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Smielewski P, Beqiri E, Mataczynski C, Placek M, Kazimierska A, Hutchinson P, Czosnyka M, Kasprowicz M. Advanced neuromonitoring powered by ICM+ and its place in the Brand New AI World, reflections at the 20th anniversary boundary. BRAIN & SPINE 2024; 4:102835. [PMID: 39071453 PMCID: PMC11278591 DOI: 10.1016/j.bas.2024.102835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 05/06/2024] [Accepted: 05/17/2024] [Indexed: 07/30/2024]
Abstract
Introduction Adoption of the ICM+® brain monitoring software by clinical research centres worldwide has been continuously growing over the past 20 years. This has necessitated ongoing updates to accommodate evolving neuromonitoring research needs, including recent explosion of artificial intelligence (AI). Research question We sought to provide an update on the current features of the software. In particular, we aimed to highlight the new options of integrating AI models. Material and methods We reviewed all currently available ICM+ analytical areas and discussed potential AI based extensions in each. We tested a proof-of-concept integration of an AI model and evaluated its performance for real-time data processing. Results ICM+ current analytical tools serve both real-time (bed-side) and offline (file based) analysis, including the calculation engine, Signal Calculator, Custom Statistics, Batch tools, ScriptLab and charting. The ICM+ Python plugin engine allows to execute custom Python scripts and take advantage of complex AI frameworks. For the proof-of-concept, we used a neural network convolutional model with 207,000 trainable parameters that classifies morphology of intracranial pressure (ICP) pulse waveform into 5 pulse categories (normal to pathological plus artefactual). When evaluated within ICM+ plugin script on a Windows 10 laptop the classification of a 5 min ICP waveform segment took only 0.19s with a 2.3s of initial, one-off, model loading time required. Conclusions Modernised ICM+ analytical tools, reviewed in this manuscript, include integration of custom AI models allowing them to be shared and run in real-time, facilitating rapid prototyping and validating of new AI ideas at the bed-side.
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Affiliation(s)
- P. Smielewski
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - E. Beqiri
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - C. Mataczynski
- Department of Computer Engineering, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
| | - M. Placek
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - A. Kazimierska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
| | - P.J. Hutchinson
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Computer Engineering, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
- Neurosurgery Department, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - M. Czosnyka
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - M. Kasprowicz
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland
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2
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Chang H, Choi JY, Shim J, Kim M, Choi M. Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records. Healthc Inform Res 2023; 29:323-333. [PMID: 37964454 PMCID: PMC10651408 DOI: 10.4258/hir.2023.29.4.323] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVES Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence. METHODS The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains. RESULTS Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied. CONCLUSIONS Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.
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Affiliation(s)
- Hyejung Chang
- Department of Management, School of Management, Kyung Hee University, Seoul,
Korea
| | - Jae-Young Choi
- Department of Business Administration, College of Business, Hallym University, Chuncheon,
Korea
| | - Jaesun Shim
- Department of Municipal Hospital Policy & Management, Seoul Health Foundation, Seoul,
Korea
| | - Mihui Kim
- Department of Nursing Science, Jeonju University, Jeonju,
Korea
| | - Mona Choi
- College of Nursing, Mo-Im Kim Nursing Research Institute, Yonsei University, Seoul,
Korea
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3
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Hofmann B. Temporal uncertainty in disease diagnosis. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2023; 26:401-411. [PMID: 37222967 PMCID: PMC10425509 DOI: 10.1007/s11019-023-10154-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/14/2023] [Indexed: 05/25/2023]
Abstract
There is a profound paradox in modern medical knowledge production: The more we know, the more we know that we (still) do not know. Nowhere is this more visible than in diagnostics and early detection of disease. As we identify ever more markers, predictors, precursors, and risk factors of disease ever earlier, we realize that we need knowledge about whether they develop into something experienced by the person and threatening to the person's health. This study investigates how advancements in science and technology alter one type of uncertainty, i.e., temporal uncertainty of disease diagnosis. As diagnosis is related to anamnesis and prognosis it identifies how uncertainties in all these fields are interconnected. In particular, the study finds that uncertainty in disease diagnosis has become more subject to prognostic uncertainty because diagnosis is more connected to technologically detected indicators and less closely connected to manifest and experienced disease. These temporal uncertainties pose basic epistemological and ethical challenges as they can result in overdiagnosis, overtreatment, unnecessary anxiety and fear, useless and even harmful diagnostic odysseys, as well as vast opportunity costs. The point is not to stop our quest for knowledge about disease but to encourage real diagnostic improvements that help more people in ever better manner as early as possible. To do so, we need to pay careful attention to specific types of temporal uncertainty in modern diagnostics.
