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Sung M, Kim JH, Min HS, Jang S, Hong J, Choi BK, Shin J, Chung KS, Park YR. Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker. Light Sci Appl 2023; 12:265. [PMID: 37932249 PMCID: PMC10628166 DOI: 10.1038/s41377-023-01309-w] [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] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/20/2023] [Accepted: 10/13/2023] [Indexed: 11/08/2023]
Abstract
Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate. The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management. We aimed to investigate the potential of three-dimensional label-free CD8 + T cell morphology as a biomarker for sepsis. This study included three-time points in the sepsis recovery cohort (N = 8) and healthy controls (N = 20). Morphological features and spatial distribution within cells were compared among the patients' statuses. We developed a deep learning model to predict the diagnosis and prognosis of sepsis using the internal cell morphology. Correlation between the morphological features and clinical indices were analysed. Cell morphological features and spatial distribution differed significantly between patients with sepsis and healthy controls and between the survival and non-survival groups. The model for predicting the diagnosis and prognosis of sepsis showed an area under the receiver operating characteristic curve of nearly 100% with only a few cells, and a strong correlation between the morphological features and clinical indices was observed. Our study highlights the potential of three-dimensional label-free CD8 + T cell morphology as a promising biomarker for sepsis. This approach is rapid, requires a minimum amount of blood samples, and has the potential to provide valuable information for the early diagnosis and prognosis of sepsis.
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Affiliation(s)
- MinDong Sung
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Hyun Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyun-Seok Min
- Tomocube, Inc, 155 Sinseong-ro, Shinsung-dong, Yuseong-gu, Daejeon, Republic of Korea
| | - Sooyoung Jang
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - JaeSeong Hong
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Bo Kyu Choi
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - JuHye Shin
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyung Soo Chung
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, 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] [What about the content of this article? (0)] [Affiliation(s)] [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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3
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Shin TY, Han H, Min HS, Cho H, Kim S, Park SY, Kim HJ, Kim JH, Lee YS. Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy. Medicina (Kaunas) 2023; 59:1402. [PMID: 37629692 PMCID: PMC10456500 DOI: 10.3390/medicina59081402] [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] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/20/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023]
Abstract
Background and Objectives: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoperative renal function. Therefore, we aimed to build an artificial intelligence (AI) model that utilizes perioperative factors to predict residual renal function and incidence of AKI following PN. Methods and Materials: This retrospective study included 785 patients (training set 706, test set 79) from six tertiary referral centers who underwent open or robotic PN. Forty-four perioperative features were used as inputs to train the AI prediction model. XG-Boost and genetic algorithms were used for the final model selection and to determine feature importance. The primary outcome measure was immediate postoperative serum creatinine (Cr) level. The secondary outcome was the incidence of AKI (estimated glomerular filtration rate (eGFR) < 60 mL/h). The average difference between the true and predicted serum Cr levels was considered the mean absolute error (MAE) and was used as a model evaluation parameter. Results: An AI model for predicting immediate postoperative serum Cr levels was selected from 2000 candidates by providing the lowest MAE (0.03 mg/dL). The model-predicted immediate postoperative serum Cr levels correlated closely with the measured values (R2 = 0.9669). The sensitivity and specificity of the model for predicting AKI were 85.5% and 99.7% in the training set, and 100.0% and 100.0% in the test set, respectively. The limitations of this study included its retrospective design. Conclusions: Our AI model successfully predicted accurate serum Cr levels and the likelihood of AKI. The accuracy of our model suggests that personalized guidelines to optimize multidisciplinary plans involving pre- and postoperative care need to be developed.
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Affiliation(s)
- Tae Young Shin
- Synergy A.I. Co., Ltd., Seoul 07985, Republic of Korea;
- Department of Urology, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea;
- Department of Urology, College of Medicine, Ewha Womans University, Seoul 07985, Republic of Korea
| | - Hyunho Han
- Department of Urology, College of Medicine, Yonsei University, Seoul 03722, Republic of Korea;
| | - Hyun-Seok Min
- Tomocube, Inc., Daejeon 34109, Republic of Korea; (H.-S.M.); (H.C.)
| | - Hyungjoo Cho
- Tomocube, Inc., Daejeon 34109, Republic of Korea; (H.-S.M.); (H.C.)
