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Holm S. Ethical trade-offs in AI for mental health. Front Psychiatry 2024; 15:1407562. [PMID: 39267699 PMCID: PMC11390554 DOI: 10.3389/fpsyt.2024.1407562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 07/15/2024] [Indexed: 09/15/2024] Open
Abstract
It is expected that machine learning algorithms will enable better diagnosis, prognosis, and treatment in psychiatry. A central argument for deploying algorithmic methods in clinical decision-making in psychiatry is that they may enable not only faster and more accurate clinical judgments but also that they may provide a more objective foundation for clinical decisions. This article argues that the outputs of algorithms are never objective in the sense of being unaffected by human values and possibly biased choices. And it suggests that the best way to approach this is to ensure awareness of and transparency about the ethical trade-offs that must be made when developing an algorithm for mental health.
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Affiliation(s)
- Sune Holm
- Department of Food and Resource Economics, University of Copenhagen, Frederiksberg, Denmark
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2
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Al-Anazi S, Al-Omari A, Alanazi S, Marar A, Asad M, Alawaji F, Alwateid S. Artificial intelligence in respiratory care: Current scenario and future perspective. Ann Thorac Med 2024; 19:117-130. [PMID: 38766378 PMCID: PMC11100474 DOI: 10.4103/atm.atm_192_23] [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: 08/13/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.
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Affiliation(s)
- Saad Al-Anazi
- Lead Clincial Appliaction AzeerTrade (Lowenstein Medical Company), Riyadh, Saudi Arabia
| | - Awad Al-Omari
- Department of Intensive Care, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Safug Alanazi
- Intensivist, Al Hammadi Hospital, Riyadh, Saudi Arabia
| | - Aqeelah Marar
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Mohammed Asad
- Department of Emergency, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Fadi Alawaji
- Ar Rass General Hospital, Qassim Health Cluster, Senior Laboratory Specialist, Rass Region, Qassim City, Saudi Arabia
| | - Salman Alwateid
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
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Chang M, Reicher JJ, Kalra A, Muelly M, Ahmad Y. Analysis of Validation Performance of a Machine Learning Classifier in Interstitial Lung Disease Cases Without Definite or Probable Usual Interstitial Pneumonia Pattern on CT Using Clinical and Pathology-Supported Diagnostic Labels. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:297-307. [PMID: 38343230 DOI: 10.1007/s10278-023-00914-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/17/2023] [Accepted: 08/10/2023] [Indexed: 03/02/2024]
Abstract
We previously validated Fibresolve, a machine learning classifier system that non-invasively predicts idiopathic pulmonary fibrosis (IPF) diagnosis. The system incorporates an automated deep learning algorithm that analyzes chest computed tomography (CT) imaging to assess for features associated with idiopathic pulmonary fibrosis. Here, we assess performance in assessment of patterns beyond those that are characteristic features of usual interstitial pneumonia (UIP) pattern. The machine learning classifier was previously developed and validated using standard training, validation, and test sets, with clinical plus pathologically determined ground truth. The multi-site 295-patient validation dataset was used for focused subgroup analysis in this investigation to evaluate the classifier's performance range in cases with and without radiologic UIP and probable UIP designations. Radiologic assessment of specific features for UIP including the presence and distribution of reticulation, ground glass, bronchiectasis, and honeycombing was used for assignment of radiologic pattern. Output from the classifier was assessed within various UIP subgroups. The machine learning classifier was able to classify cases not meeting the criteria for UIP or probable UIP as IPF with estimated sensitivity of 56-65% and estimated specificity of 92-94%. Example cases demonstrated non-basilar-predominant as well as ground glass patterns that were indeterminate for UIP by subjective imaging criteria but for which the classifier system was able to correctly identify the case as IPF as confirmed by multidisciplinary discussion generally inclusive of histopathology. The machine learning classifier Fibresolve may be helpful in the diagnosis of IPF in cases without radiological UIP and probable UIP patterns.
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Affiliation(s)
- Marcello Chang
- Stanford School of Medicine, 291 Campus Drive, Stanford, CA, USA
| | | | | | | | - Yousef Ahmad
- Department of Pulmonary and Critical Care, University of Cincinnati Medical Center, Cincinnati, USA
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Alnasser TN, Abdulaal L, Maiter A, Sharkey M, Dwivedi K, Salehi M, Garg P, Swift AJ, Alabed S. Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging. Front Cardiovasc Med 2024; 11:1323461. [PMID: 38317865 PMCID: PMC10839106 DOI: 10.3389/fcvm.2024.1323461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/08/2024] [Indexed: 02/07/2024] Open
Abstract
Background Segmentation of cardiac structures is an important step in evaluation of the heart on imaging. There has been growing interest in how artificial intelligence (AI) methods-particularly deep learning (DL)-can be used to automate this process. Existing AI approaches to cardiac segmentation have mostly focused on cardiac MRI. This systematic review aimed to appraise the performance and quality of supervised DL tools for the segmentation of cardiac structures on CT. Methods Embase and Medline databases were searched to identify related studies from January 1, 2013 to December 4, 2023. Original research studies published in peer-reviewed journals after January 1, 2013 were eligible for inclusion if they presented supervised DL-based tools for the segmentation of cardiac structures and non-coronary great vessels on CT. The data extracted from eligible studies included information about cardiac structure(s) being segmented, study location, DL architectures and reported performance metrics such as the Dice similarity coefficient (DSC). The quality of the included studies was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results 18 studies published after 2020 were included. The DSC scores median achieved for the most commonly segmented structures were left atrium (0.88, IQR 0.83-0.91), left ventricle (0.91, IQR 0.89-0.94), left ventricle myocardium (0.83, IQR 0.82-0.92), right atrium (0.88, IQR 0.83-0.90), right ventricle (0.91, IQR 0.85-0.92), and pulmonary artery (0.92, IQR 0.87-0.93). Compliance of studies with CLAIM was variable. In particular, only 58% of studies showed compliance with dataset description criteria and most of the studies did not test or validate their models on external data (81%). Conclusion Supervised DL has been applied to the segmentation of various cardiac structures on CT. Most showed similar performance as measured by DSC values. Existing studies have been limited by the size and nature of the training datasets, inconsistent descriptions of ground truth annotations and lack of testing in external data or clinical settings. Systematic Review Registration [www.crd.york.ac.uk/prospero/], PROSPERO [CRD42023431113].
