101
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Comparative Analysis of Human Operators and Advanced Technologies in the Visual Inspection of Aero Engine Blades. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12042250] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background—Aircraft inspection is crucial for safe flight operations and is predominantly performed by human operators, who are unreliable, inconsistent, subjective, and prone to err. Thus, advanced technologies offer the potential to overcome those limitations and improve inspection quality. Method—This paper compares the performance of human operators with image processing, artificial intelligence software and 3D scanning for different types of inspection. The results were statistically analysed in terms of inspection accuracy, consistency and time. Additionally, other factors relevant to operations were assessed using a SWOT and weighted factor analysis. Results—The results show that operators’ performance in screen-based inspection tasks was superior to inspection software due to their strong cognitive abilities, decision-making capabilities, versatility and adaptability to changing conditions. In part-based inspection however, 3D scanning outperformed the operator while being significantly slower. Overall, the strength of technological systems lies in their consistency, availability and unbiasedness. Conclusions—The performance of inspection software should improve to be reliably used in blade inspection. While 3D scanning showed the best results, it is not always technically feasible (e.g., in a borescope inspection) nor economically viable. This work provides a list of evaluation criteria beyond solely inspection performance that could be considered when comparing different inspection systems.
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102
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Dai C, Sun B, Wang R, Kang J. The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas. Front Oncol 2022; 11:784819. [PMID: 35004306 PMCID: PMC8733587 DOI: 10.3389/fonc.2021.784819] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/02/2021] [Indexed: 12/28/2022] Open
Abstract
Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients.
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Affiliation(s)
- Congxin Dai
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Bowen Sun
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Kang
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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103
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Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, Kunze J, Rossaint R, Riedel M, Marx G, Bickenbach J. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digit Health 2022; 8:20552076221116772. [PMID: 35983102 PMCID: PMC9380417 DOI: 10.1177/20552076221116772] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/13/2022] [Indexed: 12/23/2022] Open
Abstract
Objective The attitudes about the usage of artificial intelligence in healthcare are
controversial. Unlike the perception of healthcare professionals, the
attitudes of patients and their companions have been of less interest so
far. In this study, we aimed to investigate the perception of artificial
intelligence in healthcare among this highly relevant group along with the
influence of digital affinity and sociodemographic factors. Methods We conducted a cross-sectional study using a paper-based questionnaire with
patients and their companions at a German tertiary referral hospital from
December 2019 to February 2020. The questionnaire consisted of three
sections examining (a) the respondents’ technical affinity, (b) their
perception of different aspects of artificial intelligence in healthcare and
(c) sociodemographic characteristics. Results From a total of 452 participants, more than 90% already read or heard about
artificial intelligence, but only 24% reported good or expert knowledge.
Asked on their general perception, 53.18% of the respondents rated the use
of artificial intelligence in medicine as positive or very positive, but
only 4.77% negative or very negative. The respondents denied concerns about
artificial intelligence, but strongly agreed that artificial intelligence
must be controlled by a physician. Older patients, women, persons with lower
education and technical affinity were more cautious on the
healthcare-related artificial intelligence usage. Conclusions German patients and their companions are open towards the usage of artificial
intelligence in healthcare. Although showing only a mediocre knowledge about
artificial intelligence, a majority rated artificial intelligence in
healthcare as positive. Particularly, patients insist that a physician
supervises the artificial intelligence and keeps ultimate responsibility for
diagnosis and therapy.