| | - Seonggyun Kim
- Department of Urology, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea;
| | - Sung Yul Park
- Department of Urology, College of Medicine, Hanyang University, Seoul 04763, Republic of Korea;
| | - Hyung Joon Kim
- Department of Urology, College of Medicine, Konyang University, Daejeon 35365, Republic of Korea;
| | - Jung Hoon Kim
- Department of Urology, Chung-Ang University Gwangmyeong Hospital, College of Medicine, Chung-Ang University, Gwangmyeong 14353, Republic of Korea;
| | - Yong Seong Lee
- Department of Urology, Chung-Ang University Gwangmyeong Hospital, College of Medicine, Chung-Ang University, Gwangmyeong 14353, Republic of Korea;
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Lee YK, Ryu D, Kim S, Park J, Park SY, Ryu D, Lee H, Lim S, Min HS, Park Y, Lee EK. Machine-learning-based diagnosis of thyroid fine-needle aspiration biopsy synergistically by Papanicolaou staining and refractive index distribution. Sci Rep 2023; 13:9847. [PMID: 37330568 PMCID: PMC10276805 DOI: 10.1038/s41598-023-36951-2] [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: 02/06/2023] [Accepted: 06/13/2023] [Indexed: 06/19/2023] Open
Abstract
We developed a machine learning algorithm (MLA) that can classify human thyroid cell clusters by exploiting both Papanicolaou staining and intrinsic refractive index (RI) as correlative imaging contrasts and evaluated the effects of this combination on diagnostic performance. Thyroid fine-needle aspiration biopsy (FNAB) specimens were analyzed using correlative optical diffraction tomography, which can simultaneously measure both, the color brightfield of Papanicolaou staining and three-dimensional RI distribution. The MLA was designed to classify benign and malignant cell clusters using color images, RI images, or both. We included 1535 thyroid cell clusters (benign: malignancy = 1128:407) from 124 patients. Accuracies of MLA classifiers using color images, RI images, and both were 98.0%, 98.0%, and 100%, respectively. As information for classification, the nucleus size was mainly used in the color image; however, detailed morphological information of the nucleus was also used in the RI image. We demonstrate that the present MLA and correlative FNAB imaging approach has the potential for diagnosing thyroid cancer, and complementary information from color and RI images can improve the performance of the MLA.
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Affiliation(s)
- Young Ki Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Cancer Center, Goyang, 10408, South Korea
| | | | - Seungwoo Kim
- Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea
| | - Juyeon Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, South Korea
| | - Seog Yun Park
- Deparment of Pathology, National Cancer Center, Goyang, 10408, South Korea
| | - Donghun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, South Korea
- Department of Electrical Engineering and Computer Science (EECS), MIT, Cambridge, MA, 02139, USA
| | - Hayoung Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Cancer Center, Goyang, 10408, South Korea
| | - Sungbin Lim
- Department of Statistics, Korea University, Seoul, 02841, South Korea
| | | | - YongKeun Park
- Tomocube Inc., Daejeon, 34051, South Korea.
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, South Korea.
| | - Eun Kyung Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Cancer Center, Goyang, 10408, South Korea.
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Shin JH, Kim YH, Lee MK, Min HS, Cho H, Kim H, Kim YC, Lee YS, Shin TY. Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease. Investig Clin Urol 2023; 64:255-264. [PMID: 37341005 DOI: 10.4111/icu.20220411] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/12/2023] [Accepted: 03/26/2023] [Indexed: 06/22/2023] Open
Abstract
PURPOSE Total kidney volume (TKV) measurement is crucial for selecting treatment candidates in autosomal dominant polycystic kidney disease (ADPKD). We developed and investigated the performance of fully-automated 3D-volumetry model and applied it to software as a service (SaaS) for clinical support on tolvaptan prescription in ADPKD patients. MATERIALS AND METHODS Computed tomography scans of ADPKD patients taken between January 2000 and June 2022 were acquired from seven institutions. The quality of the images was manually reviewed in advance. The acquired dataset was split into training, validation, and test datasets at a ratio of 8.5:1:0.5. Convolutional, neural network-based automatic segmentation model was trained to obtain 3D segment mask for TKV measurement. The algorithm consisted of three steps: data preprocessing, ADPKD area extraction, and post-processing. After performance validation with the Dice score, 3D-volumetry model was applied to SaaS which is based on Mayo imaging classification for ADPKD. RESULTS A total of 753 cases with 95,117 slices were included. The differences between the ground-truth ADPKD kidney mask and the predicted ADPKD kidney mask were negligible, with intersection over union >0.95. The post-process filter successfully removed false alarms. The test-set performance was homogeneously equal and the Dice score of the model was 0.971; after post-processing, it improved to 0.979. The SaaS calculated TKV from uploaded Digital Imaging and Communications in Medicine images and classified patients according to height-adjusted TKV for age. CONCLUSIONS Our artificial intelligence-3D volumetry model exhibited effective, feasible, and non-inferior performance compared with that of human experts and successfully predicted the rapid ADPKD progressor.