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Affiliation(s)
- Turki Nasser Alnasser
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- College of Applied Medical Science, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi Arabia
| | - Lojain Abdulaal
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Ahmed Maiter
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Mahan Salehi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Pankaj Garg
- Norwich Medical School, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, United Kingdom
| | - Andrew James Swift
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
- Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom
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Zysman M, Asselineau J, Saut O, Frison E, Oranger M, Maurac A, Charriot J, Achkir R, Regueme S, Klein E, Bommart S, Bourdin A, Dournes G, Casteigt J, Blum A, Ferretti G, Degano B, Thiébaut R, Chabot F, Berger P, Laurent F, Benlala I. Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19. Eur Radiol 2023; 33:9262-9274. [PMID: 37405504 PMCID: PMC10667132 DOI: 10.1007/s00330-023-09759-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/22/2023] [Accepted: 04/04/2023] [Indexed: 07/06/2023]
Abstract
OBJECTIVES COVID-19 pandemic seems to be under control. However, despite the vaccines, 5 to 10% of the patients with mild disease develop moderate to critical forms with potential lethal evolution. In addition to assess lung infection spread, chest CT helps to detect complications. Developing a prediction model to identify at-risk patients of worsening from mild COVID-19 combining simple clinical and biological parameters with qualitative or quantitative data using CT would be relevant to organizing optimal patient management. METHODS Four French hospitals were used for model training and internal validation. External validation was conducted in two independent hospitals. We used easy-to-obtain clinical (age, gender, smoking, symptoms' onset, cardiovascular comorbidities, diabetes, chronic respiratory diseases, immunosuppression) and biological parameters (lymphocytes, CRP) with qualitative or quantitative data (including radiomics) from the initial CT in mild COVID-19 patients. RESULTS Qualitative CT scan with clinical and biological parameters can predict which patients with an initial mild presentation would develop a moderate to critical form of COVID-19, with a c-index of 0.70 (95% CI 0.63; 0.77). CT scan quantification improved the performance of the prediction up to 0.73 (95% CI 0.67; 0.79) and radiomics up to 0.77 (95% CI 0.71; 0.83). Results were similar in both validation cohorts, considering CT scans with or without injection. CONCLUSION Adding CT scan quantification or radiomics to simple clinical and biological parameters can better predict which patients with an initial mild COVID-19 would worsen than qualitative analyses alone. This tool could help to the fair use of healthcare resources and to screen patients for potential new drugs to prevent a pejorative evolution of COVID-19. CLINICAL TRIAL REGISTRATION NCT04481620. CLINICAL RELEVANCE STATEMENT CT scan quantification or radiomics analysis is superior to qualitative analysis, when used with simple clinical and biological parameters, to determine which patients with an initial mild presentation of COVID-19 would worsen to a moderate to critical form. KEY POINTS • Qualitative CT scan analyses with simple clinical and biological parameters can predict which patients with an initial mild COVID-19 and respiratory symptoms would worsen with a c-index of 0.70. • Adding CT scan quantification improves the performance of the clinical prediction model to an AUC of 0.73. • Radiomics analyses slightly improve the performance of the model to a c-index of 0.77.
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Affiliation(s)
- Maéva Zysman
- CHU Bordeaux, 33600, Pessac, France.
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France.
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France.
| | | | - Olivier Saut
- "Institut de Mathématiques de Bordeaux" (IMB), UMR5251, CNRS, University of Bordeaux, 351 Cours Libération, 33400, Talence, France
- MONC Team & SISTM Team, INRIA Bordeaux Sud-Ouest, 200 Av Vieille Tour, 33400, Talence, France
| | | | - Mathilde Oranger
- Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d'Imagerie, Nancy, France
- Faculté de Médecine de Nancy, Université de Lorraine, Institut National de La Santé Et de La Recherche Médicale (INSERM) Unité Médicale de Recherche (UMR), S 1116, Vandœuvre-Lès-Nancy, France
| | - Arnaud Maurac
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
| | - Jeremy Charriot
- Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, 34295, Montpellier, France
- PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, 34295, Montpellier, France
| | | | | | | | - Sébastien Bommart
- Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, 34295, Montpellier, France
- PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, 34295, Montpellier, France
| | - Arnaud Bourdin
- Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, 34295, Montpellier, France
- PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, 34295, Montpellier, France
| | - Gael Dournes
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
| | | | - Alain Blum
- Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d'Imagerie, Nancy, France
| | - Gilbert Ferretti
- France Service de Radiologie Diagnostique Et Interventionnelle, Université Grenoble Alpes, CHU Grenoble-Alpes, Grenoble, France
| | - Bruno Degano
- France Service de Radiologie Diagnostique Et Interventionnelle, Université Grenoble Alpes, CHU Grenoble-Alpes, Grenoble, France
| | - Rodolphe Thiébaut
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
- MONC Team & SISTM Team, INRIA Bordeaux Sud-Ouest, 200 Av Vieille Tour, 33400, Talence, France
| | - Francois Chabot
- Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d'Imagerie, Nancy, France
- Faculté de Médecine de Nancy, Université de Lorraine, Institut National de La Santé Et de La Recherche Médicale (INSERM) Unité Médicale de Recherche (UMR), S 1116, Vandœuvre-Lès-Nancy, France
| | - Patrick Berger
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
| | - Francois Laurent
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
