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Chen J, Fu L, Wei M, Zheng S, Zheng J, Lyu Z, Huang X, Sun L. Label-free white blood cells classification using a deep feature fusion neural network. Heliyon 2024; 10:e31496. [PMID: 38845979 PMCID: PMC11153090 DOI: 10.1016/j.heliyon.2024.e31496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 05/16/2024] [Indexed: 06/09/2024] Open
Abstract
White blood cell (WBC) classification is a valuable diagnostic approach for identifying diseases. However, conventional methods for WBC detection, such as flow cytometers, have limitations in terms of their high cost, large system size, and laborious staining procedures. As a result, deep learning-based label-free WBC image analysis methods are gaining popularity. Nevertheless, most existing deep learning WBC classification techniques fail to effectively utilize the subtle differences in the internal structures of WBCs observed under a microscope. To address this issue, we propose a neural network with feature fusion in this study, which enables the detection of label-free WBCs. Unlike conventional convolutional neural networks (CNNs), our approach combines low-level features extracted by shallow layers with high-level features extracted by deep layers, generating fused features for accurate bright-field WBC identification. Our method achieves an accuracy of 80.3 % on the testing set, demonstrating a potential solution for deep-learning-based biomedical diagnoses. Considering the proposed method simplifies the cell detection process and eliminates the need for complex operations like fluorescent staining, we anticipate that this automatic and label-free WBC classification network could facilitate more precise and effective analysis, and it could contribute to the future adoption of miniatured flow cytometers for point-of-care (POC) diagnostics applications.
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Affiliation(s)
| | | | - Maoyu Wei
- Ministry of Education Key Laboratory of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Sikai Zheng
- Ministry of Education Key Laboratory of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jingwen Zheng
- Ministry of Education Key Laboratory of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Zefei Lyu
- Ministry of Education Key Laboratory of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Xiwei Huang
- Ministry of Education Key Laboratory of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Lingling Sun
- Ministry of Education Key Laboratory of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
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2
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Xu C, Zhang Y, Fan X, Lan X, Ye X, Wu T. An efficient fluorescence in situ hybridization (FISH)-based circulating genetically abnormal cells (CACs) identification method based on Multi-scale MobileNet-YOLO-V4. Quant Imaging Med Surg 2022; 12:2961-2976. [PMID: 35502367 PMCID: PMC9014158 DOI: 10.21037/qims-21-909] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 02/11/2022] [Indexed: 11/06/2023]
Abstract
BACKGROUND Circulating tumor cells (CTCs) acting as "liquid biopsy" of cancer are cells that have been shed from the primary tumor, which cause the development of a secondary tumor in a distant organ site, leading to cancer metastasis. Recent research suggests that CTCs with abnormalities in gene copy numbers in mononuclear cell-enriched peripheral blood samples, namely circulating genetically abnormal cells (CACs), could be used as a non-invasive decision tool to detect patients with benign pulmonary nodules. Such cells are identified by counting the fluorescence signals of fluorescence in situ hybridization (FISH). However, owing to the rarity of CACs in the blood, identification of CACs using this technique is time-consuming and is a drawback of this method. METHODS This study has proposed an efficient and automatic FISH-based CACs identification approach which is based on a combination of the high accuracy of You Only Look Once (YOLO)-V4 and the lightweight and rapidness of MobileNet-V3. The backbone of YOLO-V4 was replaced with MobileNet-V3 to improve the detection efficiency and prevent overfitting, and the architecture of YOLO-V4 was optimized by utilizing a new feature map with a larger scale to enable the enhanced detection ability for small targets. RESULTS We trained and tested the proposed model using a dataset containing more than 7,000 cells based on five-fold cross-validation. All the images in the dataset were 2,448×2,048 (pixels) in size. The number of cells in each image was >70. The accuracy of four-color fluorescence signals detection for our proposed model were all approximately 98%, and the mean average precision (mAP) were close to 100%. The final outcome of the developed method was the type of cells, i.e., normal cells, CACs, gaining cells or deletion cells. The method had a CACs identification accuracy of 93.86% (similar to an expert pathologist), and a detection speed that was about 500 times greater than that of a pathologist. CONCLUSIONS The developed method could greatly increase the review accuracy, enhance the efficiency of reviewers, and reduce the review turnaround time during CACs identification.