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Affiliation(s)
- Jung Hyun Shin
- Department of Urology, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | | | | | | | | | - Hyunsuk Kim
- Department of Nephrology, Hallym University Chuncheon Sacred Hospital, Chuncheon, Korea
| | - Yong Chul Kim
- Department of Nephrology, Seoul National University Hospital, Seoul, Korea
| | - Yong Seong Lee
- Department of Urology, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, Korea.
| | - Tae Young Shin
- Department of Urology, Ewha Womans University Mokdong Hospital, Seoul, Korea
- Synergy A.I. Co., Ltd, Seoul, Korea.
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Kim G, Ahn D, Kang M, Park J, Ryu D, Jo Y, Song J, Ryu JS, Choi G, Chung HJ, Kim K, Chung DR, Yoo IY, Huh HJ, Min HS, Lee NY, Park Y. Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network. Light Sci Appl 2022; 11:190. [PMID: 35739098 PMCID: PMC9226356 DOI: 10.1038/s41377-022-00881-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 06/03/2022] [Accepted: 06/09/2022] [Indexed: 05/14/2023]
Abstract
The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.
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Affiliation(s)
- Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
| | - Daewoong Ahn
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Minhee Kang
- Smart Healthcare & Device Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - Jinho Park
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
| | - YoungJu Jo
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
- Tomocube Inc., Daejeon, 34109, Republic of Korea
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | - Jinyeop Song
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jea Sung Ryu
- Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Gunho Choi
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Hyun Jung Chung
- Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Kyuseok Kim
- Department of Emergency Medicine, Bundang CHA Hospital, Seongnam-si, Gyeonggi-Do, 13496, Korea
| | - Doo Ryeon Chung
- Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - In Young Yoo
- Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea
| | - Hee Jae Huh
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | | | - Nam Yong Lee
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea.
- Tomocube Inc., Daejeon, 34109, Republic of Korea.
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Lee YH, Choe YJ, Kim G, Min HS. Age-period-cohort analysis of TB mortality in South Korea, 1983-2017. Int J Tuberc Lung Dis 2022; 26:577-579. [PMID: 35650690 DOI: 10.5588/ijtld.22.0106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Y H Lee
- Korea University Anam Hospital and Korea University College of Medicine, Seoul, South Korea
| | - Y J Choe
- Korea University Anam Hospital and Korea University College of Medicine, Seoul, South Korea
| | - G Kim
- National Medical Center, Seoul, South Korea
| | - H S Min
- National Medical Center, Seoul, South Korea
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8
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Jo Y, Cho H, Park WS, Kim G, Ryu D, Kim YS, Lee M, Park S, Lee MJ, Joo H, Jo H, Lee S, Lee S, Min HS, Heo WD, Park Y. Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning. Nat Cell Biol 2021; 23:1329-1337. [PMID: 34876684 DOI: 10.1038/s41556-021-00802-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 10/25/2021] [Indexed: 02/07/2023]
Abstract
Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is hindered by limits of conventional imaging modalities. Here we propose using the refractive index (RI), an intrinsic quantity governing light-matter interaction, as a means for such measurement. We show that major endogenous subcellular structures, which are conventionally accessed via exogenous fluorescence labelling, are encoded in three-dimensional (3D) RI tomograms. We decode this information in a data-driven manner, with a deep learning-based model that infers multiple 3D fluorescence tomograms from RI measurements of the corresponding subcellular targets, thereby achieving multiplexed microtomography. This approach, called RI2FL for refractive index to fluorescence, inherits the advantages of both high-specificity fluorescence imaging and label-free RI imaging. Importantly, full 3D modelling of absolute and unbiased RI improves generalization, such that the approach is applicable to a broad range of new samples without retraining to facilitate immediate applicability. The performance, reliability and scalability of this technology are extensively characterized, and its various applications within single-cell profiling at unprecedented scales (which can generate new experimentally testable hypotheses) are demonstrated.