| | - Ilyes Benlala
- CHU Bordeaux, 33600, Pessac, France
- Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, 33600, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d'Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), 33600, Pessac, France
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Stoichita A, Ghita M, Mahler B, Vlasceanu S, Ghinet A, Mosteanu M, Cioacata A, Udrea A, Marcu A, Mitra GD, Ionescu CM, Iliesiu A. Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital. J Clin Med 2023; 12:7115. [PMID: 38002725 PMCID: PMC10672398 DOI: 10.3390/jcm12227115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/27/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND In December 2019 the World Health Organization announced that the widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection had become a global pandemic. The most affected organ by the novel virus is the lung, and imaging exploration of the thorax using computer tomography (CT) scanning and X-ray has had an important impact. MATERIALS AND METHODS We assessed the prevalence of lung lesions in vaccinated versus unvaccinated SARS-CoV-2 patients using an artificial intelligence (AI) platform provided by Medicai. The software analyzes the CT scans, performing the lung and lesion segmentation using a variant of the U-net convolutional network. RESULTS We conducted a cohort study at a tertiary lung hospital in which we included 186 patients: 107 (57.52%) male and 59 (42.47%) females, of which 157 (84.40%) were not vaccinated for SARS-CoV-2. Over five times more unvaccinated patients than vaccinated ones are admitted to the hospital and require imaging investigations. More than twice as many unvaccinated patients have more than 75% of the lungs affected. Patients in the age group 30-39 have had the most lung lesions at almost 69% of both lungs affected. Compared to vaccinated patients with comorbidities, unvaccinated patients with comorbidities had developed increased lung lesions by 5%. CONCLUSION The study revealed a higher percentage of lung lesions among unvaccinated SARS-CoV-2-positive patients admitted to The National Institute of Pulmonology "Marius Nasta" in Bucharest, Romania, underlining the importance of vaccination and also the usefulness of artificial intelligence in CT interpretation.
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Affiliation(s)
- Alexandru Stoichita
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Maria Ghita
- Research Group of Dynamical Systems and Control, Ghent University, 9052 Ghent, Belgium; (M.G.); (C.M.I.)
- Faculty of Medicine and Health Sciences, Antwerp University, 2610 Wilrijk, Belgium
| | - Beatrice Mahler
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Silviu Vlasceanu
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Andreea Ghinet
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Madalina Mosteanu
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
- Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Andreea Cioacata
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Andreea Udrea
- Medicai, 020961 Bucharest, Romania; (A.U.); (A.M.); (G.D.M.)
| | - Alina Marcu
- Medicai, 020961 Bucharest, Romania; (A.U.); (A.M.); (G.D.M.)
| | | | - Clara Mihaela Ionescu
- Research Group of Dynamical Systems and Control, Ghent University, 9052 Ghent, Belgium; (M.G.); (C.M.I.)
- Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Adriana Iliesiu
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- Clinical Hospital “Prof. Dr. Th. Burghele”, 061344 Bucharest, Romania
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Vasilev Y, Vladzymyrskyy A, Arzamasov K, Omelyanskaya O, Shulkin I, Kozikhina D, Goncharova I, Reshetnikov R, Chetverikov S, Blokhin I, Bobrovskaya T, Andreychenko A. Clinical application of radiological AI for pulmonary nodule evaluation: Replicability and susceptibility to the population shift caused by the COVID-19 pandemic. Int J Med Inform 2023; 178:105190. [PMID: 37603940 DOI: 10.1016/j.ijmedinf.2023.105190] [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: 03/19/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/23/2023]
Abstract
PURPOSE replicability and generalizability of medical AI are the recognized challenges that hinder a broad AI deployment in clinical practice. Pulmonary nodes detection and characterization based on chest CT images is one of the demanded use cases for automatization by means of AI, and multiple AI solutions addressing this task are becoming available. Here, we evaluated and compared the performance of several commercially available radiological AI with the same clinical task on the same external datasets acquired before and during the pandemic of COVID-19. APPROACH 5 commercially available AI models for pulmonary nodule detection were tested on two external datasets labelled by experts according to the intended clinical task. Dataset1 was acquired before the pandemic and did not contain radiological signs of COVID-19; dataset2 was collected during the pandemic and did contain radiological signs of COVID-19. ROC-analysis was applied separately for the dataset1 and dataset2 to select probability thresholds for each dataset separately. AUROC, sensitivity and specificity metrics were used to assess and compare the results of AI performance. RESULTS Statistically significant differences in AUROC values were observed between the AI models for the dataset1. Whereas for the dataset2 the differences of AUROC values became statistically insignificant. Sensitivity and specificity differed statistically significantly between the AI models for the dataset1. This difference was insignificant for the dataset2 when we applied the probability threshold initially selected for the dataset1. An update of the probability threshold based on the dataset2 created statistically significant differences of sensitivity and specificity between AI models for the dataset2. For 3 out of 5 AI models, the update of the probability threshold was valuable to compensate for the degradation of AI model performances with the population shift caused by the pandemic. CONCLUSIONS Population shift in the data is able to deteriorate differences of AI models performance. Update of the probability threshold together with the population shift seems to be valuable to preserve AI models performance without retraining them.