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Affiliation(s)
- Chao Xu
- China Telecommunication Technology Labs, China Academy of Information and Communications Technology, Beijing, China
| | - Yi Zhang
- China Telecommunication Technology Labs, China Academy of Information and Communications Technology, Beijing, China
| | - Xianjun Fan
- Department of Product Development, Zhuhai Sanmed Biotech Ltd., Zhuhai, China
| | - Xingjie Lan
- Department of Data Operation, Zhuhai Sanmed Biotech Ltd., Zhuhai, China
| | - Xin Ye
- Department of Product Development, Zhuhai Sanmed Biotech Ltd., Zhuhai, China
| | - Tongning Wu
- China Telecommunication Technology Labs, China Academy of Information and Communications Technology, Beijing, China
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3
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Automatic Cancer Cell Taxonomy Using an Ensemble of Deep Neural Networks. Cancers (Basel) 2022; 14:cancers14092224. [PMID: 35565352 PMCID: PMC9100154 DOI: 10.3390/cancers14092224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 04/18/2022] [Accepted: 04/26/2022] [Indexed: 12/24/2022] Open
Abstract
Microscopic image-based analysis has been intensively performed for pathological studies and diagnosis of diseases. However, mis-authentication of cell lines due to misjudgments by pathologists has been recognized as a serious problem. To address this problem, we propose a deep-learning-based approach for the automatic taxonomy of cancer cell types. A total of 889 bright-field microscopic images of four cancer cell lines were acquired using a benchtop microscope. Individual cells were further segmented and augmented to increase the image dataset. Afterward, deep transfer learning was adopted to accelerate the classification of cancer types. Experiments revealed that the deep-learning-based methods outperformed traditional machine-learning-based methods. Moreover, the Wilcoxon signed-rank test showed that deep ensemble approaches outperformed individual deep-learning-based models (p < 0.001) and were in effect to achieve the classification accuracy up to 97.735%. Additional investigation with the Wilcoxon signed-rank test was conducted to consider various network design choices, such as the type of optimizer, type of learning rate scheduler, degree of fine-tuning, and use of data augmentation. Finally, it was found that the using data augmentation and updating all the weights of a network during fine-tuning improve the overall performance of individual convolutional neural network models.
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A Consistent Protocol Reveals a Large Heterogeneity in the Biological Effectiveness of Proton and Carbon-Ion Beams for Various Sarcoma and Normal-Tissue-Derived Cell Lines. Cancers (Basel) 2022; 14:cancers14082009. [PMID: 35454915 PMCID: PMC9029457 DOI: 10.3390/cancers14082009] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 04/03/2022] [Accepted: 04/07/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary Using a consistent experimental protocol, we found a large heterogeneity in the relative biological effectiveness (RBE) values of both proton and carbon-ion beams in various sarcomas and normal-tissue-derived cell lines. Our data suggest that proton beam therapy may be more beneficial for some types of tumors. In carbon-ion therapy, for some types of tumors, large heterogeneity in RBE should prompt consideration of dose reduction or an increased dose per fraction. In particular, a higher RBE value in normal tissues requires caution. Specific dose evaluations for tumor and normal tissues are needed for both proton and carbon-ion therapies. Abstract This study investigated variations in the relative biological effectiveness (RBE) values among various sarcoma and normal-tissue-derived cell lines (normal cell line) in proton beam and carbon-ion irradiations. We used a consistent protocol that specified the timing of irradiation after plating cells and detailed the colony formation assay. We examined the cell type dependence of RBE for proton beam and carbon-ion irradiations using four human sarcoma cell lines (MG63 osteosarcoma, HT1080 fibrosarcoma, SW872 liposarcoma, and SW1353 chondrosarcoma) and three normal cell lines (HDF human dermal fibroblast, hTERT-HME1 mammary gland, and NuLi-1 bronchus epithelium). The cells were irradiated with gamma rays, proton beams at the center of the spread-out Bragg peak, or carbon-ion beams at 54.4 keV/μm linear energy transfer. In all sarcoma and normal cell lines, the average RBE values in proton beam and carbon-ion irradiations were 1.08 ± 0.11 and 2.08 ± 0.36, which were consistent with the values of 1.1 and 2.13 used in current treatment planning systems, respectively. Up to 34% difference in the RBE of the proton beam was observed between MG63 and HT1080. Similarly, a 32% difference in the RBE of the carbon-ion beam was observed between SW872 and the other sarcoma cell lines. In proton beam irradiation, normal cell lines had less variation in RBE values (within 10%), whereas in carbon-ion irradiation, RBE values differed by up to 48% between hTERT-HME1 and NuLi-1. Our results suggest that specific dose evaluations for tumor and normal tissues are necessary for treatment planning in both proton and carbon-ion therapies.