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Affiliation(s)
- Bjørn Hofmann
- Centre for Medical Ethics, Institute for Health and Society, Faculty of Medicine, PO Box 1130, Oslo, N-0318, Norway.
- Institute of the Health Sciences, The Norwegian University of Science and Technology (NTNU), Gjøvik, Norway.
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4
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Long H, Li S, Chen Y. Digital health in chronic obstructive pulmonary disease. Chronic Dis Transl Med 2023; 9:90-103. [PMID: 37305103 PMCID: PMC10249197 DOI: 10.1002/cdt3.68] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/11/2023] [Accepted: 04/03/2023] [Indexed: 06/13/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.
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Affiliation(s)
- Huanyu Long
- Department of Pulmonary and Critical Care MedicinePeking University Third HospitalBeijingChina
| | - Shurun Li
- Peking University Health Science CenterBeijingChina
| | - Yahong Chen
- Department of Pulmonary and Critical Care MedicinePeking University Third HospitalBeijingChina
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5
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Bettauer V, Costa ACBP, Omran RP, Massahi S, Kirbizakis E, Simpson S, Dumeaux V, Law C, Whiteway M, Hallett MT. A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies. Microbiol Spectr 2022; 10:e0147222. [PMID: 35972285 PMCID: PMC9604015 DOI: 10.1128/spectrum.01472-22] [Citation(s) in RCA: 3] [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: 04/26/2022] [Accepted: 07/25/2022] [Indexed: 12/31/2022] Open
Abstract
We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen Candida albicans. Our system, entitled Candescence, automatically detects C. albicans cells from differential image contrast microscopy and labels each detected cell with one of nine morphologies. This ranges from yeast white and opaque forms to hyphal and pseudohyphal filamentous morphologies. The software is based upon a fully convolutional one-stage (FCOS) object detector, a deep learning technique that uses an extensive set of images that we manually annotated with the location and morphology of each cell. We developed a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple yeast forms to complex filamentous architectures. Candescence achieves very good performance (~85% recall; 81% precision) on this difficult learning set, where some images contain hundreds of cells with substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology and how they intermix, we used a second technique from deep learning entitled generative adversarial networks. The resultant models allow us to identify and explore technical variables, developmental trajectories, and morphological switches. Importantly, the model allows us to quantitatively capture morphological plasticity observed with genetically modified strains or strains grown in different media and environments. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology. IMPORTANCE The fungus Candida albicans can "shape shift" between 12 morphologies in response to environmental variables. The cytoprotective capacity provided by this polymorphism makes C. albicans a formidable pathogen to treat clinically. Microscopy images of C. albicans colonies can contain hundreds of cells in different morphological states. Manual annotation of images can be difficult, especially as a result of densely packed and filamentous colonies and of technical artifacts from the microscopy itself. Manual annotation is inherently subjective, depending on the experience and opinion of annotators. Here, we built a deep learning approach entitled Candescence to parse images in an automated, quantitative, and objective fashion: each cell in an image is located and labeled with its morphology. Candescence effectively replaces simple rules based on visual phenotypes (size, shape, and shading) with neural circuitry capable of capturing subtle but salient features in images that may be too complex for human annotators.
