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Laffafchi S, Ebrahimi A, Kafan S. Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data. Health Inf Sci Syst 2024; 12:17. [PMID: 38464464 PMCID: PMC10917730 DOI: 10.1007/s13755-024-00276-9] [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: 12/15/2022] [Accepted: 01/17/2024] [Indexed: 03/12/2024] Open
Abstract
Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.
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Affiliation(s)
- Soroor Laffafchi
- Department of Business Administration and Entrepreneurship, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Ahmad Ebrahimi
- Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Samira Kafan
- Department of Pulmonary Medicine, Sina Hospital, International Relations Office, Medical School, Tehran University of Medical Sciences, PourSina St., Tehran, 1417613151 Iran
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Xin Y, Li H, Zhou Y, Yang Q, Mu W, Xiao H, Zhuo Z, Liu H, Wang H, Qu X, Wang C, Liu H, Yu K. The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2023; 23:155. [PMID: 37559062 PMCID: PMC10410953 DOI: 10.1186/s12911-023-02256-7] [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: 08/01/2022] [Accepted: 08/02/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND The purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients. METHODS The PubMed, Embase, Web of Science, CNKI, Wanfang, China Biomedical Literature, and VIP databases were systematically searched from inception to October 2022 to identify studies that evaluated the predictive effects of artificial intelligence on mortality among COVID-19 patients. The retrieved literature was screened according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the QUADAS-2 tools. Statistical analysis of the included studies was performed using Review Manager 5.3, Stata 16.0, and Meta-DiSc 1.4 statistical software. This meta-analysis was registered in PROSPERO (CRD42022315158). FINDINGS Of 2193 studies, 23 studies involving a total of 25 AI models met the inclusion criteria. Among them, 18 studies explicitly mentioned training and test sets, and 5 studies did not explicitly mention grouping. In the training set, the pooled sensitivity was 0.93 [0.87, 0.96], the pooled specificity was 0.94 [0.87, 0.97], and the area under the ROC curve was 0.98 [0.96, 0.99]. In the validation set, the pooled sensitivity was 0.84 [0.78, 0.88], the pooled specificity was 0.89 [0.85, 0.92], and the area under the ROC curve was 0.93 [1.00, 0.00]. In the subgroup analysis, the areas under the summary receiver operating characteristic (SROC) curves of the artificial intelligence models KNN, SVM, ANN, RF and XGBoost were 0.98, 0.98, 0.94, 0.92, and 0.91, respectively. The Deeks funnel plot indicated that there was no significant publication bias in this study (P > 0.05). INTERPRETATION Artificial intelligence models have high accuracy in predicting mortality among COVID-19 patients and have high prognostic value. Among them, the KNN, SVM, ANN, RF, XGBoost, and other models have the highest levels of accuracy.
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Affiliation(s)
- Yu Xin
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Hongxu Li
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Yuxin Zhou
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Qing Yang
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Wenjing Mu
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Han Xiao
- Departments of Pharmacy and Cardiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Zipeng Zhuo
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Hongyu Liu
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Hongying Wang
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Xutong Qu
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Changsong Wang
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China.
| | - Haitao Liu
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China.