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5
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Asai A, Konno M, Taniguchi M, Vecchione A, Ishii H. Computational healthcare: Present and future perspectives (Review). Exp Ther Med 2021; 22:1351. [PMID: 34659497 PMCID: PMC8515560 DOI: 10.3892/etm.2021.10786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 07/19/2021] [Indexed: 12/05/2022] Open
Abstract
Artificial intelligence (AI) has been developed through repeated new discoveries since around 1960. The use of AI is now becoming widespread within society and our daily lives. AI is also being introduced into healthcare, such as medicine and drug development; however, it is currently biased towards specific domains. The present review traces the history of the development of various AI-based applications in healthcare and compares AI-based healthcare with conventional healthcare to show the future prospects for this type of care. Knowledge of the past and present development of AI-based applications would be useful for the future utilization of novel AI approaches in healthcare.
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Affiliation(s)
- Ayumu Asai
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan.,Artificial Intelligence Research Center, Osaka University, Ibaraki, Osaka 567-0047, Japan.,The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka 567-0047, Japan
| | - Masamitsu Konno
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
| | - Masateru Taniguchi
- The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka 567-0047, Japan
| | - Andrea Vecchione
- Department of Clinical and Molecular Medicine, University of Rome 'Sapienza', Santo Andrea Hospital, I-1035-00189 Rome, Italy
| | - Hideshi Ishii
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
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6
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Shigene K, Hiasa Y, Otake Y, Soufi M, Janewanthanakul S, Nishimura T, Sato Y, Suetsugu S. Translation of Cellular Protein Localization Using Convolutional Networks. Front Cell Dev Biol 2021; 9:635231. [PMID: 34422790 PMCID: PMC8375474 DOI: 10.3389/fcell.2021.635231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 07/15/2021] [Indexed: 12/15/2022] Open
Abstract
Protein localization in cells has been analyzed by fluorescent labeling using indirect immunofluorescence and fluorescent protein tagging. However, the relationships between the localization of different proteins had not been analyzed using artificial intelligence. Here, we applied convolutional networks for the prediction of localization of the cytoskeletal proteins from the localization of the other proteins. Lamellipodia are one of the actin-dependent subcellular structures involved in cell migration and are mainly generated by the Wiskott-Aldrich syndrome protein (WASP)-family verprolin homologous protein 2 (WAVE2) and the membrane remodeling I-BAR domain protein IRSp53. Focal adhesion is another actin-based structure that contains vinculin protein and promotes lamellipodia formation and cell migration. In contrast, microtubules are not directly related to actin filaments. The convolutional network was trained using images of actin filaments paired with WAVE2, IRSp53, vinculin, and microtubules. The generated images of WAVE2, IRSp53, and vinculin were highly similar to their real images. In contrast, the microtubule images generated from actin filament images were inferior without the generation of filamentous structures, suggesting that microscopic images of actin filaments provide more information about actin-related protein localization. Collectively, this study suggests that image translation by the convolutional network can predict the localization of functionally related proteins, and the convolutional network might be used to describe the relationships between the proteins by their localization.
