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Yu S, Jeon BR, Liu C, Kim D, Park HI, Park HD, Shin JH, Lee JH, Choi Q, Kim S, Yun YM, Cho EJ. Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence. Ann Lab Med 2024; 44:562-571. [PMID: 38953115 PMCID: PMC11375187 DOI: 10.3343/alm.2024.0111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/03/2024] [Accepted: 06/21/2024] [Indexed: 07/03/2024] Open
Abstract
Background Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM). Methods A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions). Results In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles. Conclusions This survey highlighted KSLM members' awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.
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Affiliation(s)
- Shinae Yu
- Department of Laboratory Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Byung Ryul Jeon
- Department of Laboratory Medicine & Genetics, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea
| | - Changseung Liu
- Departments of Laboratory Medicine, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea
| | - Dokyun Kim
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul, Korea
| | - Hae-Il Park
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyung Doo Park
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeong Hwan Shin
- Department of Laboratory Medicine, Inje University College of Medicine, Busan, Korea
| | - Jun Hyung Lee
- Department of Laboratory Medicine, GC Labs, Yongin, Korea
| | - Qute Choi
- Department of Laboratory Medicine, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, Daejeon, Korea
| | - Sollip Kim
- Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yeo Min Yun
- Department of Laboratory Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Eun-Jung Cho
- Department of Laboratory Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
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2
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Reyes Gil M, Pantanowitz J, Rashidi HH. Venous thromboembolism in the era of machine learning and artificial intelligence in medicine. Thromb Res 2024; 242:109121. [PMID: 39213896 DOI: 10.1016/j.thromres.2024.109121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/19/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024]
Abstract
In this review, we embark on a comprehensive exploration of venous thromboembolism (VTE) in the context of medical history and its current practice within medicine. We delve into the landscape of artificial intelligence (AI), exploring its present utility and envisioning its transformative roles within VTE management, from prevention to screening and beyond. Central to our discourse is a forward-looking perspective on the integration of AI within VTE in medicine, advocating for rigorous study design, robust validation processes, and meticulous statistical analysis to gauge the efficacy of AI applications. We further illuminate the potential of large language models and generative AI in revolutionizing VTE care, while acknowledging their inherent limitations and proposing innovative solutions to overcome challenges related to data availability and integrity, including the strategic use of synthetic data. The critical importance of navigating ethical, legal, and privacy concerns associated with AI is underscored, alongside the imperative for comprehensive governance and policy frameworks to regulate its deployment in VTE treatment. We conclude on a note of cautious optimism, where we highlight the significance of proactively addressing the myriad challenges that accompany the advent of AI in healthcare. Through diligent design, stringent validation, extensive education, and prudent regulation, we can harness AI's potential to significantly enhance our understanding and management of VTE. As we stand on the cusp of a new era, our commitment to these principles will be instrumental in ensuring that the promise of AI is fully realized within the realm of VTE care.
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Affiliation(s)
- Morayma Reyes Gil
- Thrombosis and Hemostasis Labs, Robert J. Tomsich Pathology & Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, United States of America.
| | - Joshua Pantanowitz
- Computational Pathology and AI Center of Excellence, University of Pittsburgh Medical Center, Pittsburgh, PA, United States of America
| | - Hooman H Rashidi
- Computational Pathology and AI Center of Excellence, University of Pittsburgh Medical Center, Pittsburgh, PA, United States of America.
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3
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George H, Sun Y, Wu J, Yan Y, Wang R, Pesavento RP, Mathew MT. Intelligent salivary biosensors for periodontitis: in vitro simulation of oral oxidative stress conditions. Med Biol Eng Comput 2024; 62:2409-2434. [PMID: 38609577 DOI: 10.1007/s11517-024-03077-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 03/16/2024] [Indexed: 04/14/2024]
Abstract
ASTRACT One of the most common oral diseases affecting millions of people worldwide is periodontitis. Usually, proteins in body fluids are used as biomarkers of diseases. This study focused on hydrogen peroxide, lipopolysaccharide (LPS), and lactic acid as salivary non-protein biomarkers for oxidative stress conditions of periodontitis. Electrochemical analysis of artificial saliva was done using Gamry with increasing hydrogen peroxide, bLPS, and lactic acid concentrations. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) were conducted. From EIS data, change in capacitance and CV plot area were calculated for each test condition. Hydrogen peroxide groups had a decrease in CV area and an increase in percentage change in capacitance, lipopolysaccharide groups had a decrease in CV area and a decrease in percentage change in capacitance, and lactic acid groups had an increase of CV area and an increase in percentage change in capacitance with increasing concentrations. These data showed a unique combination of electrochemical properties for the three biomarkers. Scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) employed to observe the change in the electrode surface and elemental composition data present on the sensor surface also showed a unique trend of elemental weight percentages. Machine learning models using hydrogen peroxide, LPS, and lactic acid electrochemical data were applied for the prediction of risk levels of periodontitis. The detection of hydrogen peroxide, LPS, and lactic acid by electrochemical biosensors indicates the potential to use these molecules as electrochemical biomarkers and use the data for ML-driven prediction tool for the periodontitis risk levels.