| | - Kaijiang Yu
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
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Albano D, Gitto S, Messina C, Serpi F, Salvatore C, Castiglioni I, Zagra L, De Vecchi E, Sconfienza LM. MRI-based artificial intelligence to predict infection following total hip arthroplasty failure. LA RADIOLOGIA MEDICA 2023; 128:340-346. [PMID: 36786971 PMCID: PMC10020270 DOI: 10.1007/s11547-023-01608-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/03/2023] [Indexed: 02/15/2023]
Abstract
PURPOSE To investigate whether artificial intelligence (AI) can differentiate septic from non-septic total hip arthroplasty (THA) failure based on preoperative MRI features. MATERIALS AND METHODS We included 173 patients (98 females, age: 67 ± 12 years) subjected to first-time THA revision surgery after preoperative pelvis MRI. We divided the patients into a training/validation/internal testing cohort (n = 117) and a temporally independent external-testing cohort (n = 56). MRI features were used to train, validate and test a machine learning algorithm based on support vector machine (SVM) to predict THA infection on the training-internal validation cohort with a nested fivefold validation approach. Machine learning performance was evaluated on independent data from the external-testing cohort. RESULTS MRI features were significantly more frequently observed in THA infection (P < 0.001), except bone destruction, periarticular soft-tissue mass, and fibrous membrane (P > 0.005). Considering all MRI features in the training/validation/internal-testing cohort, SVM classifier reached 92% sensitivity, 62% specificity, 79% PPV, 83% NPV, 82% accuracy, and 81% AUC in predicting THA infection, with bone edema, extracapsular edema, and synovitis having been the best predictors. After being tested on the external-testing cohort, the classifier showed 92% sensitivity, 79% specificity, 89% PPV, 83% NPV, 88% accuracy, and 89% AUC in predicting THA infection. SVM classifier showed 81% sensitivity, 76% specificity, 66% PPV, 88% NPV, 80% accuracy, and 74% AUC in predicting THA infection in the training/validation/internal-testing cohort based on the only presence of periprosthetic bone marrow edema on MRI, while it showed 68% sensitivity, 89% specificity, 93% PPV, 60% NPV, 75% accuracy, and 79% AUC in the external-testing cohort. CONCLUSION AI using SVM classifier showed promising results in predicting THA infection based on MRI features. This model might support radiologists in identifying THA infection.
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Affiliation(s)
- Domenico Albano
- Unità Operativa Di Radiologia Diagnostica E Interventistica, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy.
| | - Salvatore Gitto
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, 20133, Milan, Italy
| | - Carmelo Messina
- Unità Operativa Di Radiologia Diagnostica E Interventistica, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, 20133, Milan, Italy
| | - Francesca Serpi
- Unità Operativa Di Radiologia Diagnostica E Interventistica, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, 20133, Milan, Italy
| | - Christian Salvatore
- DeepTrace Technologies S.R.L., Milan, Italy
- Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, Pavia, Italy
| | - Isabella Castiglioni
- Department of Physics, Università Degli Studi Di Milano-Bicocca, 20126, Milan, Italy
- Institute of Biomedical Imaging and Physiology, Consiglio Nazionale Delle Ricerche, 20090, Segrate, Italy
| | - Luigi Zagra
- Hip Department, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
| | - Elena De Vecchi
- Laboratory of Clinical Chemistry and Microbiology, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
| | - Luca Maria Sconfienza
- Unità Operativa Di Radiologia Diagnostica E Interventistica, IRCCS Istituto Ortopedico Galeazzi, 20161, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, 20133, Milan, Italy
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Abegaz KH, Etikan İ. Boosting the Performance of Artificial Intelligence-Driven Models in Predicting COVID-19 Mortality in Ethiopia. Diagnostics (Basel) 2023; 13:diagnostics13040658. [PMID: 36832146 PMCID: PMC9955316 DOI: 10.3390/diagnostics13040658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
Like other nations around the world, Ethiopia has suffered negative effects from COVID-19. The objective of this study was to predict COVID-19 mortality using Artificial Intelligence (AI)-driven models. Two-year daily recorded data related to COVID-19 were trained and tested to predict mortality using machine learning algorithms. Normalization of features, sensitivity analysis for feature selection, modelling of AI-driven models, and comparing the boosting model with single AI-driven models were the main activities performed in this study. Prediction of COVID-19 mortality was conducted using a combination of four dominant feature variables, and hence, the best determination of coefficient (DC) of AdaBoost, KNN, ANN-6, and SVM in the prediction process were 0.9422, 0.8618, 0.8629, and 0.7171, respectively. The Boosting model improved the performance of the individual AI-driven models KNN, SVM, and ANN-6 by 7.94, 22.51, and 8.02 percent, respectively, at the verification stage using the testing dataset. This suggests that the boosting model has the best performance for prediction of COVID-19 mortality in Ethiopia. As a result, it suggests a promising potential performance of boosting ensemble model to be applied in predicting mortality and cases from similarly recorded daily data to predict mortality due to COVID-19 in other parts of the world.