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Affiliation(s)
- Kei Shigene
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Yuta Hiasa
- Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan
| | - Yoshito Otake
- Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan
| | - Mazen Soufi
- Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan
| | - Suphamon Janewanthanakul
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Tamako Nishimura
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Yoshinobu Sato
- Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan.,Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan
| | - Shiro Suetsugu
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan.,Center for Digital Green-Innovation, Nara Institute of Science and Technology, Ikoma, Japan
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7
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Jiang L, Loo SCJ. Intelligent Nanoparticle-Based Dressings for Bacterial Wound Infections. ACS APPLIED BIO MATERIALS 2021; 4:3849-3862. [PMID: 34056562 PMCID: PMC8155196 DOI: 10.1021/acsabm.0c01168] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/26/2020] [Indexed: 12/14/2022]
Abstract
Conventional wound dressing materials containing free antibiotics for bacterial wound infections are presented with several limitations, that is, lack of controlled and triggered release capabilities, and may often not be adequate to address the complex bacteria microenvironment of such infections. Additionally, the improper usage of antibiotics may also result in the emergence of drug resistant strains. While delivery systems (i.e., nanoparticles) that encapsulate antibiotics may potentially overcome some of these limitations, their therapeutic outcomes are still less than desirable. For example, premature drug release or unintended drug activation may occur, which would greatly reduce treatment efficacy. To address this, responsive nanoparticle-based antimicrobial therapies could be a promising strategy. Such nanoparticles can be functionalized to react to a single stimulus or multi stimulus within the bacteria microenvironment and subsequently elicit a therapeutic response. Such "intelligent" nanoparticles can be designed to respond to the microenvironment, that is, an acidic pH, the presence of specific enzymes, bacterial toxins, etc. or to an external stimulus, for example, light, thermal, etc. These responsive nanoparticles can be further incorporated into wound dressings to better promote wound healing. This review summarizes and highlights the recent progress on such intelligent nanoparticle-based dressings as potential wound dressings for bacteria-infected wounds, along with the current challenges and prospects for these technologies to be successfully translated into the clinic.
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Affiliation(s)
- Lai Jiang
- School
of Materials Science & Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Say Chye Joachim Loo
- School
of Materials Science & Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Singapore
Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
- Harvard
T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, Massachusetts 02115, United States
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8
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Chen Z, Ma N, Sun X, Li Q, Zeng Y, Chen F, Sun S, Xu J, Zhang J, Ye H, Ge J, Zhang Z, Cui X, Leong K, Chen Y, Gu Z. Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition. Biomaterials 2021; 272:120770. [PMID: 33798957 DOI: 10.1016/j.biomaterials.2021.120770] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 12/26/2022]
Abstract
Three-dimensional in vitro tumor models provide more physiologically relevant responses to drugs than 2D models, but the lack of proper evaluation indices and the laborious quantitation of tumor behavior in 3D have limited the use of 3D tumor models in large-scale preclinical drug screening. Here we propose two indices of 3D tumor invasiveness-the excess perimeter index (EPI) and the multiscale entropy index (MSEI)-and combine these indices with a new convolutional neural network-based algorithm for tumor spheroid boundary detection. This new algorithm for 3D tumor boundary detection and invasiveness analysis is more accurate than any other existing algorithms. We apply this spheroid monitoring and AI-based recognition technique ("SMART") to evaluating the invasiveness of tumor spheroids grown from tumor cell lines and from primary tumor cells in 3D culture.
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Affiliation(s)
- Zaozao Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China; Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Ning Ma
- School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, 210096, China
| | - Xiaowei Sun
- Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Qiwei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China
| | - Yi Zeng
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China
| | - Fei Chen
- Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Shiqi Sun
- Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Jun Xu
- Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Jing Zhang
- Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Huan Ye
- Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Jianjun Ge
- Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Zheng Zhang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China
| | - Xingran Cui
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China
| | - Kam Leong
- Department of Biomedical Engineering, Columbia University, New York, NY, 10032, USA
| | - Yang Chen
- School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, 210096, China.