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Affiliation(s)
- Van Bettauer
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada
| | | | | | - Samira Massahi
- Department of Biology, Concordia University, Montreal, Quebec, Canada
| | | | - Shawn Simpson
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada
| | - Vanessa Dumeaux
- Department of Anatomy and Cell Biology, Western University, London, Ontario, Canada
| | - Chris Law
- Centre for Microscopy and Cellular Imaging, Concordia University, Montreal, Quebec, Canada
| | - Malcolm Whiteway
- Department of Biology, Concordia University, Montreal, Quebec, Canada
| | - Michael T. Hallett
- Department of Biology, Concordia University, Montreal, Quebec, Canada
- Department of Biochemistry, Western University, London, Ontario, Canada
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6
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Pisapia P, L'Imperio V, Galuppini F, Sajjadi E, Russo A, Cerbelli B, Fraggetta F, d'Amati G, Troncone G, Fassan M, Fusco N, Pagni F, Malapelle U. The evolving landscape of anatomic pathology. Crit Rev Oncol Hematol 2022; 178:103776. [PMID: 35934262 DOI: 10.1016/j.critrevonc.2022.103776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 12/11/2022] Open
Abstract
Anatomic pathology has changed dramatically in recent years. Although the microscopic assessment of tissues and cells is and will remain the mainstay of cancer diagnosis molecular profiling has become equally relevant. Thus, to stay abreast of the evolving landscape of today's anatomic pathology, modern pathologists must be able to master the intricate world of predictive molecular pathology. To this aim, pathologists have had to acquire additional knowledge to bridge the gap between clinicians and molecular biologists. This new role is particularly important, as cases are now collegially discussed in molecular tumor boards (MTBs). Moreover, as opposed to traditional pathologists, modern pathologists have also adamantly embraced innovation while keeping a constant eye on tradition. In this article, we depict the highlights and shadows of the upcoming "Anatomic Pathology 2.0" by placing particular emphasis on the pathologist's growing role in the management of cancer patients.
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Affiliation(s)
- Pasquale Pisapia
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | - Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, University of Milan-Bicocca (UNIMIB), Monza, Italy
| | - Francesca Galuppini
- Unit of Surgical Pathology, Department of Medicine (DIMED), University of Padua, Padua, Italy
| | - Elham Sajjadi
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Bruna Cerbelli
- Department of Radiology, Oncology and Pathology, Sapienza, University of Rome, Rome, Italy
| | - Filippo Fraggetta
- Pathology Unit, Gravina Hospital Caltagirone, ASP Catania, Caltagirone, Italy
| | - Giulia d'Amati
- Department of Radiology, Oncology and Pathology, Sapienza, University of Rome, Rome, Italy
| | - Giancarlo Troncone
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | - Matteo Fassan
- Unit of Surgical Pathology, Department of Medicine (DIMED), University of Padua, Padua, Italy; Veneto Institute of Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Padua, Veneto, Italy.
| | - Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, University of Milan-Bicocca (UNIMIB), Monza, Italy
| | - Umberto Malapelle
- Department of Public Health, University of Naples Federico II, Naples, Italy
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7
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Bak M, Madai VI, Fritzsche MC, Mayrhofer MT, McLennan S. You Can't Have AI Both Ways: Balancing Health Data Privacy and Access Fairly. Front Genet 2022; 13:929453. [PMID: 35769991 PMCID: PMC9234328 DOI: 10.3389/fgene.2022.929453] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used “consent or anonymize approach” undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The “AI revolution” in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.
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Affiliation(s)
- Marieke Bak
- Department of Ethics, Law and Humanities, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Vince Istvan Madai
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany.,School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
| | - Marie-Christine Fritzsche
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Michaela Th Mayrhofer
- ELSI Services and Research, Biobanking and BioMolecular Resources Research Infrastructure European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Stuart McLennan
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany
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8
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Abstract
The artificial neural network (ANN) is a computer software design or model that simulates the biological neural network of the human brain. Instead of biological neurons, ANN is composed of many layers of nodes that carry the signal and process it to make the final decision. ANN is a modern technology that is widely used in different fields of science. The ANN is reshaping the medical system and the various areas of pathology. In this paper, the basic concept and applications of ANN in cytology have been discussed. In this paper, the various articles published on ANN in the field of cytology have been systemically reviewed. The ANN is relatively less used in cytology. After introducing convolutional neural network and whole slide scanners in the commercial market, it is now essential to have thorough knowledge in this field to start diagnostic application of ANN.