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Affiliation(s)
- Haritha George
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Yani Sun
- Department of Material Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Junyi Wu
- Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA
| | - Yan Yan
- Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA
| | - Rong Wang
- Department of Biological and Chemical Sciences, Illinois Institute of Technology, Chicago, IL, USA
| | - Russell P Pesavento
- Department of Oral Biology, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA
| | - Mathew T Mathew
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA.
- Department of Material Science, University of Illinois at Chicago, Chicago, IL, USA.
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4
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Hou H, Zhang R, Li J. Artificial intelligence in the clinical laboratory. Clin Chim Acta 2024; 559:119724. [PMID: 38734225 DOI: 10.1016/j.cca.2024.119724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/13/2024]
Abstract
Laboratory medicine has become a highly automated medical discipline. Nowadays, artificial intelligence (AI) applied to laboratory medicine is also gaining more and more attention, which can optimize the entire laboratory workflow and even revolutionize laboratory medicine in the future. However, only a few commercially available AI models are currently approved for use in clinical laboratories and have drawbacks such as high cost, lack of accuracy, and the need for manual review of model results. Furthermore, there are a limited number of literature reviews that comprehensively address the research status, challenges, and future opportunities of AI applications in laboratory medicine. Our article begins with a brief introduction to AI and some of its subsets, then reviews some AI models that are currently being used in clinical laboratories or that have been described in emerging studies, and explains the existing challenges associated with their application and possible solutions, finally provides insights into the future opportunities of the field. We highlight the current status of implementation and potential applications of AI models in different stages of the clinical testing process.
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Affiliation(s)
- Hanjing Hou
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
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5
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Pratiwi NKC, Tayara H, Chong KT. An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction. Int J Mol Sci 2024; 25:5957. [PMID: 38892144 PMCID: PMC11172808 DOI: 10.3390/ijms25115957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/27/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulations. While the gains in AUC and other metrics might seem modest, they contribute to a model that is more robust, consistent, and adaptable. To assess the effectiveness of various approaches, we compared the performance of logistic regression to four baseline models. Our results indicate that logistic regression consistently underperforms across all evaluated metrics. This suggests that it may not be well-suited to capture the complex relationships within this dataset. Tree-based models, on the other hand, appear to be more effective for problems involving molecular dynamics simulations. Extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) are optimized for performance and speed, handling datasets effectively and incorporating regularizations to avoid over-fitting. Our findings indicate that the ensemble method enhances the predictive capability of PPIs, offering a promising tool for computational biology and drug discovery by accurately identifying potential interaction sites and facilitating the understanding of complex protein functions within biological systems.
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Affiliation(s)
- Nor Kumalasari Caecar Pratiwi
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea;
- Department of Electrical Engineering, Telkom University, Bandung 40257, West Java, Indonesia
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea;
- Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju 54896, Republic of Korea
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6
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Rashidi HH, Fennell BD, Albahra S, Hu B, Gorbett T. The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool. J Pathol Inform 2023; 14:100342. [PMID: 38116171 PMCID: PMC10727991 DOI: 10.1016/j.jpi.2023.100342] [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: 07/28/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 12/21/2023] Open
Abstract
AI Chat Bots such as ChatGPT are revolutionizing our AI capabilities, especially in text generation, to help expedite many tasks, but they introduce new dilemmas. The detection of AI-generated text has become a subject of great debate considering the AI text detector's known and unexpected limitations. Thus far, much research in this area has focused on the detection of AI-generated text; however, the goal of this study was to evaluate the opposite scenario, an AI-text detection tool's ability to discriminate human-generated text. Thousands of abstracts from several of the most well-known scientific journals were used to test the predictive capabilities of these detection tools, assessing abstracts from 1980 to 2023. We found that the AI text detector erroneously identified up to 8% of the known real abstracts as AI-generated text. This further highlights the current limitations of such detection tools and argues for novel detectors or combined approaches that can address this shortcoming and minimize its unanticipated consequences as we navigate this new AI landscape.