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China; Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China.
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Malherbe K. Tumor Microenvironment and the Role of Artificial Intelligence in Breast Cancer Detection and Prognosis. THE AMERICAN JOURNAL OF PATHOLOGY 2021; 191:1364-1373. [PMID: 33639101 DOI: 10.1016/j.ajpath.2021.01.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/02/2021] [Accepted: 01/28/2021] [Indexed: 12/21/2022]
Abstract
A critical knowledge gap has been noted in breast cancer detection, prognosis, and evaluation between tumor microenvironment and associated neoplasm. Artificial intelligence (AI) has multiple subsets or methods for data extraction and evaluation, including artificial neural networking, which allows computational foundations, similar to neurons, to make connections and new neural pathways during data set training. Deep machine learning and AI hold great potential to accurately assess tumor microenvironment models employing vast data management techniques. Despite the significant potential AI holds, there is still much debate surrounding the appropriate and ethical curation of medical data from picture archiving and communication systems. AI output's clinical significance depends on its human predecessor's data training sets. Integration between biomarkers, risk factors, and imaging data will allow the best predictor models for patient-based outcomes.
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Affiliation(s)
- Kathryn Malherbe
- Department Radiography, Faculty Health Sciences, University of Pretoria, Pretoria, South Africa.
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10
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Tan X, Li K, Zhang J, Wang W, Wu B, Wu J, Li X, Huang X. Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study. Cancer Cell Int 2021; 21:35. [PMID: 33413391 PMCID: PMC7791865 DOI: 10.1186/s12935-020-01742-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 12/14/2020] [Accepted: 12/25/2020] [Indexed: 12/21/2022] Open
Abstract
Background The incidence rates of cervical cancer in developing countries have been steeply increasing while the medical resources for prevention, detection, and treatment are still quite limited. Computer-based deep learning methods can achieve high-accuracy fast cancer screening. Such methods can lead to early diagnosis, effective treatment, and hopefully successful prevention of cervical cancer. In this work, we seek to construct a robust deep convolutional neural network (DCNN) model that can assist pathologists in screening cervical cancer. Methods ThinPrep cytologic test (TCT) images diagnosed by pathologists from many collaborating hospitals in different regions were collected. The images were divided into a training dataset (13,775 images), validation dataset (2301 images), and test dataset (408,030 images from 290 scanned copies) for training and effect evaluation of a faster region convolutional neural network (Faster R-CNN) system. Results The sensitivity and specificity of the proposed cervical cancer screening system was 99.4 and 34.8%, respectively, with an area under the curve (AUC) of 0.67. The model could also distinguish between negative and positive cells. The sensitivity values of the atypical squamous cells of undetermined significance (ASCUS), the low-grade squamous intraepithelial lesion (LSIL), and the high-grade squamous intraepithelial lesions (HSIL) were 89.3, 71.5, and 73.9%, respectively. This system could quickly classify the images and generate a test report in about 3 minutes. Hence, the system can reduce the burden on the pathologists and saves them valuable time to analyze more complex cases. Conclusions In our study, a CNN-based TCT cervical-cancer screening model was established through a retrospective study of multicenter TCT images. This model shows improved speed and accuracy for cervical cancer screening, and helps overcome the shortage of medical resources required for cervical cancer screening.
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Affiliation(s)
- Xiangyu Tan
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China
| | - Kexin Li
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China
| | - Jiucheng Zhang
- College of Computer Science & Technology, Zhejiang University, 310027, Hangzhou, China
| | - Wenzhe Wang
- College of Computer Science & Technology, Zhejiang University, 310027, Hangzhou, China
| | - Bian Wu
- Data Science and AI Lab, WeDoctor Group Limited, 311200, Hangzhou, China
| | - Jian Wu
- School of Public Health, Zhejiang University, 310027, Hangzhou, China
| | - Xiaoping Li
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China.
| | - Xiaoyuan Huang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China.