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Affiliation(s)
- YoungJu Jo
- 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.,Departments of Applied Physics and of Biology, Stanford University, Stanford, CA, USA
| | | | - Wei Sun 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
| | - 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
| | - 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
| | - Young Seo 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.,Graduate School of Medial Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Moosung 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
| | - Sangwoo Park
- Gwangju Center, Korea Basic Science Institute (KBSI), Gwangju, Republic of Korea
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.,Graduate School of Medial Science and Engineering, KAIST, Daejeon, Republic of Korea
| | | | | | - Seongsoo Lee
- Gwangju Center, Korea Basic Science Institute (KBSI), Gwangju, Republic of Korea
| | - Sumin Lee
- Tomocube, Daejeon, Republic of Korea
| | | | - Won Do Heo
- Department of Biological Sciences, KAIST, Daejeon, Republic of Korea. .,KAIST Institute for the BioCentury, KAIST, Daejeon, Republic of Korea.
| | - 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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9
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Ryu D, Kim J, Lim D, Min HS, Yoo IY, Cho D, Park Y. Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning. BME Front 2021; 2021:9893804. [PMID: 37849908 PMCID: PMC10521749 DOI: 10.34133/2021/9893804] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 06/29/2021] [Indexed: 10/19/2023] Open
Abstract
Objective and Impact Statement. We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index (RI) tomography. Our computational approach that fully utilizes tomographic information of bone marrow (BM) white blood cell (WBC) enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research. Introduction. Conventional methods for examining blood cells, such as blood smear analysis by medical professionals and fluorescence-activated cell sorting, require significant time, costs, and domain knowledge that could affect test results. While label-free imaging techniques that use a specimen's intrinsic contrast (e.g., multiphoton and Raman microscopy) have been used to characterize blood cells, their imaging procedures and instrumentations are relatively time-consuming and complex. Methods. The RI tomograms of the BM WBCs are acquired via Mach-Zehnder interferometer-based tomographic microscope and classified by a 3D convolutional neural network. We test our deep learning classifier for the four types of bone marrow WBC collected from healthy donors (n = 10 ): monocyte, myelocyte, B lymphocyte, and T lymphocyte. The quantitative parameters of WBC are directly obtained from the tomograms. Results. Our results show >99% accuracy for the binary classification of myeloids and lymphoids and >96% accuracy for the four-type classification of B and T lymphocytes, monocyte, and myelocytes. The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique. Conclusion. We envision that the proposed cell classification framework can be easily integrated into existing blood cell investigation workflows, providing cost-effective and rapid diagnosis for hematologic malignancy.
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Affiliation(s)
- DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
| | - Jinho Kim
- Department of Health Sciences and Technology, Samsung Advanced Institute For Health Sciences and Technology, Sungkyunkwan University, Seoul 06355, Republic of Korea
| | - Daejin Lim
- Department of Health and Safety Convergence Science, Korea University, Seoul 02841, Republic of Korea
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | | | - In Young Yoo
- Department of Laboratory Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Duck Cho
- Department of Health Sciences and Technology, Samsung Advanced Institute For Health Sciences and Technology, Sungkyunkwan University, Seoul 06355, Republic of Korea
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
- Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul 06531, Republic of Korea
| | - YongKeun Park
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute For Health Sciences and Technology, Sungkyunkwan University, Seoul 06355, Republic of Korea
- Tomocube, Inc., Daejeon 34051Republic of Korea
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Ryu D, Ryu D, Baek Y, Cho H, Kim G, Kim YS, Lee Y, Kim Y, Ye JC, Min HS, Park Y. DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning. IEEE Trans Med Imaging 2021; 40:1508-1518. [PMID: 33566760 DOI: 10.1109/tmi.2021.3058373] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial resolution due to limited access to the three-dimensional optical transfer function. This missing cone problem has been addressed through regularization algorithms that use a priori information, such as non-negativity and sample smoothness. However, the iterative nature of these algorithms and their parameter dependency make real-time visualization impossible. In this article, we propose and experimentally demonstrate a deep neural network, which we term DeepRegularizer, that rapidly improves the resolution of a three-dimensional refractive index map. Trained with pairs of datasets (a raw refractive index tomogram and a resolution-enhanced refractive index tomogram via the iterative total variation algorithm), the three-dimensional U-net-based convolutional neural network learns a transformation between the two tomogram domains. The feasibility and generalizability of our network are demonstrated using bacterial cells and a human leukaemic cell line, and by validating the model across different samples. DeepRegularizer offers more than an order of magnitude faster regularization performance compared to the conventional iterative method. We envision that the proposed data-driven approach can bypass the high time complexity of various image reconstructions in other imaging modalities.
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11
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Lee M, Lee YH, Song J, Kim G, Jo Y, Min H, Kim CH, Park Y. Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells. eLife 2020; 9:49023. [PMID: 33331817 PMCID: PMC7817186 DOI: 10.7554/elife.49023] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 12/16/2020] [Indexed: 12/14/2022] Open
Abstract
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.