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Affiliation(s)
- Sebastian J Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
- Juelich Supercomputing Centre, Forschungszentrum Juelich, Germany
| | - Andrea Blankenheim
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
| | - Alina Wahl
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
| | - Petra Hetfeld
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Oliver Maassen
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Saskia Deffge
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Julian Kunze
- SMITH Consortium of the German Medical Informatics Initiative, Germany
- Department of Anesthesiology, University Hospital RWTH Aachen, Germany
| | - Rolf Rossaint
- Department of Anesthesiology, University Hospital RWTH Aachen, Germany
| | - Morris Riedel
- SMITH Consortium of the German Medical Informatics Initiative, Germany
- Juelich Supercomputing Centre, Forschungszentrum Juelich, Germany
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Iceland
| | - Gernot Marx
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Germany
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104
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Taylor AM. The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging. Pediatr Radiol 2022; 52:2131-2138. [PMID: 34936019 PMCID: PMC9537201 DOI: 10.1007/s00247-021-05218-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/13/2021] [Accepted: 10/05/2021] [Indexed: 11/24/2022]
Abstract
Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience.
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Affiliation(s)
- Andrew M. Taylor
- Great Ormond Street Hospital for Children, Zayed Centre for Research, 20 Guildford St., Room 3.7, London, WC1N 1DZ UK ,Cardiovascular Imaging, UCL Institute of Cardiovascular Science, London, UK
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105
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de Bem Machado A, Secinaro S, Calandra D, Lanzalonga F. Knowledge management and digital transformation for Industry 4.0: a structured literature review. KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE 2021. [DOI: 10.1080/14778238.2021.2015261] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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106
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Pandya S, Thakur A, Saxena S, Jassal N, Patel C, Modi K, Shah P, Joshi R, Gonge S, Kadam K, Kadam P. A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions. SENSORS (BASEL, SWITZERLAND) 2021; 21:7786. [PMID: 34883787 PMCID: PMC8659723 DOI: 10.3390/s21237786] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/17/2021] [Accepted: 11/21/2021] [Indexed: 12/13/2022]
Abstract
The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.
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Affiliation(s)
- Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Aanchal Thakur
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Santosh Saxena
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Nandita Jassal
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Chirag Patel
- Computer Science & Engineering, Devang Patel Institute of Advance Technology and Research, Changa 388421, India;
| | - Kirit Modi
- Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar 384315, India;
| | - Pooja Shah
- Information Technology Department, Gandhinagar Institute of Technology, Ahmedabad 382010, India;
| | - Rahul Joshi
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Sudhanshu Gonge
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Kalyani Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Prachi Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
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107
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Lim MJR. Letter: Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery 2021; 89:E333-E334. [PMID: 34498686 DOI: 10.1093/neuros/nyab337] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Affiliation(s)
- Mervyn J R Lim
- Division of Neurosurgery University Surgical Centre National University Hospital Singapore
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108
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Sargent CS. Replacing Financial Audits with Blockchain: The Verification Issue. JOURNAL OF COMPUTER INFORMATION SYSTEMS 2021. [DOI: 10.1080/08874417.2021.1992805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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109
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Benis A, Chatsubi A, Levner E, Ashkenazi S. Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence-Based Infodemiology Study. ACTA ACUST UNITED AC 2021; 1:e31983. [PMID: 34693212 PMCID: PMC8521455 DOI: 10.2196/31983] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/05/2021] [Accepted: 09/18/2021] [Indexed: 12/14/2022]
Abstract
Background Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. Objective Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. Methods The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.