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Affiliation(s)
- Kedir Hussein Abegaz
- Biostatistics and Health Informatics, Public Health Department, Madda Walabu University, Robe 247, Ethiopia
- Department of Biostatistics, Faculty of Medicine, Near East University, Near East Avenue, Nicosia 99138, Turkey
- Correspondence: ; Tel.: +251-913-012630
| | - İlker Etikan
- Department of Biostatistics, Faculty of Medicine, Near East University, Near East Avenue, Nicosia 99138, Turkey
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Wunderlich P, Wiegräbe F, Dörksen H. Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1215. [PMID: 36673969 PMCID: PMC9859038 DOI: 10.3390/ijerph20021215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/31/2022] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
Due to the demographic aging of society, the demand for skilled caregiving is increasing. However, the already existing shortage of professional caregivers will exacerbate in the future. As a result, family caregivers must shoulder a heavier share of the care burden. To ease the burden and promote a better work-life balance, we developed the Digital Case Manager. This tool uses machine learning algorithms to learn the relationship between a care situation and the next care steps and helps family caregivers balance their professional and private lives so that they are able to continue caring for their family members without sacrificing their own jobs and personal ambitions. The data for the machine learning model are generated by means of a questionnaire based on professional assessment instruments. We implemented a proof-of-concept of the Digital Case Manager and initial tests show promising results. It offers a quick and easy-to-use tool for family caregivers in the early stages of a care situation.
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Abegaz KH, Etikan İ. Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa. Diagnostics (Basel) 2022; 12:2861. [PMID: 36428921 PMCID: PMC9689547 DOI: 10.3390/diagnostics12112861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial intelligence-driven ensemble model in East Africa. The dataset, which spans two years, was divided into training and verification datasets. To predict the mortality, three steps were conducted, which included a sensitivity analysis, the modelling of four single AI-driven models, and development of four ensemble models. Four dominant input variables were selected to conduct the single models. Hence, the coefficients of determination of ANFIS, FFNN, SVM, and MLR were 0.9273, 0.8586, 0.8490, and 0.7956, respectively. The non-linear ensemble approaches performed better than the linear approaches, and the ANFIS ensemble was the best-performing ensemble approach that boosted the predicting performance of the single AI-driven models. This fact revealed the promising capability of ensemble models for predicting the daily mortality due to COVID-19 in other parts of the globe.
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Affiliation(s)
- Kedir Hussein Abegaz
- Biostatistics and Health Informatics, Public Health Department, Madda Walabu University, Robe 247, Ethiopia
- Department of Biostatistics, Faculty of Medicine, Near East University, Near East Avenue, North Cyprus, Mersin 10, Nicosia 99138, Turkey
| | - İlker Etikan
- HOD Biostatistics, Faculty of Medicine, Near East University, Near East Avenue, North Cyprus, Mersin 10, Nicosia 99138, Turkey
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How the COVID-19 Pandemic Affected Attendance at a Tertiary Orthopedic Center Emergency Department: A Comparison between the First and Second Waves. Diagnostics (Basel) 2022; 12:diagnostics12112855. [PMID: 36428919 PMCID: PMC9689342 DOI: 10.3390/diagnostics12112855] [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: 10/14/2022] [Revised: 11/11/2022] [Accepted: 11/13/2022] [Indexed: 11/19/2022] Open
Abstract
Italy was the first European country to face the SARS-CoV-2 virus (COVID-19) pandemic in 2020. The country quickly implemented strategies to contain contagions and re-organize medical resources. We evaluated the COVID-19 effects on the activity of a tertiary-level orthopedic emergency department (ED) during the first and second pandemic waves. We retrospectively collected and compared clinical radiological data of ED admissions during four periods: period A, first pandemic wave; period B, second pandemic wave; period C, three months before the COVID-19 outbreak; period D, same timeframe of the first wave but in 2019. During period A, we found a reduction in ED admissions (-68.2% and -59.9% compared with periods D and C) and a decrease in white codes (non-urgent) (-7.5%) compared with pre-pandemic periods, with a slight increase for all other codes: +6.3% green (urgent, not critical), +0.8% yellow (moderately critical) and +0.3% red (highly urgent, risk of death). We observed an increased rate of fracture diagnosis in period A: +14.9% and +13.3% compared with periods D and C. Our study shows that the COVID-19 pandemic caused a drastic change in the ED patient flow and clinical radiological activity, with a marked reduction in admissions and an increased rate of more severe triage codes and diagnosed fractures.