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9
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Artificial Intelligence in Cardiovascular Medicine - Are we ready? Trends Cardiovasc Med 2022:S1050-1738(22)00037-8. [PMID: 35217178 DOI: 10.1016/j.tcm.2022.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 02/15/2022] [Indexed: 11/20/2022]
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10
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Abstract
As resources in the healthcare environment continue to wane, leaders are seeking ways to continue to provide quality care bounded by the constraints of a reduced budget. This manuscript synthesizes the experience from a number of institutions to provide the healthcare leadership with an understanding of the value of an enterprise imaging program. The value of such a program extends across the entire health system. It leads to operational efficiencies through infrastructure and application consolidation and the creation of focused support capabilities with increased depth of skill. An enterprise imaging program provides a centralized foundation for all phases of image management from every image-producing specialty. Through centralization, standardized image exchange functions can be provided to all image producers. Telehealth services can be more tightly integrated into the electronic medical record. Mobile platforms can be utilized for image viewing and sharing by patients and providers. Mobile tools can also be utilized for image upload directly into the centralized image repository. Governance and data standards are more easily distributed, setting the stage for artificial intelligence and data analytics. Increased exposure to all image producers provides opportunities for cybersecurity optimization and increased awareness.
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11
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Cui M, Zhang DY. Artificial intelligence and computational pathology. J Transl Med 2021; 101:412-422. [PMID: 33454724 PMCID: PMC7811340 DOI: 10.1038/s41374-020-00514-0] [Citation(s) in RCA: 218] [Impact Index Per Article: 54.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/08/2020] [Accepted: 11/10/2020] [Indexed: 02/07/2023] Open
Abstract
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.
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Affiliation(s)
- Miao Cui
- St. Luke's Roosevelt Hospital Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10025, USA
| | - David Y Zhang
- Pathology and Laboratory Services, VA Medical Center, New York, NY, 10010, USA.
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Chen K, Zhai X, Sun K, Wang H, Yang C, Li M. A narrative review of machine learning as promising revolution in clinical practice of scoliosis. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:67. [PMID: 33553360 PMCID: PMC7859734 DOI: 10.21037/atm-20-5495] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 11/27/2020] [Indexed: 11/06/2022]
Abstract
Machine learning (ML), as an advanced domain of artificial intelligence (AI), is progressively changing our view of the world. By implementing its algorithms, our ability to detect previously undiscoverable patterns in data has the potential to revolutionize predictive analytics. Scoliosis, as a relatively specialized branch in the spine field, mainly covers the pediatric, adult and the elderly populations, and its diagnosis and treatment remain difficult. With recent efforts and interdisciplinary cooperation, ML has been widely applied to investigate issues related to scoliosis, and surprisingly augment a surgeon's ability in clinical practice related to scoliosis. Meanwhile, ML models penetrate in every stage of the clinical practice procedure of scoliosis. In this review, we first present a brief description of the application of ML in the clinical practice procedures regarding scoliosis, including screening, diagnosis and classification, surgical decision making, intraoperative manipulation, complication prediction, prognosis prediction and rehabilitation. Meanwhile, the ML models and specific applications adopted are presented. Additionally, current limitations and future directions are briefly discussed regarding its use in the field of scoliosis. We believe that the implementation of ML is a promising revolution to assist surgeons in all aspects of clinical practice related to scoliosis in the near future.
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Affiliation(s)
- Kai Chen
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Xiao Zhai
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Kaiqiang Sun
- Department of Orthopedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Haojue Wang
- Basic medicine college, Navy Medical University, Shanghai, China
| | - Changwei Yang
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Ming Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
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13
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Pieszko K, Hiczkiewicz J, Budzianowski J, Musielak B, Hiczkiewicz D, Faron W, Rzeźniczak J, Burchardt P. Clinical applications of artificial intelligence in cardiology on the verge of the decade. Cardiol J 2020; 28:460-472. [PMID: 32648252 DOI: 10.5603/cj.a2020.0093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/29/2020] [Accepted: 05/25/2020] [Indexed: 11/25/2022] Open
Abstract
Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people's lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise - acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools.