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Affiliation(s)
- Arriel Benis
- Faculty of Industrial Engineering and Technology Management Holon Institute of Technology Holon Israel.,Faculty of Digital Technologies in Medicine Holon Institute of Technology Holon Israel
| | - Anat Chatsubi
- Faculty of Industrial Engineering and Technology Management Holon Institute of Technology Holon Israel
| | - Eugene Levner
- Faculty of Sciences Holon Institute of Technology Holon Israel
| | - Shai Ashkenazi
- Adelson School of Medicine Ariel University Ariel Israel
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110
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Taha-Mehlitz S, Hendie A, Taha A. The Development of Electronic Health and Artificial Intelligence in Surgery after the SARS-CoV-2 Pandemic-A Scoping Review. J Clin Med 2021; 10:jcm10204789. [PMID: 34682912 PMCID: PMC8537136 DOI: 10.3390/jcm10204789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/13/2021] [Accepted: 10/18/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND SARS-CoV-2 has significantly transformed the healthcare environment, and it has triggered the development of electronic health and artificial intelligence mechanisms, for instance. In this overview, we concentrated on enhancing the two concepts in surgery after the pandemic, and we examined the factors on a global scale. OBJECTIVE The primary goal of this scoping review is to elaborate on how surgeons have used eHealth and AI before; during; and after the current global pandemic. More specifically, this review focuses on the empowerment of the concepts of electronic health and artificial intelligence after the pandemic; which mainly depend on the efforts of countries to advance the notions of surgery. DESIGN The use of an online search engine was the most applied method. The publication years of all the studies included in the study ranged from 2013 to 2021. Out of the reviewed studies; forty-four qualified for inclusion in the review. DISCUSSION We evaluated the prevalence of the concepts in different continents such as the United States; Europe; Asia; the Middle East; and Africa. Our research reveals that the success of eHealth and artificial intelligence adoption primarily depends on the efforts of countries to advance the notions in surgery. CONCLUSIONS The study's primary limitation is insufficient information on eHealth and artificial intelligence concepts; particularly in developing nations. Future research should focus on establishing methods of handling eHealth and AI challenges around confidentiality and data security.
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Affiliation(s)
- Stephanie Taha-Mehlitz
- Clarunis, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, 4002 Basel, Switzerland;
| | - Ahmad Hendie
- Department of Computer Engineering, McGill University, Montreal, QC H3A 0C6, Canada;
| | - Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, 4321 Allschwil, Switzerland
- Correspondence: ; Tel.: +41-61-207-54-02
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111
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Aznar-Gimeno R, Esteban LM, Labata-Lezaun G, del-Hoyo-Alonso R, Abadia-Gallego D, Paño-Pardo JR, Esquillor-Rodrigo MJ, Lanas Á, Serrano MT. A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8677. [PMID: 34444425 PMCID: PMC8394359 DOI: 10.3390/ijerph18168677] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 12/24/2022]
Abstract
The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787-0.854) and accurate calibration (slope = 1, intercept = -0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice.
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Affiliation(s)
- Rocío Aznar-Gimeno
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - Luis M. Esteban
- Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor, 5, 50100 La Almunia de Doña Godina, Spain;
| | - Gorka Labata-Lezaun
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - Rafael del-Hoyo-Alonso
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - David Abadia-Gallego
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - J. Ramón Paño-Pardo
- Infectious Disease Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain;
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain; (Á.L.); (M.T.S.)
- Aragon Health Research Institute (IIS Aragon), 50009 Zaragoza, Spain
| | - M. José Esquillor-Rodrigo
- Internal Medicine Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain;
| | - Ángel Lanas
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain; (Á.L.); (M.T.S.)
- Aragon Health Research Institute (IIS Aragon), 50009 Zaragoza, Spain
- Digestive Diseases Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain
- CIBEREHD, 28029 Madrid, Spain
| | - M. Trinidad Serrano
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain; (Á.L.); (M.T.S.)
- Aragon Health Research Institute (IIS Aragon), 50009 Zaragoza, Spain
- Digestive Diseases Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain
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112
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Joo MI, Aich S, Kim HC. Development of a System for Storing and Executing Bio-Signal Analysis Algorithms Developed in Different Languages. Healthcare (Basel) 2021; 9:healthcare9081016. [PMID: 34442153 PMCID: PMC8394268 DOI: 10.3390/healthcare9081016] [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: 07/08/2021] [Revised: 08/01/2021] [Accepted: 08/03/2021] [Indexed: 11/16/2022] Open
Abstract
With the development of mobile and wearable devices with biosensors, various healthcare services in our life have been recently introduced. A significant issue that arises supports the smart interface among bio-signals developed by different vendors and different languages. Despite its importance for convenient and effective development, however, it has been nearly unexplored. This paper focuses on the smart interface format among bio-signal data processing and mining algorithms implemented by different languages. We designed and implemented an advanced software structure where analysis algorithms implemented by different languages and tools would seem to work in one common environment, overcoming different developing language barriers. By presenting our design in this paper, we hope there will be much more chances for higher service-oriented developments utilizing bio-signals in the future.