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Association of the changes in pulmonary artery diameters with clinical outcomes in hospitalized patients with COVID-19 infection: A crosssectional study. MARMARA MEDICAL JOURNAL 2022. [DOI: 10.5472/marumj.1195539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Objective: Enlarged pulmonary artery diameter (PAD) can be associated with mortality risk in coronavirus disease 2019 (COVID-19)
patients. Our aim is to find the factors that cause changes in PAD and the relationship between radiological findings and clinical
outcomes in COVID-19 patients.
Patients and Methods: In this descriptive, retrospective, and single centered study, among the hospitalized 3264 patients, 209 patients
with previous chest computed tomography (CT) were included. Findings of current chest CTs of patients obtained during COVID-19
were compared with that of previous chest CTs. Pulmonary involvements, World Health Organization (WHO) Clinical Progression
Scale scores and laboratory variables were recorded. Intensive Care Unit (ICU) admission, intubation and mortality were clinical
outcomes that were evaluated by using uni – and multivariate analyses.
Results: Patients with high D-dimer had significantly increased risk for enlarged PAD and increase in PAD compared to previous
chest CT (ΔPAD) (OR=1.18, p
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Chen Y, Gong J, He G, Jie Y, Chen J, Wu Y, Hu S, Xu J, Hu B. An early novel prognostic model for predicting 80-day survival of patients with COVID-19. Front Cell Infect Microbiol 2022; 12:1010683. [PMID: 36389149 PMCID: PMC9647191 DOI: 10.3389/fcimb.2022.1010683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/11/2022] [Indexed: 08/23/2023] Open
Abstract
The outbreak of the novel coronavirus disease 2019 (COVID-19) has had an unprecedented impact worldwide, and it is of great significance to predict the prognosis of patients for guiding clinical management. This study aimed to construct a nomogram to predict the prognosis of COVID-19 patients. Clinical records and laboratory results were retrospectively reviewed for 331 patients with laboratory-confirmed COVID-19 from Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital) and Third Affiliated Hospital of Sun Yat-sen University. All COVID-19 patients were followed up for 80 days, and the primary outcome was defined as patient death. Cases were randomly divided into training (n=199) and validation (n=132) groups. Based on baseline data, we used statistically significant prognostic factors to construct a nomogram and assessed its performance. The patients were divided into Death (n=23) and Survival (n=308) groups. Analysis of clinical characteristics showed that these patients presented with fever (n=271, 81.9%), diarrhea (n=20, 6.0%) and had comorbidities (n=89, 26.9.0%). Multivariate Cox regression analysis showed that age, UREA and LDH were independent risk factors for predicting 80-day survival of COVID-19 patients. We constructed a qualitative nomogram with high C-indexes (0.933 and 0.894 in the training and validation groups, respectively). The calibration curve for 80-day survival showed optimal agreement between the predicted and actual outcomes. Decision curve analysis revealed the high clinical net benefit of the nomogram. Overall, our nomogram could effectively predict the 80-day survival of COVID-19 patients and hence assist in providing optimal treatment and decreasing mortality rates.
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Affiliation(s)
- Yaqiong Chen
- Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jiao Gong
- Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Guowei He
- Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yusheng Jie
- Department of Infectious Diseases, Key Laboratory of Liver Disease of Guangdong Province, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jiahao Chen
- Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuankai Wu
- Department of Infectious Diseases, Key Laboratory of Liver Disease of Guangdong Province, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shixiong Hu
- Department of Laboratory Medicine, Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital), Huangshi, Hubei, China
| | - Jixun Xu
- Department of Laboratory Medicine, Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital), Huangshi, Hubei, China
| | - Bo Hu
- Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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Ferrara P, Battiato S, Polosa R. Progress and prospects for artificial intelligence in clinical practice: learning from COVID-19. Intern Emerg Med 2022; 17:1855-1857. [PMID: 36063262 PMCID: PMC9442555 DOI: 10.1007/s11739-022-03080-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 08/11/2022] [Indexed: 12/05/2022]
Affiliation(s)
- Pietro Ferrara
- Center for Public Health Research, University of Milan Bicocca, Monza, Italy
- IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Sebastiano Battiato
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy
| | - Riccardo Polosa
- Center of Excellence for the Acceleration of Harm Reduction (CoEHAR), University of Catania, Catania, Italy.