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Affiliation(s)
- Moosung Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Young-Ho Lee
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.,Curocell Inc, Daejeon, Republic of Korea
| | - Jinyeop Song
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - YoungJu Jo
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | | | - Chan Hyuk Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
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12
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Chang T, Ryu D, Jo Y, Choi G, Min HS, Park Y. Calibration-free quantitative phase imaging using data-driven aberration modeling. Opt Express 2020; 28:34835-34847. [PMID: 33182943 DOI: 10.1364/oe.412009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 10/09/2020] [Indexed: 06/11/2023]
Abstract
We present a data-driven approach to compensate for optical aberrations in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field. The high fidelity and stability of our method is demonstrated on 2D and 3D QPI measurements of various confluent eukaryotic cells and microbeads, benchmarking against the conventional method using background subtractions.
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13
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Min HS, Yoo JH, Kang SJ, Lee JG, Cho H, Lee PH, Ahn JM, Park DW, Lee SW, Kim YH, Lee CW, Park SW, Park SJ. Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning. EUROINTERVENTION 2020; 16:404-412. [DOI: 10.4244/eij-d-19-00487] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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14
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Ryu D, Kim K, Cho H, Dao K, Kim YS, Ahn D, Min HS, Shin EC, Park Y. Label-free 3-D quantitative phase imaging cytometry with deep learning: identifying naive, memory, and senescent T cells. The Journal of Immunology 2020. [DOI: 10.4049/jimmunol.204.supp.86.5] [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] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Abstract
The current prevailing methods for identifying immune cell subsets exploit a group of differentiation markers (CDs) targeted by fluorochrome or metal conjugated antibodies. However, such labeling methods, requiring a staining process and specific reagents, prevent rapid and cost-effective identification of immune cell subsets. Therefore we developed a label-free imaging cytometry platform that synergistically used refractive index (RI) tomography and three-dimensional (3-D) deep learning. We constructed and trained a deep learning classifier that learns unique representations from the 3-D RI map of each cell obtained using RI tomography without labeling. In this study, we were able to classify human naïve, memory, and senescent T cells according to the expression of CD4, CD8, CD45RA, CCR7 and CD57 using the label-free classifier within milliseconds, with high precision (>95%) even though the morphological and biochemical characteristics extracted from the RI tomograms of the T cells are almost homogeneous. This cannot be achieved by conventional machine learning approaches that only exploit the set of manually extracted features. Our label-free cell sorting platform will facilitate rapid and cost-effective immunological and biomedical studies by eliminating the laborious labeling process.
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Affiliation(s)
| | - Kyunghwan Kim
- 2Graduate School of Medical Science and Engineering, KAIST, South Korea
| | - Hayeon Cho
- 1Department of Physics, KAIST, South Korea
| | - Khoi Dao
- 1Department of Physics, KAIST, South Korea
| | | | | | | | - Eui-Cheol Shin
- 2Graduate School of Medical Science and Engineering, KAIST, South Korea
| | - YongKeun Park
- 1Department of Physics, KAIST, South Korea
- 3Tomocube, Inc., South Korea
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15
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Cho H, Lee JG, Kang SJ, Kim WJ, Choi SY, Ko J, Min HS, Choi GH, Kang DY, Lee PH, Ahn JM, Park DW, Lee SW, Kim YH, Lee CW, Park SW, Park SJ. Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions. J Am Heart Assoc 2020; 8:e011685. [PMID: 30764731 PMCID: PMC6405668 DOI: 10.1161/jaha.118.011685] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background An angiography-based supervised machine learning ( ML ) algorithm was developed to classify lesions as having fractional flow reserve ≤0.80 versus >0.80. Methods and Results With a 4:1 ratio, 1501 patients with 1501 intermediate lesions were randomized into training versus test sets. Between the ostium and 10 mm distal to the target lesion, a series of angiographic lumen diameter measurements along the centerline was plotted. The 24 computed angiographic features based on the diameter plot and 4 clinical features (age, sex, body surface area, and involve segment) were used for ML by XGBoost. The model was independently trained and tested by 2000 bootstrap iterations. External validation with 79 patients was conducted. Including all 28 features, the ML model with 5-fold cross-validation in the 1204 training samples predicted fractional flow reserve ≤0.80 with overall diagnostic accuracy of 78±4% (averaged area under the curve: 0.84±0.03). The 12 high-ranking features selected by scatter search were involved segment; body surface area; distal lumen diameter; minimal lumen diameter; length of a lumen diameter <2.0 mm, <1.5 mm, and <1.25 mm; mean lumen diameter within the worst segment; sex; diameter stenosis; distal 5-mm reference lumen diameter; and length of diameter stenosis >70%. Using those 12 features, the ML predicted fractional flow reserve ≤0.80 in the test set with sensitivity of 84%, specificity of 80%, and overall accuracy of 82% (area under the curve: 0.87). The averaged diagnostic accuracy in bootstrap replicates was 81±1% (averaged area under the curve: 0.87±0.01). External validation showed accuracy of 85% (area under the curve: 0.87). Conclusions Angiography-based ML showed good diagnostic performance in identifying ischemia-producing lesions and reduced the need for pressure wires.