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Affiliation(s)
- Moon-Il Joo
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae-si 50834, Korea;
| | - Satyabrata Aich
- Department of Computer Engineering, Inje University, Gimhae-si 50834, Korea;
| | - Hee-Cheol Kim
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae-si 50834, Korea;
- Department of Computer Engineering, Inje University, Gimhae-si 50834, Korea;
- Correspondence: ; Tel.: +82-55-320-3720
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113
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Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data. ELECTRONICS 2021. [DOI: 10.3390/electronics10141626] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.
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114
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Tseng RMWW, Tham YC, Rim TH, Cheng CY. Emergence of non-artificial intelligence digital health innovations in ophthalmology: A systematic review. Clin Exp Ophthalmol 2021; 49:741-756. [PMID: 34235833 DOI: 10.1111/ceo.13971] [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: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 11/30/2022]
Abstract
The prominent rise of digital health in ophthalmology is evident in the current age of Industry 4.0. Despite the many facets of digital health, there has been a greater slant in interest and focus on artificial intelligence recently. Other major elements of digital health like wearables could also substantially impact patient-focused outcomes but have been relatively less explored and discussed. In this review, we comprehensively evaluate the use of non-artificial intelligence digital health tools in ophthalmology. 53 papers were included in this systematic review - 25 papers discuss virtual or augmented reality, 14 discuss mobile applications and 14 discuss wearables. Most papers focused on the use of technologies to detect or rehabilitate visual impairment, glaucoma and age-related macular degeneration. Overall, the findings on patient-focused outcomes with the adoption of these technologies are encouraging. Further validation, large-scale studies and earlier consideration of real-world barriers are warranted to enable better real-world implementation.
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Affiliation(s)
| | - Yih-Chung Tham
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore.,Duke-NUS Medical School, Singapore
| | - Tyler Hyungtaek Rim
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore.,Duke-NUS Medical School, Singapore
| | - Ching-Yu Cheng
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore.,Duke-NUS Medical School, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
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Nakajima K, Igata M, Higuchi R. Potential Role of Artificial Intelligence for the Previous Study Using Traditional Analysis. J Clin Med Res 2021; 13:409-411. [PMID: 34394784 PMCID: PMC8336939 DOI: 10.14740/jocmr4568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 07/26/2021] [Indexed: 02/05/2023] Open
Affiliation(s)
- Kei Nakajima
- School of Nutrition and Dietetics, Faculty of Health and Social Services, Kanagawa University of Human Services, 1-10-1 Heisei-cho, Yokosuka, Kanagawa 238-8522, Japan
- Department of Endocrinology and Diabetes, Saitama Medical Center, Saitama Medical University, 1981 Kamoda, Kawagoe, Saitama 350-8550, Japan
- Corresponding Author: Kei Nakajima, School of Nutrition and Dietetics, Faculty of Health and Social Services, Kanagawa University of Human Services, 1-10-1 Heisei-cho, Yokosuka, Kanagawa 238-8522, Japan.
| | - Manami Igata
- School of Nutrition and Dietetics, Faculty of Health and Social Services, Kanagawa University of Human Services, 1-10-1 Heisei-cho, Yokosuka, Kanagawa 238-8522, Japan
| | - Ryoko Higuchi
- School of Nutrition and Dietetics, Faculty of Health and Social Services, Kanagawa University of Human Services, 1-10-1 Heisei-cho, Yokosuka, Kanagawa 238-8522, Japan
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