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
- Institute of Internal Medicine, AOU "Policlinico-V. Emanuele", Via S. Sofia, 78, Catania, Italy.
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Lakhani A, Laturkar N, Dhok A, Mitra K. Prognostic utility of cardiovascular indices in COVID-19 infection: A single-center prospective study in India. J Family Med Prim Care 2022; 11:6297-6302. [PMID: 36618222 PMCID: PMC9810928 DOI: 10.4103/jfmpc.jfmpc_501_22] [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: 03/02/2022] [Revised: 05/22/2022] [Accepted: 06/02/2022] [Indexed: 11/10/2022] Open
Abstract
Background Cardiac signs can show illness progression and severity in a number of respiratory and cardiovascular disorders. The possible importance of CT findings in the prognosis and result of COVID-19 patients is related to the severity of lung disease and cardiac parameters. The CT-assessed cardiac indices are known for predicting the involvement of extent of diseases. Hence, the objective of this study was to correlate the extent of cardiovascular and respiratory involvement in predicting the severity of disease using CT-assessed cardiac indices in Indian population suffering from COVID-19. Methodology A total of 120 COVID-19 patients were included following the inclusion criteria for one year. The confounding factors were assessed and analyzed. The correlation between the cumulative hazard function of death and duration in hospital along with survival rate were done in terms of pulmonary artery-to-aorta ratio (PA/A), and cardiothoracic ratio (CTR). Results The analysis showed mean age of patients to be 49.5(±15.32) years in which mean females were 38(±31.7) and males were 82(±68.3). The interquartile range of CT severity was 8. The PA/A ratio in discharged patients was 0.85 when compared to deceased patients with 1.03 having statistically significant inference (P = 0.00). The CTR (P = 0.00), epicardial adipose thickness (P = 0.00), epicardial adipose density (P = 0.00), and D-dimer (P = 0.007) were showing statistically significant inference. Conclusion The predictive values of CT-assessed cardiac indices might be used for predicting the involvement of cardiovascular and respiratory involvement in COVID-19 patients. It could have an impact on improving the possibilities of survival of patients suffering from COVID-19 in India.
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Affiliation(s)
- Aisha Lakhani
- Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur, Maharashtra, India
| | - Nikhil Laturkar
- Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur, Maharashtra, India
| | - Avinash Dhok
- Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur, Maharashtra, India,Address for correspondence: Dr. Avinash Dhok, Professor and Head, Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur - 440 019, Maharashtra, India. E-mail:
| | - Kajal Mitra
- Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur, Maharashtra, India
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Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Design of an artificial neural network to predict mortality among COVID-19 patients. INFORMATICS IN MEDICINE UNLOCKED 2022; 31:100983. [PMID: 35664686 PMCID: PMC9148440 DOI: 10.1016/j.imu.2022.100983] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/26/2022] [Accepted: 05/26/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction The fast pandemic of coronavirus disease 2019 (COVID-19) has challenged clinicians with many uncertainties and ambiguities regarding disease outcomes and complications. To deal with these uncertainties, our study aimed to develop and evaluate several artificial neural networks (ANNs) to predict the mortality risk in hospitalized COVID-19 patients. Material and methods The data of 1710 hospitalized COVID-19 patients were used in this retrospective and developmental study. First, a Chi-square test (P < 0.05), Eta coefficient (η > 0.4), and binary logistics regression (BLR) analysis were performed to determine the factors affecting COVID-19 mortality. Then, using the selected variables, two types of feed-forward (FF) models, including the back-propagation (BP) and distributed time delay (DTD) were trained. The models' performance was assessed using mean squared error (MSE), error histogram (EH), and area under the ROC curve (AUC-ROC) metrics. Results After applying the univariate and multivariate analysis, 13 variables were selected as important features in predicting COVID-19 mortality at P < 0.05. A comparison of the two ANN architectures using the MSE showed that the BP-ANN (validation error: 0.067, most of the classified samples having 0.049 and 0.05 error rates, and AUC-ROC: 0.888) was the best model. Conclusions Our findings show the acceptable performance of ANN for predicting the risk of mortality in hospitalized COVID-19 patients. Application of the developed ANN-based CDSS in a real clinical environment will improve patient safety and reduce disease severity and mortality.