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Affiliation(s)
- Hyungjoo Cho
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - June-Goo Lee
- 2 Biomedical Engineering Research Center Asan Institute for Life Sciences Seoul Korea
| | - Soo-Jin Kang
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Won-Jang Kim
- 3 Department of Cardiology CHA Bundang Medical Center CHA University Seongnam Korea
| | - So-Yeon Choi
- 4 Department of Cardiology Ajou University Suwon Korea
| | - Jiyuon Ko
- 2 Biomedical Engineering Research Center Asan Institute for Life Sciences Seoul Korea
| | - Hyun-Seok Min
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Gun-Ho Choi
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Do-Yoon Kang
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Pil Hyung Lee
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Jung-Min Ahn
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Duk-Woo Park
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Seung-Whan Lee
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Young-Hak Kim
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Cheol Whan Lee
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Seong-Wook Park
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
| | - Seung-Jung Park
- 1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea
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16
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Bae Y, Kang SJ, Kim G, Lee JG, Min HS, Cho H, Kang DY, Lee PH, Ahn JM, Park DW, Lee SW, Kim YH, Lee CW, Park SW, Park SJ. Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning. Atherosclerosis 2019; 288:168-174. [PMID: 31130215 DOI: 10.1016/j.atherosclerosis.2019.04.228] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 04/16/2019] [Accepted: 04/30/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND AIMS Although grayscale intravascular ultrasound (IVUS) is commonly used for assessing coronary lesion morphology and optimizing stent implantation, detection of vulnerable plaques by IVUS remains challenging. We aimed to develop machine learning (ML) models for predicting optical coherence tomography-derived thin-cap fibroatheromas (OCT-TCFAs). METHODS In 517 patients with angina, 414 and 103 coronary lesions were randomized into training vs. test sets. Each of the IVUS-OCT co-registered frames (including 32,807 for training and 8101 for test) was labeled according to the presence vs. absence of OCT-TCFA. Among 1449 computed IVUS features based on two-dimensional geometry and texture, 17 features were finally selected and used in supervised ML with artificial neural network (ANN), support vector machine (SVM), and naïve Bayes. RESULTS IVUS sections with (vs. without) OCT-TCFA showed a larger plaque burden, and a smaller and eccentric lumen. TCFA-containing sections were characterized by increased ratios of variance, entropy, and kurtosis; reduced ratio of homogeneity within the superficial to the deeper plaque; and decreased smoothness within the fibrous cap. In addition, OCT-TCFA was associated with low ratios of gamma-beta, Nakagami-μ and Nakagami-ω, and a high ratio of Rayleigh-b within the superficial to the deeper region. With a 5-fold cross-validation, the averaged accuracies were 81 ± 5% for ANN (area under the curve [AUC] = 0.80 ± 0.08), 77 ± 4% for SVM (AUC = 0.74 ± 0.05), and 78 ± 2% for naïve Bayes (AUC = 0.77 ± 0.04) for predicting OCT-TCFA. In the test set, ANN and naïve Bayes showed the overall accuracies of >80%. CONCLUSIONS Supervised ML algorithms with computed IVUS features predicted the presence of OCT-TCFA. This data-driven approach may help clinicians in recognizing high-risk coronary lesions.
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Affiliation(s)
- Youngoh Bae
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Soo-Jin Kang
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.
| | - Geena Kim
- College of Computer & Information Sciences, Regis University, Denver, CO, USA
| | - June-Goo Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Seoul, South Korea
| | - Hyun-Seok Min
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Hyungjoo Cho
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Do-Yoon Kang
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Pil Hyung Lee
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Jung-Min Ahn
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Duk-Woo Park
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Seung-Whan Lee
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Young-Hak Kim
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Cheol Whan Lee
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Seong-Wook Park
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Seung-Jung Park
- Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
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17
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Choi G, Ryu D, Jo Y, Kim YS, Park W, Min HS, Park Y. Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography. Opt Express 2019; 27:4927-4943. [PMID: 30876102 DOI: 10.1364/oe.27.004927] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising.