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Affiliation(s)
- Yuriy Vasilev
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Anton Vladzymyrskyy
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia; I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 8-2 Trubetskaya str. Moscow, 119991, Russian Federation
| | - Kirill Arzamasov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Olga Omelyanskaya
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Igor Shulkin
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Darya Kozikhina
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Inna Goncharova
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Roman Reshetnikov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Sergey Chetverikov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Ivan Blokhin
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Tatiana Bobrovskaya
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia.
| | - Anna Andreychenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
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8
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Ekingen E, Teleş M, Yıldız A, Yıldırım M. Mediating effect of work stress in the relationship between fear of COVID-19 and nurses' organizational and professional turnover intentions. Arch Psychiatr Nurs 2023; 42:97-105. [PMID: 36842836 PMCID: PMC9806922 DOI: 10.1016/j.apnu.2022.12.027] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 11/20/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023]
Abstract
Nursing is one of the most stressful and high-risk professions. It is important to identify the psychological problems experienced by nurses during the COVID-19 pandemic and examine the relationship between these problems to devise measures that can properly address them. This study examined mediating effect of work stress in the relationship between fear of COVID-19 and nurses' organizational and professional turnover intentions. Using a cross-sectional research design, this study was conducted on 486 nurses working in seven hospitals in Turkey. The mean age of the participants was 35.24 ± 6.81 and 59.9 % of them were women. The Fear of COVID-19 Scale, the General Work Stress Scale, and the Turnover Intention Scale were used to collect data. A mediation model showed that fear of COVID-19 was positively associated with work stress and organizational and professional turnover intentions. The model also revealed that work stress was positively associated with organizational and professional turnover intentions. Furthermore, the results demonstrated that fear of COVID-19 did not only have a direct effect on organizational and professional turnover intentions but also had an indirect effect on it via increased work stress. Findings improve our understanding of the role of work stress in the relationship between fear of COVID-19 and organizational and professional turnover intentions. The findings are fruitful for tailoring and implementing intervention programs to reduce the adverse psychological impacts of COVID-19 on nurses.
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Affiliation(s)
- Erhan Ekingen
- Department of Health Management, Batman University, Turkey
| | - Mesut Teleş
- Department of Health Management, Niğde Ömer Halisdemir University, Turkey
| | - Ahmet Yıldız
- Department of Health Management, Batman University, Turkey
| | - Murat Yıldırım
- Department of Psychology, Ağrı İbrahim Çeçen University, Turkey.
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9
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Hemalatha M. A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection. EXPERT SYSTEMS WITH APPLICATIONS 2022; 210:118227. [PMID: 35880010 PMCID: PMC9300559 DOI: 10.1016/j.eswa.2022.118227] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 06/08/2022] [Accepted: 07/17/2022] [Indexed: 05/09/2023]
Abstract
COVID-19 is a global pandemic that mostly affects patients' respiratory systems, and the only way to protect oneself against the virus at present moment is to diagnose the illness, isolate the patient, and provide immunization. In the present situation, the testing used to predict COVID-19 is inefficient and results in more false positives. This difficulty can be solved by developing a remote medical decision support system that detects illness using CT scans or X-ray images with less manual interaction and is less prone to errors. The state-of-art techniques mainly used complex deep learning architectures which are not quite effective when deployed in resource-constrained edge devices. To overcome this problem, a multi-objective Modified Heat Transfer Search (MOMHTS) optimized hybrid Random Forest Deep learning (HRFDL) classifier is proposed in this paper. The MOMHTS algorithm mainly optimizes the deep learning model in the HRFDL architecture by optimizing the hyperparameters associated with it to support the resource-constrained edge devices. To evaluate the efficiency of this technique, extensive experimentation is conducted on two real-time datasets namely the COVID19 lung CT scan dataset and the Chest X-ray images (Pneumonia) datasets. The proposed methodology mainly offers increased speed for communication between the IoT devices and COVID-19 detection via the MOMHTS optimized HRFDL classifier is modified to support the resources which can only support minimal computation and handle minimum storage. The proposed methodology offers an accuracy of 99% for both the COVID19 lung CT scan dataset and the Chest X-ray images (Pneumonia) datasets with minimal computational time, cost, and storage. Based on the simulation outcomes, we can conclude that the proposed methodology is an appropriate fit for edge computing detection to identify the COVID19 and pneumonia with higher detection accuracy.
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Affiliation(s)
- Murugan Hemalatha
- Department of Faculty of Artificial Intelligence and Data Science, Saveetha Engineering College, India
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10
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Hasanzadeh A, Hamblin MR, Kiani J, Noori H, Hardie JM, Karimi M, Shafiee H. Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? NANO TODAY 2022; 47:101665. [PMID: 37034382 PMCID: PMC10081506 DOI: 10.1016/j.nantod.2022.101665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.
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Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Jafar Kiani
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Joseph M. Hardie
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran 141556559, Iran
- Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran 1584743311, Iran
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
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11
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Javidi H, Mariam A, Khademi G, Zabor EC, Zhao R, Radivoyevitch T, Rotroff DM. Identification of robust deep neural network models of longitudinal clinical measurements. NPJ Digit Med 2022; 5:106. [PMID: 35896817 PMCID: PMC9329311 DOI: 10.1038/s41746-022-00651-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 07/06/2022] [Indexed: 11/09/2022] Open
Abstract
Deep learning (DL) from electronic health records holds promise for disease prediction, but systematic methods for learning from simulated longitudinal clinical measurements have yet to be reported. We compared nine DL frameworks using simulated body mass index (BMI), glucose, and systolic blood pressure trajectories, independently isolated shape and magnitude changes, and evaluated model performance across various parameters (e.g., irregularity, missingness). Overall, discrimination based on variation in shape was more challenging than magnitude. Time-series forest-convolutional neural networks (TSF-CNN) and Gramian angular field(GAF)-CNN outperformed other approaches (P < 0.05) with overall area-under-the-curve (AUCs) of 0.93 for both models, and 0.92 and 0.89 for variation in magnitude and shape with up to 50% missing data. Furthermore, in a real-world assessment, the TSF-CNN model predicted T2D with AUCs reaching 0.72 using only BMI trajectories. In conclusion, we performed an extensive evaluation of DL approaches and identified robust modeling frameworks for disease prediction based on longitudinal clinical measurements.
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Affiliation(s)
- Hamed Javidi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA
| | - Arshiya Mariam
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Gholamreza Khademi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Emily C Zabor
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ran Zhao
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Tomas Radivoyevitch
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA.
- Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, OH, USA.
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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12
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Rhodes CJ, Sweatt AJ, Maron BA. Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension. Circ Res 2022; 130:1423-1444. [PMID: 35482840 PMCID: PMC9070103 DOI: 10.1161/circresaha.121.319969] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.
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Affiliation(s)
- Christopher J Rhodes
- Department of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.)
| | - Andrew J Sweatt
- Department of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.)
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (B.A.M.).,Division of Cardiology, VA Boston Healthcare System, West Roxbury, MA (B.A.M.)
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13
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Anand S, Sharma V, Pourush R, Jaiswal S. A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19. Ann Med Surg (Lond) 2022; 76:103519. [PMID: 35401978 PMCID: PMC8975609 DOI: 10.1016/j.amsu.2022.103519] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/09/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022] Open
Abstract
The novel coronavirus, renamed SARS-CoV-2 and most commonly referred to as COVID-19, has infected nearly 44.83 million people in 224 countries and has been designated SARS-CoV-2. In this study, we used 'web of Science', 'Scopus' and 'goggle scholar' with the keywords of "SARS-CoV-2 detection" or "coronavirus 2019 detection" or "COVID 2019 detection" or "COVID 19 detection" "corona virus techniques for detection of COVID-19", "audio techniques for detection of COVID-19", "speech techniques for detection of COVID-19", for period of 2019-2021. Some COVID-19 instances have an impact on speech production, which suggests that researchers should look for signs of disease detection in speech utilising audio and speech recognition signals from humans to better understand the condition. It is presented in this review that an overview of human audio signals is presented using an AI (Artificial Intelligence) model to diagnose, spread awareness, and monitor COVID-19, employing bio and non-obtrusive signals that communicated human speech and non-speech audio information is presented. Development of accurate and rapid screening techniques that permit testing at a reasonable cost is critical in the current COVID-19 pandemic crisis, according to the World Health Organization. In this context, certain existing investigations have shown potential in the detection of COVID 19 diagnostic signals from relevant auditory noises, which is a promising development. According to authors, it is not a single "perfect" COVID-19 test that is required, but rather a combination of rapid and affordable tests, non-clinic pre-screening tools, and tools from a variety of supply chains and technologies that will allow us to safely return to our normal lives while we await the completion of the hassle free COVID-19 vaccination process for all ages. This review was able to gather information on biomedical signal processing in the detection of speech, coughing sounds, and breathing signals for the purpose of diagnosing and screening the COVID-19 virus.
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Affiliation(s)
- Satyajit Anand
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Vikrant Sharma
- Mechanical Engineering, Mody University of Science and Technology, India
| | - Rajeev Pourush
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Sandeep Jaiswal
- Biomedical Engineering, Mody University of Science and Technology, India
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14
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Filipow N, Main E, Sebire NJ, Booth J, Taylor AM, Davies G, Stanojevic S. Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review. BMJ Open Respir Res 2022; 9:9/1/e001165. [PMID: 35297371 PMCID: PMC8928277 DOI: 10.1136/bmjresp-2021-001165] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/06/2022] [Indexed: 11/23/2022] Open
Abstract
Machine learning (ML) holds great potential for predicting clinical outcomes in heterogeneous chronic respiratory diseases (CRD) affecting children, where timely individualised treatments offer opportunities for health optimisation. This paper identifies rate-limiting steps in ML prediction model development that impair clinical translation and discusses regulatory, clinical and ethical considerations for ML implementation. A scoping review of ML prediction models in paediatric CRDs was undertaken using the PRISMA extension scoping review guidelines. From 1209 results, 25 articles published between 2013 and 2021 were evaluated for features of a good clinical prediction model using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines. Most of the studies were in asthma (80%), with few in cystic fibrosis (12%), bronchiolitis (4%) and childhood wheeze (4%). There were inconsistencies in model reporting and studies were limited by a lack of validation, and absence of equations or code for replication. Clinician involvement during ML model development is essential and diversity, equity and inclusion should be assessed at each step of the ML pipeline to ensure algorithms do not promote or amplify health disparities among marginalised groups. As ML prediction studies become more frequent, it is important that models are rigorously developed using published guidelines and take account of regulatory frameworks which depend on model complexity, patient safety, accountability and liability.
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Affiliation(s)
- Nicole Filipow
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Eleanor Main
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Neil J Sebire
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,GOSH NIHR BRC, Great Ormond Street Hospital for Children, London, UK
| | - John Booth
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,GOSH NIHR BRC, Great Ormond Street Hospital for Children, London, UK
| | - Andrew M Taylor
- GOSH NIHR BRC, Great Ormond Street Hospital for Children, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Gwyneth Davies
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,GOSH NIHR BRC, Great Ormond Street Hospital for Children, London, UK
| | - Sanja Stanojevic
- Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
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15
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Cau R, Faa G, Nardi V, Balestrieri A, Puig J, Suri JS, SanFilippo R, Saba L. Long-COVID diagnosis: From diagnostic to advanced AI-driven models. Eur J Radiol 2022; 148:110164. [PMID: 35114535 PMCID: PMC8791239 DOI: 10.1016/j.ejrad.2022.110164] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 12/19/2022]
Abstract
SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as "long COVID-19 syndrome". Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes. In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit. The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Italy
| | - Valentina Nardi
- Department of Cardiovascular Medicine Mayo Clinic, Rochester, MN, USA
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy
| | - Josep Puig
- Department of Radiology (IDI), Hospital Universitari de Girona, Girona, Spain
| | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, Atheropoint LLC, Roseville, CA, USA
| | - Roberto SanFilippo
- Department of Vascular Surgery, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy.