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Affiliation(s)
- Hooman H. Rashidi
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States
- PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Brandon D. Fennell
- University of California, San Francisco – Department of Medicine, San Francisco, CA, United States
| | - Samer Albahra
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States
- PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Bo Hu
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States
- PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Tom Gorbett
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States
- PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
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7
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Ivanenko M, Smolik WT, Wanta D, Midura M, Wróblewski P, Hou X, Yan X. Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax. SENSORS (BASEL, SWITZERLAND) 2023; 23:7774. [PMID: 37765831 PMCID: PMC10538128 DOI: 10.3390/s23187774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023]
Abstract
Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results.
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Affiliation(s)
- Mikhail Ivanenko
- Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland; (M.I.); (D.W.); (M.M.); (P.W.)
| | - Waldemar T. Smolik
- Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland; (M.I.); (D.W.); (M.M.); (P.W.)
| | - Damian Wanta
- Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland; (M.I.); (D.W.); (M.M.); (P.W.)
| | - Mateusz Midura
- Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland; (M.I.); (D.W.); (M.M.); (P.W.)
| | - Przemysław Wróblewski
- Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland; (M.I.); (D.W.); (M.M.); (P.W.)
| | - Xiaohan Hou
- Faculty of Electrical and Control Engineering, Liaoning Technical University, No. 188 Longwan Street, Huludao 125105, China; (X.H.); (X.Y.)
| | - Xiaoheng Yan
- Faculty of Electrical and Control Engineering, Liaoning Technical University, No. 188 Longwan Street, Huludao 125105, China; (X.H.); (X.Y.)
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8
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Loecher A, Bruyns-Haylett M, Ballester PJ, Borros S, Oliva N. A machine learning approach to predict cellular uptake of pBAE polyplexes. Biomater Sci 2023; 11:5797-5808. [PMID: 37401742 DOI: 10.1039/d3bm00741c] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
The delivery of genetic material (DNA and RNA) to cells can cure a wide range of diseases but is limited by the delivery efficiency of the carrier system. Poly β-amino esters (pBAEs) are promising polymer-based vectors that form polyplexes with negatively charged oligonucleotides, enabling cell membrane uptake and gene delivery. pBAE backbone polymer chemistry, as well as terminal oligopeptide modifications, define cellular uptake and transfection efficiency in a given cell line, along with nanoparticle size and polydispersity. Moreover, uptake and transfection efficiency of a given polyplex formulation also vary from cell type to cell type. Therefore, finding the optimal formulation leading to high uptake in a new cell line is dictated by trial and error, and requires time and resources. Machine learning (ML) is an ideal in silico screening tool to learn the non-linearities of complex data sets, like the one presented herein, with the aim of predicting cellular internalisation of pBAE polyplexes. A library of pBAE nanoparticles was fabricated and the uptake studied in 4 different cell lines, on which various ML models were successfully trained. The best performing models were found to be gradient-boosted trees and neural networks. The gradient-boosted trees model was then analysed using SHapley Additive exPlanations, to interpret the model and gain an understanding into the important features and their impact on the predicted outcome.
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Affiliation(s)
- Aparna Loecher
- Department of Bioengineering, Imperial College London, SW7 2AZ London, UK.
| | | | - Pedro J Ballester
- Department of Bioengineering, Imperial College London, SW7 2AZ London, UK.
| | - Salvador Borros
- Department of Bioengineering, Institut Quimic de Sarria, Via Augusta 390, 08017 Barcelona, Spain
| | - Nuria Oliva
- Department of Bioengineering, Imperial College London, SW7 2AZ London, UK.
- Department of Bioengineering, Institut Quimic de Sarria, Via Augusta 390, 08017 Barcelona, Spain
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9
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Gong L, Martinez O, Mesquita P, Kurtz K, Xu Y, Lin Y. A microfluidic approach for label-free identification of small-sized microplastics in seawater. Sci Rep 2023; 13:11011. [PMID: 37419935 PMCID: PMC10329028 DOI: 10.1038/s41598-023-37900-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023] Open
Abstract
Marine microplastics are emerging as a growing environmental concern due to their potential harm to marine biota. The substantial variations in their physical and chemical properties pose a significant challenge when it comes to sampling and characterizing small-sized microplastics. In this study, we introduce a novel microfluidic approach that simplifies the trapping and identification process of microplastics in surface seawater, eliminating the need for labeling. We examine various models, including support vector machine, random forest, convolutional neural network (CNN), and residual neural network (ResNet34), to assess their performance in identifying 11 common plastics. Our findings reveal that the CNN method outperforms the other models, achieving an impressive accuracy of 93% and a mean area under the curve of 98 ± 0.02%. Furthermore, we demonstrate that miniaturized devices can effectively trap and identify microplastics smaller than 50 µm. Overall, this proposed approach facilitates efficient sampling and identification of small-sized microplastics, potentially contributing to crucial long-term monitoring and treatment efforts.