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11
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Wang S, Zhou Y, Qin X, Nair S, Huang X, Liu Y. Label-free detection of rare circulating tumor cells by image analysis and machine learning. Sci Rep 2020; 10:12226. [PMID: 32699281 PMCID: PMC7376046 DOI: 10.1038/s41598-020-69056-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 06/22/2020] [Indexed: 12/14/2022] Open
Abstract
Detection and characterization of rare circulating tumor cells (CTCs) in patients' blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of CTCs from patient blood samples, by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network, a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and CTCs. It requires minimal data pre-processing and has an easy experimental setup. Through our experiments, we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying CTCs. With more data becoming available in the future, the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for CTC analysis.
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Affiliation(s)
- Shen Wang
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, 18015, USA
| | - Yuyuan Zhou
- Department of Bioengineering, Lehigh University, Bethlehem, PA, 18015, USA
| | - Xiaochen Qin
- Department of Bioengineering, Lehigh University, Bethlehem, PA, 18015, USA
| | - Suresh Nair
- Lehigh Valley Health Network, Lehigh Valley Cancer Institute, Allentown, PA, 18103, USA
| | - Xiaolei Huang
- College of Information Sciences and Technology and Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, 16802, USA.
| | - Yaling Liu
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, 18015, USA. .,Department of Bioengineering, Lehigh University, Bethlehem, PA, 18015, USA.
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12
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Alami H, Lehoux P, Auclair Y, de Guise M, Gagnon MP, Shaw J, Roy D, Fleet R, Ag Ahmed MA, Fortin JP. Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity. J Med Internet Res 2020; 22:e17707. [PMID: 32406850 PMCID: PMC7380986 DOI: 10.2196/17707] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 04/25/2020] [Accepted: 05/13/2020] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) is seen as a strategic lever to improve access, quality, and efficiency of care and services and to build learning and value-based health systems. Many studies have examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services raises. To help decision makers address these issues in a systemic and holistic manner, this viewpoint paper relies on the health technology assessment core model to contrast the expectations of the health sector toward the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, financing, and reimbursing novel technologies. This paper suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal, and ethical. The assessment of AI's value proposition should thus go beyond technical performance and cost logic by performing a holistic analysis of its value in a real-world context of care and services. To guide AI development, generate knowledge, and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical, and technological conditions for innovation should be created as a first step.
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Affiliation(s)
- Hassane Alami
- Public Health Research Center, Université de Montréal, Montreal, QC, Canada
- Department of Health Management, Evaluation and Policy, École de santé publique de l'Université de Montréal, Montreal, QC, Canada
- Institut national d'excellence en santé et services sociaux, Montréal, QC, Canada
| | - Pascale Lehoux
- Public Health Research Center, Université de Montréal, Montreal, QC, Canada
- Department of Health Management, Evaluation and Policy, École de santé publique de l'Université de Montréal, Montreal, QC, Canada
| | - Yannick Auclair
- Institut national d'excellence en santé et services sociaux, Montréal, QC, Canada
| | - Michèle de Guise
- Institut national d'excellence en santé et services sociaux, Montréal, QC, Canada
| | - Marie-Pierre Gagnon
- Research Center on Healthcare and Services in Primary Care, Université Laval, Quebec, QC, Canada
- Faculty of Nursing Science, Université Laval, Quebec, QC, Canada
| | - James Shaw
- Joint Centre for Bioethics, University of Toronto, Toronto, ON, Canada
- Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada
| | - Denis Roy
- Institut national d'excellence en santé et services sociaux, Montréal, QC, Canada
| | - Richard Fleet
- Research Center on Healthcare and Services in Primary Care, Université Laval, Quebec, QC, Canada
- Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université Laval, Quebec, QC, Canada
- Research Chair in Emergency Medicine, Université Laval - CHAU Hôtel-Dieu de Lévis, Lévis, QC, Canada
| | - Mohamed Ali Ag Ahmed
- Research Chair on Chronic Diseases in Primary Care, Université de Sherbrooke, Chicoutimi, QC, Canada
| | - Jean-Paul Fortin
- Research Center on Healthcare and Services in Primary Care, Université Laval, Quebec, QC, Canada
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec, QC, Canada
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Kriegsmann M, Haag C, Weis CA, Steinbuss G, Warth A, Zgorzelski C, Muley T, Winter H, Eichhorn ME, Eichhorn F, Kriegsmann J, Christopolous P, Thomas M, Witzens-Harig M, Sinn P, von Winterfeld M, Heussel CP, Herth FJF, Klauschen F, Stenzinger A, Kriegsmann K. Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer. Cancers (Basel) 2020; 12:cancers12061604. [PMID: 32560475 PMCID: PMC7352768 DOI: 10.3390/cancers12061604] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 06/14/2020] [Accepted: 06/15/2020] [Indexed: 12/24/2022] Open
Abstract
Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.