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16
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Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3:1-7. [DOI: 10.35711/aimi.v3.i1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/14/2022] [Accepted: 02/23/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a branch of computer science where machines are trained to imitate human-level intelligence and perform well-defined tasks. AI can provide accurate results as well as analyze vast amounts of data that cannot be analyzed via conventional statistical methods. AI has been utilized in pulmonary medicine for almost two decades and its utilization continues to expand. AI can help in making diagnoses and predicting outcomes in pulmonary diseases based on clinical data, chest imaging, lung pathology, and pulmonary function testing. AI-based applications enable physicians to use enormous amounts of data and improve their precision in the treatment of pulmonary diseases. Given the growing role of AI in pulmonary medicine, it is important for practitioners caring for patients with pulmonary diseases to understand how AI can work in order to implement it into clinical practices and improve patient care. The goal of this mini-review is to discuss the use of AI in pulmonary medicine and imaging in cases of obstructive lung disease, interstitial lung disease, infections, nodules, and lung cancer.
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Affiliation(s)
- Saiara Choudhury
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Asad Chohan
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Rahul Dadhwal
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Abhay P Vakil
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Rene Franco
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Pahnwat Tonya Taweesedt
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
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17
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An Overview of Medical Electronic Hardware Security and Emerging Solutions. ELECTRONICS 2022. [DOI: 10.3390/electronics11040610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Electronic healthcare technology is widespread around the world and creates massive potential to improve clinical outcomes and transform care delivery. However, there are increasing concerns with respect to the cyber vulnerabilities of medical tools, malicious medical errors, and security attacks on healthcare data and devices. Increased connectivity to existing computer networks has exposed the medical devices/systems and their communicating data to new cybersecurity vulnerabilities. Adversaries leverage the state-of-the-art technologies, in particular artificial intelligence and computer vision-based techniques, in order to launch stronger and more detrimental attacks on the medical targets. The medical domain is an attractive area for cybercrimes for two fundamental reasons: (a) it is rich resource of valuable and sensitive data; and (b) its protection and defensive mechanisms are weak and ineffective. The attacks aim to steal health information from the patients, manipulate the medical information and queries, maliciously change the medical diagnosis, decisions, and prescriptions, etc. A successful attack in the medical domain causes serious damage to the patient’s health and even death. Therefore, cybersecurity is critical to patient safety and every aspect of the medical domain, while it has not been studied sufficiently. To tackle this problem, new human- and computer-based countermeasures are researched and proposed for medical attacks using the most effective software and hardware technologies, such as artificial intelligence and computer vision. This review provides insights to the novel and existing solutions in the literature that mitigate cyber risks, errors, damage, and threats in the medical domain. We have performed a scoping review analyzing the four major elements in this area (in order from a medical perspective): (1) medical errors; (2) security weaknesses of medical devices at software- and hardware-level; (3) artificial intelligence and/or computer vision in medical applications; and (4) cyber attacks and defenses in the medical domain. Meanwhile, artificial intelligence and computer vision are key topics in this review and their usage in all these four elements are discussed. The review outcome delivers the solutions through building and evaluating the connections among these elements in order to serve as a beneficial guideline for medical electronic hardware security.
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18
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Karandashova S, Florova G, Idell S, Komissarov AA. From Bedside to the Bench—A Call for Novel Approaches to Prognostic Evaluation and Treatment of Empyema. Front Pharmacol 2022; 12:806393. [PMID: 35126140 PMCID: PMC8811368 DOI: 10.3389/fphar.2021.806393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 12/31/2021] [Indexed: 11/13/2022] Open
Abstract
Empyema, a severe complication of pneumonia, trauma, and surgery is characterized by fibrinopurulent effusions and loculations that can result in lung restriction and resistance to drainage. For decades, efforts have been focused on finding a universal treatment that could be applied to all patients with practice recommendations varying between intrapleural fibrinolytic therapy (IPFT) and surgical drainage. However, despite medical advances, the incidence of empyema has increased, suggesting a gap in our understanding of the pathophysiology of this disease and insufficient crosstalk between clinical practice and preclinical research, which slows the development of innovative, personalized therapies. The recent trend towards less invasive treatments in advanced stage empyema opens new opportunities for pharmacological interventions. Its remarkable efficacy in pediatric empyema makes IPFT the first line treatment. Unfortunately, treatment approaches used in pediatrics cannot be extrapolated to empyema in adults, where there is a high level of failure in IPFT when treating advanced stage disease. The risk of bleeding complications and lack of effective low dose IPFT for patients with contraindications to surgery (up to 30%) promote a debate regarding the choice of fibrinolysin, its dosage and schedule. These challenges, which together with a lack of point of care diagnostics to personalize treatment of empyema, contribute to high (up to 20%) mortality in empyema in adults and should be addressed preclinically using validated animal models. Modern preclinical studies are delivering innovative solutions for evaluation and treatment of empyema in clinical practice: low dose, targeted treatments, novel biomarkers to predict IPFT success or failure, novel delivery methods such as encapsulating fibrinolysin in echogenic liposomal carriers to increase the half-life of plasminogen activator. Translational research focused on understanding the pathophysiological mechanisms that control 1) the transition from acute to advanced-stage, chronic empyema, and 2) differences in outcomes of IPFT between pediatric and adult patients, will identify new molecular targets in empyema. We believe that seamless bidirectional communication between those working at the bedside and the bench would result in novel personalized approaches to improve pharmacological treatment outcomes, thus widening the window for use of IPFT in adult patients with advanced stage empyema.