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Affiliation(s)
- Liyuan Gong
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA
| | - Omar Martinez
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA
| | - Pedro Mesquita
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA
| | - Kayla Kurtz
- Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, RI, USA
| | - Yang Xu
- Department of Computer Science, San Diego State University, San Diego, CA, USA
| | - Yang Lin
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA.
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10
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Albahra S, Gorbett T, Robertson S, D'Aleo G, Kumar SVS, Ockunzzi S, Lallo D, Hu B, Rashidi HH. Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts. Semin Diagn Pathol 2023; 40:71-87. [PMID: 36870825 DOI: 10.1053/j.semdp.2023.02.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 02/17/2023]
Abstract
Machine learning (ML) is becoming an integral aspect of several domains in medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such tools and are unprepared for their inevitable integration. To bridge this knowledge gap, we present an overview of key elements within this emerging data science discipline. First, we will cover general, well-established concepts within ML, such as data type concepts, data preprocessing methods, and ML study design. We will describe common supervised and unsupervised learning algorithms and their associated common machine learning terms (provided within a comprehensive glossary of terms that are discussed within this review). Overall, this review will offer a broad overview of the key concepts and algorithms in machine learning, with a focus on pathology and laboratory medicine. The objective is to provide an updated useful reference for those new to this field or those who require a refresher.
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Affiliation(s)
- Samer Albahra
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States.
| | - Tom Gorbett
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Scott Robertson
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Giana D'Aleo
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Sushasree Vasudevan Suseel Kumar
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Samuel Ockunzzi
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Daniel Lallo
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Bo Hu
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Hooman H Rashidi
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States.
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Akshay A, Abedi M, Shekarchizadeh N, Burkhard FC, Katoch M, Bigger-Allen A, Adam RM, Monastyrskaya K, Gheinani AH. MLcps: machine learning cumulative performance score for classification problems. Gigascience 2022; 12:giad108. [PMID: 38091508 PMCID: PMC10716825 DOI: 10.1093/gigascience/giad108] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/02/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias. RESULTS We propose the Machine Learning Cumulative Performance Score (MLcps), a novel evaluation metric for classification problems. MLcps integrates several precomputed evaluation metrics into a unified score, enabling a comprehensive assessment of the trained model's strengths and weaknesses. We tested MLcps on 4 publicly available datasets, and the results demonstrate that MLcps provides a holistic evaluation of the model's robustness, ensuring a thorough understanding of its overall performance. CONCLUSIONS By utilizing MLcps, researchers and practitioners no longer need to individually examine and compare multiple metrics to identify the best-performing models. Instead, they can rely on a single MLcps value to assess the overall performance of their ML models. This streamlined evaluation process saves valuable time and effort, enhancing the efficiency of model evaluation. MLcps is available as a Python package at https://pypi.org/project/MLcps/.
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Affiliation(s)
- Akshay Akshay
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland
| | - Masoud Abedi
- Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany
| | - Navid Shekarchizadeh
- Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, 04105 Leipzig, Germany
| | - Fiona C Burkhard
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland
- Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland
| | - Mitali Katoch
- Institute of Neuropathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Alex Bigger-Allen
- Biological & Biomedical Sciences Program, Division of Medical Sciences, Harvard Medical School, 02115 Boston, MA, USA
- Urological Diseases Research Center, Boston Children's Hospital, 02115 Boston, MA, USA
- Department of Surgery, Harvard Medical School, 02115 Boston, MA, USA
- Broad Institute of MIT and Harvard, 02142 Cambridge, MA, USA
| | - Rosalyn M Adam
- Urological Diseases Research Center, Boston Children's Hospital, 02115 Boston, MA, USA
- Department of Surgery, Harvard Medical School, 02115 Boston, MA, USA
- Broad Institute of MIT and Harvard, 02142 Cambridge, MA, USA
| | - Katia Monastyrskaya
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland
- Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland
| | - Ali Hashemi Gheinani
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland
- Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland
- Urological Diseases Research Center, Boston Children's Hospital, 02115 Boston, MA, USA
- Department of Surgery, Harvard Medical School, 02115 Boston, MA, USA
- Broad Institute of MIT and Harvard, 02142 Cambridge, MA, USA
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