginnings in the 19th century, laboratory hematology has absorbed waves of progress yielding improvements in both of accuracy and efficiency. The next wave of change in laboratory hematology will be the result of the ML revolution that has already touched many corners of healthcare and society at large. CONTENT This review will describe the manifestations of ML and artificial intelligence (AI) already utilized in the clinical hematology laboratory. This will be followed by a topical summary of the innovative and investigational applications of this technology in each of the major subdomains within laboratory hematology. SUMMARY Application of this technology to laboratory hematology will increase standardization and efficiency by reducing laboratory staff involvement in automatable activities. This will unleash time and resources for focus on more meaningful activities such as the complexities of patient care, research and development, and process improvement.
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Affiliation(s)
- Amrom E Obstfeld
- Department of Pathology & Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA.,Department of Pathology & Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
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Yang Y, Zhang Y, Li Y. Artificial intelligence applications in pediatric oncology diagnosis. Explor Target Antitumor Ther 2023; 4:157-169. [PMID: 36937318 PMCID: PMC10017189 DOI: 10.37349/etat.2023.00127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/30/2022] [Indexed: 03/04/2023] Open
Abstract
Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.
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Affiliation(s)
- Yuhan Yang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yimao Zhang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yuan Li
- Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
- Correspondence: Yuan Li, Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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12
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Li JL, Lin YC, Wang YF, Monaghan SA, Ko BS, Lee CC. A Chunking-for-Pooling Strategy for Cytometric Representation Learning for Automatic Hematologic Malignancy Classification. IEEE J Biomed Health Inform 2022; 26:4773-4784. [PMID: 35588419 DOI: 10.1109/jbhi.2022.3175514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of antibody-bound cells, but the manual interpretation of large scale flow cytometry data has long been a time-consuming and complicated task for hematologists and laboratory professionals. Past studies have led to the development of representation learning algorithms to perform sample-level automatic classification. In this work, we propose a chunking-for-pooling strategy to include large-scale FC data into a supervised deep representation learning procedure for automatic hematologic malignancy classification. The use of discriminatively-trained representation learning strategy and the fixed-size chunking and pooling design are key components of this framework. It improves the discriminative power of the FC sample-level embedding and simultaneously addresses the robustness issue due to an inevitable use of down-sampling in conventional distribution based approaches for deriving FC representation. We evaluated our framework on two datasets. Our framework outperformed other baseline methods and achieved 92.3% unweighted average recall (UAR) for four-class recognition on the UPMC dataset and 85.0% UAR for five-class recognition on the hema.to dataset. We further compared the robustness of our proposed framework with that of the traditional downsampling approach. Analysis of the effects of the chunk size and the error cases revealed further insights about different hematologic malignancy characteristics in the FC data.
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