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18
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Ha EJ, Baek JH, Na DG, Kim JH, Kim JK, Min HS, Song DE, Lee KE, Shong YK. The Role of Core Needle Biopsy and Its Impact on Surgical Management in Patients with Medullary Thyroid Cancer: Clinical Experience at 3 Medical Institutions. AJNR Am J Neuroradiol 2015; 36:1512-7. [PMID: 25929882 DOI: 10.3174/ajnr.a4317] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Accepted: 01/04/2015] [Indexed: 01/21/2023]
Abstract
BACKGROUND AND PURPOSE Medullary thyroid carcinoma is an uncommon malignancy that is challenging to diagnose. Our aim was to present our experience using core needle biopsy for the diagnosis of medullary thyroid carcinoma compared with fine-needle aspiration. MATERIALS AND METHODS Between January 2000 and March 2012, 202 thyroid nodules in 191 patients were diagnosed as medullary thyroid cancer by using sonography-guided fine-needle aspiration, core needle biopsy, or surgery. One hundred eighty-three thyroid nodules in 172 patients were included on the basis of the final diagnosis. We evaluated the sensitivity and positive predictive value of fine-needle aspiration and core needle biopsy for the diagnosis of medullary thyroid cancer. We compared the rate of a delayed diagnosis, a diagnostic surgery, and surgery with an incorrect diagnosis for fine-needle aspiration and core needle biopsy and investigated the factors related to the fine-needle aspiration misdiagnosis of medullary thyroid cancer. RESULTS Fine-needle aspiration showed 43.8% sensitivity and 85.1% positive predictive value for the diagnosis of medullary thyroid cancer; 25.7% (44/171) of patients had a delayed diagnosis, while 18.7% (32/171) underwent an operation for accurate diagnosis, and 20.5% (35/171) underwent an operation with an incorrect diagnosis. Core needle biopsy achieved 100% sensitivity and positive predictive value without a delay in diagnosis (0/22), the need for a diagnostic operation (0/22), or an operation for an incorrect diagnosis (0/22). A calcitonin level of <100 pg/mL was the only significant factor for predicting the fine-needle aspiration misdiagnosis of medullary thyroid cancer (P = .034). CONCLUSIONS Core needle biopsy showed a superior sensitivity and positive predictive value to fine-needle aspiration and could optimize the surgical management in patients with medullary thyroid cancer. Because the ability of fine-needle aspiration to diagnose medullary thyroid cancer significantly decreases in patients with serum calcitonin levels of <100 pg/mL, core needle biopsy could be indicated for these patients to optimize their surgical management.
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Affiliation(s)
- E J Ha
- From the Department of Radiology and the Research Institute of Radiology (E.J.H., J.H.B) Department of Radiology (E.J.H.), Ajou University School of Medicine, Suwon, Korea
| | - J H Baek
- From the Department of Radiology and the Research Institute of Radiology (E.J.H., J.H.B)
| | - D G Na
- Department of Radiology (D.G.N.), Human Medical Imaging and Intervention Center, Seoul, Korea Healthcare System Gangnam Center (D.G.N.), Seoul National University Hospital, Seoul, Korea
| | - J-h Kim
- Departments of Radiology (J.-h.K.)
| | - J K Kim
- Department of Radiology (J.K.K.), Chung-Ang University College of Medicine, Seoul, Korea
| | | | - D E Song
- Departments of Pathology (D.E.S.)