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Di Meglio L, Carriero S, Biondetti P, Wood BJ, Carrafiello G. Chest imaging in patients with acute respiratory failure because of coronavirus disease 2019. Curr Opin Crit Care 2022; 28:17-24. [PMID: 34864792 PMCID: PMC8711303 DOI: 10.1097/mcc.0000000000000906] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE OF REVIEW This review aims to explore the different imaging modalities, such as chest radiography (CXR), computed tomography (CT), ultrasound, PET/CT scan, and MRI to describe the main features for the evaluation of the chest in COVID-19 patients with ARDS. RECENT FINDINGS This article includes a systematic literature search, evidencing the different chest imaging modalities used in patients with ARDS from COVID-19. Literature evidences different possible approaches going from the conventional CXR and CT to the LUS, MRI, and PET/CT. SUMMARY CT is the technique with higher sensitivity and definition for studying chest in COVID-19 patients. LUS or bedside CXR are critical in patients requiring close and repeated monitoring. Moreover, LUS and CXR reduce the radiation burden and the risk of infection compared with CT. PET/CT and MRI, especially in ARDS patients, are not usually used for diagnostic or follow-up purposes.
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Affiliation(s)
- Letizia Di Meglio
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano
| | - Serena Carriero
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano
| | - Pierpaolo Biondetti
- Operative Unit of Radiology, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Bradford J. Wood
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Gianpaolo Carrafiello
- Operative Unit of Radiology, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
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Hsu SD, Chao E, Chen SJ, Hueng DY, Lan HY, Chiang HH. Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury. J Pers Med 2021; 11:jpm11111144. [PMID: 34834496 PMCID: PMC8618756 DOI: 10.3390/jpm11111144] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/30/2021] [Accepted: 11/02/2021] [Indexed: 12/02/2022] Open
Abstract
Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.
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Affiliation(s)
- Sheng-Der Hsu
- Division of Traumatology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 10490, Taiwan;
| | - En Chao
- Department of Medical Affairs, Song Shan Branch, Tri-Service General Hospital, Taipei 10490, Taiwan;
| | - Sy-Jou Chen
- Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 10490, Taiwan;
| | - Dueng-Yuan Hueng
- Department of Neurological Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 10490, Taiwan;
| | - Hsiang-Yun Lan
- School of Nursing, National Defense Medical Center, No 161, Section 6, Minquan E. Road, Neihu District, Taipei 10490, Taiwan;
| | - Hui-Hsun Chiang
- School of Nursing, National Defense Medical Center, No 161, Section 6, Minquan E. Road, Neihu District, Taipei 10490, Taiwan;
- Correspondence: ; Tel.: +886-2-8792-3100 (ext. 18761); Fax: +886-2-87923109
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Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19. Diagnostics (Basel) 2021; 11:diagnostics11101924. [PMID: 34679622 PMCID: PMC8534829 DOI: 10.3390/diagnostics11101924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/10/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022] Open
Abstract
Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.
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Affiliation(s)
- Tianming Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Quanliang Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Cong Ma
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Xiangyu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Enhua Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Molecular Imaging Research Center, Central South University, Changsha 410008, China
- Correspondence:
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Legal and Regulatory Framework for AI Solutions in Healthcare in EU, US, China, and Russia: New Scenarios after a Pandemic. RADIATION 2021. [DOI: 10.3390/radiation1040022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The COVID-19 crisis has exposed some of the most pressing challenges affecting healthcare and highlighted the benefits that robust integration of digital and AI technologies in the healthcare setting may bring. Although medical solutions based on AI are growing rapidly, regulatory issues and policy initiatives including ownership and control of data, data sharing, privacy protection, telemedicine, and accountability need to be carefully and continually addressed as AI research requires robust and ethical guidelines, demanding an update of the legal and regulatory framework all over the world. Several recently proposed regulatory frameworks provide a solid foundation but do not address a number of issues that may prevent algorithms from being fully trusted. A global effort is needed for an open, mature conversation about the best possible way to guard against and mitigate possible harms to realize the potential of AI across health systems in a respectful and ethical way. This conversation must include national and international policymakers, physicians, digital health and machine learning leaders from industry and academia. If this is done properly and in a timely fashion, the potential of AI in healthcare will be realized.
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