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Affiliation(s)
- Sophia Karandashova
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Galina Florova
- Department of Cellular and Molecular Biology, The University of Texas Health Science Center at Tyler, Tyler, TX, United States
| | - Steven Idell
- Department of Cellular and Molecular Biology, The University of Texas Health Science Center at Tyler, Tyler, TX, United States
| | - Andrey A. Komissarov
- Department of Cellular and Molecular Biology, The University of Texas Health Science Center at Tyler, Tyler, TX, United States
- *Correspondence: Andrey A. Komissarov,
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19
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Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease. Diagnostics (Basel) 2021; 11:diagnostics11122396. [PMID: 34943632 PMCID: PMC8700350 DOI: 10.3390/diagnostics11122396] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/26/2021] [Accepted: 12/18/2021] [Indexed: 01/21/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortality and contributes to high morbidity worldwide. Patients with COPD have a higher risk for acute respiratory failure, ventilator dependence, and mortality after hospitalization compared with the general population. Accurate and early risk detection will provide more information for early management and better decision making. This study aimed to build prediction models using patients’ characteristics, laboratory data, and comorbidities for early detection of acute respiratory failure, ventilator dependence, and mortality in patients with COPD after hospitalization. We retrospectively collected the electronic medical records of 5061 patients with COPD in three hospitals of the Chi Mei Medical Group, Taiwan. After data cleaning, we built three prediction models for acute respiratory failure, ventilator dependence, and mortality using seven machine learning algorithms. Based on the AUC value, the best model for mortality was built by the XGBoost algorithm (AUC = 0.817), the best model for acute respiratory failure was built by random forest algorithm (AUC = 0.804), while the best model for ventilator dependence was built by LightGBM algorithm (AUC = 0.809). A web service application was implemented with the best models and integrated into the existing hospital information system for physician’s trials and evaluations. Our machine learning models exhibit excellent predictive quality and can therefore provide physicians with a useful decision-making reference for the adverse prognosis of COPD patients.
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Editorial Comment: Deep Learning Algorithm Outperforms a Radiologist-Or Does It? AJR Am J Roentgenol 2021; 218:650. [PMID: 34817196 DOI: 10.2214/ajr.21.27102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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21
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Digital Transformation and Artificial Intelligence Applied to Business: Legal Regulations, Economic Impact and Perspective. LAWS 2021. [DOI: 10.3390/laws10030070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Digital transformation can be defined as the integration of new technologies into all areas of a company. This technological integration will ultimately imply a need to transform traditional business models. Similarly, artificial intelligence has been one of the most disruptive technologies of recent decades, with a high potential impact on business and people. Cognitive approaches that simulate both human behavior and thinking are leading to advanced analytical models that help companies to boost sales and customer engagement, improve their operational efficiency, improve their services and, in short, generate new relevant information from data. These decision-making models are based on descriptive, predictive and prescriptive analytics. This necessitates the existence of a legal framework that regulates all digital changes with uniformity between countries and helps a proper digital transformation process under a clear regulation. On the other hand, it is essential that this digital disruption is not slowed down by the regulatory framework. This work will demonstrate that AI and digital transformation will be an intrinsic part of many applications and will therefore be universally deployed. However, this implementation will have to be done under common regulations and in line with the new reality.
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22
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Darbari A, Kumar K, Darbari S, Patil PL. Requirement of artificial intelligence technology awareness for thoracic surgeons. THE CARDIOTHORACIC SURGEON 2021; 29:13. [PMID: 38624757 PMCID: PMC8254051 DOI: 10.1186/s43057-021-00053-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/26/2021] [Indexed: 12/15/2022] Open
Abstract
Background We have recently witnessed incredible interest in computer-based, internet web-dependent mechanisms and artificial intelligence (AI)-dependent technique emergence in our day-to-day lives. In the recent era of COVID-19 pandemic, this nonhuman, machine-based technology has gained a lot of momentum. Main body of the abstract The supercomputers and robotics with AI technology have shown the potential to equal or even surpass human experts' accuracy in some tasks in the future. Artificial intelligence (AI) is prompting massive data interweaving with elements from many digital sources such as medical imaging sorting, electronic health records, and transforming healthcare delivery. But in thoracic surgical and our counterpart pulmonary medical field, AI's main applications are still for interpretation of thoracic imaging, lung histopathological slide evaluation, physiological data interpretation, and biosignal testing only. The query arises whether AI-enabled technology-based or autonomous robots could ever do or provide better thoracic surgical procedures than current surgeons but it seems like an impossibility now. Short conclusion This review article aims to provide information pertinent to the use of AI to thoracic surgical specialists. In this review article, we described AI and related terminologies, current utilisation, challenges, potential, and current need for awareness of this technology.
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Affiliation(s)
| | - Krishan Kumar
- CSE Department, National Institute of Technology, Srinagar, Uttarakhand 246174 India
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Afshar-Oromieh A, Prosch H, Schaefer-Prokop C, Bohn KP, Alberts I, Mingels C, Thurnher M, Cumming P, Shi K, Peters A, Geleff S, Lan X, Wang F, Huber A, Gräni C, Heverhagen JT, Rominger A, Fontanellaz M, Schöder H, Christe A, Mougiakakou S, Ebner L. A comprehensive review of imaging findings in COVID-19 - status in early 2021. Eur J Nucl Med Mol Imaging 2021; 48:2500-2524. [PMID: 33932183 PMCID: PMC8087891 DOI: 10.1007/s00259-021-05375-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 03/09/2021] [Indexed: 02/06/2023]
Abstract
Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [18F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.
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Affiliation(s)
- Ali Afshar-Oromieh
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, CH-3010, Bern, Switzerland.