| | - K E Lee
- Surgery (K.E.L.), Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Y K Shong
- Metabolism and Endocrinology (Y.K.S.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Lee JK, Shin JY, Kim S, Lee S, Park C, Kim JY, Koh Y, Keam B, Min HS, Kim TM, Jeon YK, Kim DW, Chung DH, Heo DS, Lee SH, Kim JI. Primary resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in patients with non-small-cell lung cancer harboring TKI-sensitive EGFR mutations: an exploratory study. Ann Oncol 2013; 24:2080-7. [PMID: 23559152 DOI: 10.1093/annonc/mdt127] [Citation(s) in RCA: 94] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Affiliation(s)
- J K Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
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20
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Affiliation(s)
- S J Cho
- Center for Gastric Cancer, Research Institute and Hospital, National Cancer Center, Korea
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21
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Kim DW, Min HS, Lee KH, Kim YJ, Oh DY, Jeon YK, Lee SH, Im SA, Chung DH, Kim YT, Kim TY, Bang YJ, Sung SW, Kim JH, Heo DS. High tumour islet macrophage infiltration correlates with improved patient survival but not with EGFR mutations, gene copy number or protein expression in resected non-small cell lung cancer. Br J Cancer 2008; 98:1118-24. [PMID: 18283317 PMCID: PMC2275476 DOI: 10.1038/sj.bjc.6604256] [Citation(s) in RCA: 78] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2007] [Revised: 01/02/2008] [Accepted: 01/18/2008] [Indexed: 11/09/2022] Open
Abstract
The purpose of this study was to investigate the prognostic value of tumour-associated macrophages with a focus on micro-anatomical localisation and determine whether molecular changes of the epidermal growth factor receptor (EGFR) are related to macrophage infiltration in resected non-small cell lung cancer (NSCLC). One hundred and forty-four patients were included in this study. Immunohistochemistry was used to identify CD68+ macrophages in the tumour islet and surrounding stroma. Epidermal growth factor receptor mutations were studied by direct sequencing. The EGFR gene copy number and protein expression were analysed by fluorescence in situ hybridisation and immunohistochemistry. Patients with a high tumour islet macrophage density survived longer than did the patient with a low tumour islet macrophage density (5-year overall survival rate was 63.9 vs 38.9%, P=0.0002). A multivariate Cox proportional hazard analysis revealed that the tumour islet macrophage count was an independent prognostic factor for survival (hazard ratio 0.471, 95% confidence interval 0.300-0.740). However, EGFR mutations, gene copy number, and protein expression were not related to the macrophage infiltration. In conclusion, tumour islet macrophage infiltration was identified as a strong favourable independent prognostic marker for survival but not correlated with the molecular changes of the EGFR in patients with resected NSCLC.
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Affiliation(s)
- D-W Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - H S Min
- Department of Pathology, Seoul National University Hospital, Seoul, Korea
| | - K-H Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Y J Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - D-Y Oh
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Y K Jeon
- Department of Pathology, Seoul National University Hospital, Seoul, Korea
| | - S-H Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - S-A Im
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - D H Chung
- Department of Pathology, Seoul National University Hospital, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Y T Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Korea
| | - T-Y Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Y-J Bang
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - S W Sung
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Korea
| | - J H Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Korea
| | - D S Heo
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
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Park YJ, Kim HM, Ahn YJ, Park SJ, Min HS, Kwak HK, Joo SE, Park YC, Kim KH, Oh KS, Kimm K, Park C. The Study of Association Between Metabolic Syndrome Incidence and Bmi Change in the Korean Health and Genome Study. Am J Epidemiol 2006. [DOI: 10.1093/aje/163.suppl_11.s44-b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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PARK SJ, Ahn Y, Min HS, Kim HM, Park YJ, Oh KS, Shin C, Cho NH, Park C, Kimm K. 320: Metabolic Syndrome Prevalence and Related Factors in the Korean Health and Genome Study. Am J Epidemiol 2005. [DOI: 10.1093/aje/161.supplement_1.s80c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- S J PARK
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
| | - Y Ahn
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
| | - H S Min
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
| | - H M Kim
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
| | - Y J Park
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
| | - K S Oh
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
| | - C Shin
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
| | - N H Cho
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
| | - C Park
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
| | - K Kimm
- National Genome Research Institute, Korea Center for Disease Control and Prevention, Seoul, Korea
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Min HS, Ahn Y, Kim HM, Park YJ, Park SJ, Yang EJ, Oh KS, Shin C, Cho NH, Park C, Kimm K. 212: Patterns of Time-Dependent Insulin Blood Levels During the Oral Glucose Tolerance Test in Korean Subjects. Am J Epidemiol 2005. [DOI: 10.1093/aje/161.supplement_1.s53c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- H S Min
- National Genome Research Institute, Republic of Korea
| | - Y Ahn
- National Genome Research Institute, Republic of Korea
| | - H M Kim
- National Genome Research Institute, Republic of Korea
| | - Y J Park
- National Genome Research Institute, Republic of Korea
| | - S J Park
- National Genome Research Institute, Republic of Korea
| | - E J Yang
- National Genome Research Institute, Republic of Korea
| | - K S Oh
- National Genome Research Institute, Republic of Korea
| | - C Shin
- National Genome Research Institute, Republic of Korea
| | - N H Cho
- National Genome Research Institute, Republic of Korea
| | - C Park
- National Genome Research Institute, Republic of Korea
| | - K Kimm
- National Genome Research Institute, Republic of Korea
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Min HS. [Nursing care of a patient with a vertebral fracture]. Taehan Kanho 1965; 4:54-60. [PMID: 5216271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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