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Affiliation(s)
- Konrad Pieszko
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland. .,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland.
| | - Jarosław Hiczkiewicz
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Jan Budzianowski
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Bogdan Musielak
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Dariusz Hiczkiewicz
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Wojciech Faron
- Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Janusz Rzeźniczak
- Józefa Strusia Hospital, Cardiology Clinic, Szwajcarska 3,, 61-285 Poznań, Poland
| | - Paweł Burchardt
- Józefa Strusia Hospital, Cardiology Clinic, Szwajcarska 3,, 61-285 Poznań, Poland.,Department of Biology and Environmental Protection, Poznań University of Medical Sciences, ul. Rokietnicka 8, 60-806 Poznań, Poland
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14
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Big data and machine learning algorithms for health-care delivery. Lancet Oncol 2020; 20:e262-e273. [PMID: 31044724 DOI: 10.1016/s1470-2045(19)30149-4] [Citation(s) in RCA: 637] [Impact Index Per Article: 127.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 03/18/2019] [Accepted: 03/18/2019] [Indexed: 02/06/2023]
Abstract
Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.
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Bhattad PB, Jain V. Artificial Intelligence in Modern Medicine - The Evolving Necessity of the Present and Role in Transforming the Future of Medical Care. Cureus 2020; 12:e8041. [PMID: 32528777 PMCID: PMC7282357 DOI: 10.7759/cureus.8041] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The dexterity of computer systems to resemble and mimic human intelligence is artificial intelligence. Artificial intelligence has reformed the diagnostic and therapeutic precision and competence in various fields of medicine. Artificial intelligence appears to play a bright role in medical diagnosis. Computer systems using artificial intelligence help in the assessment of medical images and enormous data. This research aims to identify how artificial intelligence-based technology is reforming the art of medicine. Artificial intelligence empowers providers in improving efficiency and overall healthcare. Newer machine learning techniques lead the automatic diagnostic systems. Areas of medicine such as medical imaging, automated clinical decision-making support have made significant advances with respect to artificial intelligence technology. With improved diagnosis and prognosis, artificial intelligence possesses the capability to revolutionize various fields of medicine. Artificial intelligence has its own limitations and cannot replace a bedside clinician. In the evolving modern medical digital world, physicians need to support artificial intelligence rather than fear it replacing trained physicians for improved healthcare.
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Affiliation(s)
| | - Vinay Jain
- Radiology, James H. Quillen Veterans Affairs Medical Center, Johnson City, USA
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A machine learning approach for the prediction of pulmonary hypertension. PLoS One 2019; 14:e0224453. [PMID: 31652290 PMCID: PMC6814224 DOI: 10.1371/journal.pone.0224453] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 10/14/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters. METHODS In our database of 90 patients with invasively determined pulmonary artery pressure (PAP) with corresponding echocardiographic estimations of PAP obtained within 24 hours, we trained and applied five ML algorithms (random forest of classification trees, random forest of regression trees, lasso penalized logistic regression, boosted classification trees, support vector machines) using a 10 times 3-fold cross-validation (CV) scheme. RESULTS ML algorithms achieved high prediction accuracies: support vector machines (AUC 0.83; 95% CI 0.73-0.93), boosted classification trees (AUC 0.80; 95% CI 0.68-0.92), lasso penalized logistic regression (AUC 0.78; 95% CI 0.67-0.89), random forest of classification trees (AUC 0.85; 95% CI 0.75-0.95), random forest of regression trees (AUC 0.87; 95% CI 0.78-0.96). In contrast to the best of several conventional formulae (by Aduen et al.), this ML algorithm is based on several echocardiographic signs and feature selection, with estimated right atrial pressure (RAP) being of minor importance. CONCLUSIONS Using ML, we were able to predict pulmonary hypertension based on a broader set of echocardiographic data with little reliance on estimated RAP compared to an existing formula with non-inferior performance. With the conceptual advantages of a broader and unbiased selection and weighting of data our ML approach is suited for high level assistance in PH prediction.
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Tizhoosh HR, Pantanowitz L. Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform 2018; 9:38. [PMID: 30607305 PMCID: PMC6289004 DOI: 10.4103/jpi.jpi_53_18] [Citation(s) in RCA: 253] [Impact Index Per Article: 36.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 08/27/2018] [Indexed: 12/13/2022] Open
Abstract
In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI potentials in computational pathology. In this paper, we strive to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.
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Affiliation(s)
- Hamid Reza Tizhoosh
- Kimia Lab, University of Waterloo, Canada.,Huron Digital Pathology, Engineering Department, St. Jacobs, ON, Canada
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA 15261, USA
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