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Cornelia Schaefer-Prokop
- Department of Radiology, Meander Medical Center, Amersfoort, Netherlands
- Department of Medical Imaging, Radboud University, Nijmegen, Netherlands
| | - Karl Peter Bohn
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, CH-3010, Bern, Switzerland
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, CH-3010, Bern, Switzerland
| | - Clemens Mingels
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, CH-3010, Bern, Switzerland
| | - Majda Thurnher
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Paul Cumming
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, CH-3010, Bern, Switzerland
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, CH-3010, Bern, Switzerland
| | - Alan Peters
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Silvana Geleff
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - Xiaoli Lan
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Wang
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Adrian Huber
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Christoph Gräni
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Johannes T Heverhagen
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, CH-3010, Bern, Switzerland
| | - Matthias Fontanellaz
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Heiko Schöder
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Stavroula Mougiakakou
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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Rolandsson Enes S, Krasnodembskaya AD, English K, Dos Santos CC, Weiss DJ. Research Progress on Strategies that can Enhance the Therapeutic Benefits of Mesenchymal Stromal Cells in Respiratory Diseases With a Specific Focus on Acute Respiratory Distress Syndrome and Other Inflammatory Lung Diseases. Front Pharmacol 2021; 12:647652. [PMID: 33953680 PMCID: PMC8089479 DOI: 10.3389/fphar.2021.647652] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/29/2021] [Indexed: 01/16/2023] Open
Abstract
Recent advances in cell based therapies for lung diseases and critical illnesses offer significant promise. Despite encouraging preclinical results, the translation of efficacy to the clinical settings have not been successful. One of the possible reasons for this is the lack of understanding of the complex interaction between mesenchymal stromal cells (MSCs) and the host environment. Other challenges for MSC cell therapies include cell sources, dosing, disease target, donor variability, and cell product manufacturing. Here we provide an overview on advances and current issues with a focus on MSC-based cell therapies for inflammatory acute respiratory distress syndrome varieties and other inflammatory lung diseases.
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Affiliation(s)
- Sara Rolandsson Enes
- Department of Experimental Medical Science, Faculty of Medicine, Lund University, Lund, Sweden
| | - Anna D Krasnodembskaya
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry, and Biomedical Sciences, Queens University, Belfast, United Kingdom
| | - Karen English
- Cellular Immunology Laboratory, Biology Department, Kathleen Lonsdale Institute for Human Health Research, Maynooth University, Maynooth, Ireland
| | - Claudia C Dos Santos
- Interdepartmental Division of Critical Care, Department of Medicine and the Keenan Center for Biomedical Research, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Daniel J Weiss
- Department of Medicine, 226 Health Science Research Facility, Larner College of Medicine, University of Vermont, Burlington, VT, United States
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Angelini E, Shah A. Using Artificial Intelligence in Fungal Lung Disease: CPA CT Imaging as an Example. Mycopathologia 2021; 186:733-737. [PMID: 33840005 PMCID: PMC8536566 DOI: 10.1007/s11046-021-00546-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 12/17/2022]
Abstract
This positioning paper aims to discuss current challenges and opportunities for artificial intelligence (AI) in fungal lung disease, with a focus on chronic pulmonary aspergillosis and some supporting proof-of-concept results using lung imaging. Given the high uncertainty in fungal infection diagnosis and analyzing treatment response, AI could potentially have an impactful role; however, developing imaging-based machine learning raises several specific challenges. We discuss recommendations to engage the medical community in essential first steps towards fungal infection AI with gathering dedicated imaging registries, linking with non-imaging data and harmonizing image-finding annotations.
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Affiliation(s)
- Elsa Angelini
- NIHR Imperial Biomedical Research Centre, ITMAT Data Science Group, Imperial College London, London, UK.,Department of Metabolism-Digestion-Reproduction, Imperial College London, London, UK
| | - Anand Shah
- Respiratory Medicine, Royal Brompton and Harefield NHS Foundation Trust, London, UK. .,MRC Centre of Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
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Dwivedi K, Sharkey M, Condliffe R, Uthoff JM, Alabed S, Metherall P, Lu H, Wild JM, Hoffman EA, Swift AJ, Kiely DG. Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review. Diagnostics (Basel) 2021; 11:diagnostics11040679. [PMID: 33918838 PMCID: PMC8070579 DOI: 10.3390/diagnostics11040679] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/05/2021] [Accepted: 04/05/2021] [Indexed: 12/24/2022] Open
Abstract
Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.
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Affiliation(s)
- Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK; (M.S.); (R.C.); (S.A.); (P.M.); (J.M.W.); (A.J.S.); (D.G.K.)
- Correspondence:
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK; (M.S.); (R.C.); (S.A.); (P.M.); (J.M.W.); (A.J.S.); (D.G.K.)
- Radiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Robin Condliffe
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK; (M.S.); (R.C.); (S.A.); (P.M.); (J.M.W.); (A.J.S.); (D.G.K.)
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Johanna M. Uthoff
- Department of Computer Science, University of Sheffield, Sheffield S1 4DP, UK; (J.M.U.); (H.L.)
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK; (M.S.); (R.C.); (S.A.); (P.M.); (J.M.W.); (A.J.S.); (D.G.K.)
| | - Peter Metherall
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK; (M.S.); (R.C.); (S.A.); (P.M.); (J.M.W.); (A.J.S.); (D.G.K.)
- Radiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Haiping Lu
- Department of Computer Science, University of Sheffield, Sheffield S1 4DP, UK; (J.M.U.); (H.L.)
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield S1 3JD, UK
| | - Jim M. Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK; (M.S.); (R.C.); (S.A.); (P.M.); (J.M.W.); (A.J.S.); (D.G.K.)
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield S1 3JD, UK
| | - Eric A. Hoffman
- Advanced Pulmonary Physiomic Imaging Laboratory, University of Iowa, C748 GH, Iowa City, IA 52242, USA;
| | - Andrew J. Swift
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK; (M.S.); (R.C.); (S.A.); (P.M.); (J.M.W.); (A.J.S.); (D.G.K.)
- Radiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield S1 3JD, UK
| | - David G. Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK; (M.S.); (R.C.); (S.A.); (P.M.); (J.M.W.); (A.J.S.); (D.G.K.)
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield S1 3JD, UK
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Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic. SUSTAINABLE OPERATIONS AND COMPUTERS 2021; 2. [PMCID: PMC8052508 DOI: 10.1016/j.susoc.2021.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Background and aims Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart. Methods We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure. Results AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed. Conclusions Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.
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