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Affiliation(s)
- Mark Kriegsmann
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Correspondence: (M.K.); (K.K.); Tel.: +49-6221-56-36930 (M.K.); +49-6221-56-37238 (K.K.)
| | - Christian Haag
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
- Department Hematology, Oncology and Rheumatology, Heidelberg University, 69120 Heidelberg, Germany
| | - Cleo-Aron Weis
- Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, 68782 Mannheim, Germany;
| | - Georg Steinbuss
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
- Department Hematology, Oncology and Rheumatology, Heidelberg University, 69120 Heidelberg, Germany
| | - Arne Warth
- Institute of Pathology, Cytopathology, and Molecular Pathology, UEGP MVZ Gießen/Wetzlar/Limburg, 65549 Limburg, Germany;
| | - Christiane Zgorzelski
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
| | - Thomas Muley
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Hauke Winter
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Martin E. Eichhorn
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Florian Eichhorn
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Joerg Kriegsmann
- Molecular Pathology Trier, 54296 Trier, Germany;
- Danube Private University Krems, 3500 Krems, Austria
| | - Petros Christopolous
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Oncology, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Michael Thomas
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Oncology, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | | | - Peter Sinn
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
| | - Moritz von Winterfeld
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
| | - Claus Peter Heussel
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, Heidelberg University, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Thoraxklinik, Heidelberg University, 69120 Heidelberg, Germany
| | - Felix J. F. Herth
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Pneumology and Critical Care Medicine, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | | | - Albrecht Stenzinger
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
| | - Katharina Kriegsmann
- Department Hematology, Oncology and Rheumatology, Heidelberg University, 69120 Heidelberg, Germany
- Correspondence: (M.K.); (K.K.); Tel.: +49-6221-56-36930 (M.K.); +49-6221-56-37238 (K.K.)
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14
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Yanagisawa K, Toratani M, Asai A, Konno M, Niioka H, Mizushima T, Satoh T, Miyake J, Ogawa K, Vecchione A, Doki Y, Eguchi H, Ishii H. Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells. Int J Mol Sci 2020; 21:E3166. [PMID: 32365822 PMCID: PMC7246790 DOI: 10.3390/ijms21093166] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/28/2020] [Accepted: 04/28/2020] [Indexed: 01/08/2023] Open
Abstract
It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system.
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Affiliation(s)
- Kiminori Yanagisawa
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; (K.Y.); (A.A.); (T.M.); (Y.D.); (H.E.)
- Department of Medical Data Science, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan;
- Department of Frontier Science for Cancer and Chemotherapy, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan;
| | - Masayasu Toratani
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; (M.T.); (K.O.)
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka-shi, Osaka 541-8567, Japan
| | - Ayumu Asai
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; (K.Y.); (A.A.); (T.M.); (Y.D.); (H.E.)
- Department of Medical Data Science, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan;
- Artificial Intelligence Research Center, The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
| | - Masamitsu Konno
- Department of Medical Data Science, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan;
- Department of Frontier Science for Cancer and Chemotherapy, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan;
| | - Hirohiko Niioka
- Institute for Datability Science, Osaka University, 2-8 Yamadaoka, Suita, Osaka 565-0871, Japan;
| | - Tsunekazu Mizushima
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; (K.Y.); (A.A.); (T.M.); (Y.D.); (H.E.)
| | - Taroh Satoh
- Department of Frontier Science for Cancer and Chemotherapy, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan;
| | - Jun Miyake
- Global Center for Medical Engineering and Informatics, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan;
| | - Kazuhiko Ogawa
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; (M.T.); (K.O.)
| | - Andrea Vecchione
- Department of Clinical and Molecular Medicine, University of Rome “Sapienza”, Santo Andrea Hospital, via di Grottarossa, 1035-00189 Rome, Italy;
| | - Yuichiro Doki
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; (K.Y.); (A.A.); (T.M.); (Y.D.); (H.E.)
| | - Hidetoshi Eguchi
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; (K.Y.); (A.A.); (T.M.); (Y.D.); (H.E.)
| | - Hideshi Ishii
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; (K.Y.); (A.A.); (T.M.); (Y.D.); (H.E.)
- Department of Medical Data Science, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan;
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15
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Klonowski W, Korzynska A, Chwala A. Computer analysis of histopathological images for tumor grading. 2. Physiol Meas 2019; 40:075010. [PMID: 31158821 DOI: 10.1088/1361-6579/ab267e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE We have upgraded our own original color filtration pixel-by-pixel (CFPP) method (Klonowski et al 2018a Physiol. Meas. 39 034002) to enable not only automatic and rapid assessment of the proliferation index of a tumor or neoplasm but also quick automatic location of hot-spots (regions of interest, ROIs) in immunohistochemically stained microscopic images of neoplasms and tumors. APPROACH Neoplastic cells stain differently from normal cells. By counting in a given window the number of pixels belonging to the given subspaces of (R,G,B) color space which correspond, respectively, to proliferating cells (which are mostly neoplastic) and non-proliferating cells (which are mostly normal) we calculate the local proliferation index in this window. The window is moved all around the whole histopathological virtual slide (WSI) or around a chosen part of the WSI. By adding the respective numbers calculated for all the windows covering the WSI or the chosen part of it one can easily calculate the global proliferation index. MAIN RESULTS The method is rapid and does not require the time-consuming step of selecting ROIs manually nor does it need computationally complicated detection of hot-spots, both of which attempt to emulate a pathologist's way of thinking. We apply our method to a set of slide images of diffuse large B-cell lymphoma. SIGNIFICANCE By appropriate changes in the (R,G,B) color filtration thresholds, our method may be adapted to the analysis of other types of tumors. It may also be adapted for analysis of microscopic images in neuropathology. Because of its rapidity and simplicity it may also used for analysis of series of images to assess local dynamics of image complexity in network physiology applications.
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Affiliation(s)
- Wlodzimierz Klonowski
- Laboratory of Processing and Analysis of Microscopic Images, Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Warsaw, Poland. Author to whom any correspondence should be addressed
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Dzobo K, Adotey S, Thomford NE, Dzobo W. Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2019; 24:247-263. [PMID: 31313972 DOI: 10.1089/omi.2019.0038] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.
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Affiliation(s)
- Kevin Dzobo
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), UCT Medical Campus, Anzio Road, Observatory 7925, Cape Town, South Africa.,Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Sampson Adotey
- International Development Innovation Network, D-Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Nicholas E Thomford
- Pharmacogenetics Research Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory 7925, Cape Town, South Africa
| | - Witness Dzobo
- Pathology and Immunology Department, University Hospital Southampton, Mail Point B, Tremona Road, Southampton, UK.,University of Portsmouth, Faculty of Science, St Michael's Building, White Swan Road, Portsmouth, UK
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17
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Affiliation(s)
- Masamitsu Konno
- Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Hideshi Ishii
- Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
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