1
|
Shin HJ, Lee EH, Han K, Ryu L, Kim EK. Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results. Sci Rep 2024; 14:14415. [PMID: 38909087 PMCID: PMC11193777 DOI: 10.1038/s41598-024-65488-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 06/20/2024] [Indexed: 06/24/2024] Open
Abstract
This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia between March 2020 and August 2021 were included. We developed prognostic models, including an AI-based consolidation score in addition to the conventional CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥ 65) and pneumonia severity index (PSI) for predicting pneumonia outcomes, defined as 30-day mortality during admission. A total of 489 patients, including 310 and 179 patients in training and test sets, were included. In the training set, the AI-based consolidation score on CXR was a significant variable for predicting the outcome (hazard ratio 1.016, 95% confidence interval [CI] 1.001-1.031). The model that combined CURB-65, initial O2 requirement, intubation, and the AI-based consolidation score showed a significantly high C-index of 0.692 (95% CI 0.628-0.757) compared to other models. In the test set, this model also demonstrated a significantly high C-index of 0.726 (95% CI 0.644-0.809) compared to the conventional CURB-65 and PSI (p < 0.001 and 0.017, respectively). Therefore, a new prognostic model incorporating AI-based CXR results along with traditional pneumonia severity score could be a simple and useful tool for predicting pneumonia outcomes in clinical practice.
Collapse
Affiliation(s)
- Hyun Joo Shin
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, South Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu16995, Yongin-si, Gyeonggi-do, South Korea
| | - Eun Hye Lee
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, South Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu16995, Yongin-si, Gyeonggi-do, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, South Korea
| | - Leeha Ryu
- Department of Biostatistics and Computing, Yonsei University Graduate School, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, South Korea
| | - Eun-Kyung Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, South Korea.
| |
Collapse
|
2
|
Lindroth H, Nalaie K, Raghu R, Ayala IN, Busch C, Bhattacharyya A, Moreno Franco P, Diedrich DA, Pickering BW, Herasevich V. Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings. J Imaging 2024; 10:81. [PMID: 38667979 PMCID: PMC11050909 DOI: 10.3390/jimaging10040081] [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: 01/31/2024] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/28/2024] Open
Abstract
Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.
Collapse
Affiliation(s)
- Heidi Lindroth
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
- Center for Aging Research, Regenstrief Institute, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Center for Health Innovation and Implementation Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Keivan Nalaie
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| | - Roshini Raghu
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
| | - Ivan N. Ayala
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
| | - Charles Busch
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA; (K.N.); (R.R.); (I.N.A.); (C.B.)
- College of Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Pablo Moreno Franco
- Department of Transplantation Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Daniel A. Diedrich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| | - Brian W. Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA; (D.A.D.); (B.W.P.); (V.H.)
| |
Collapse
|
3
|
Arian A, Mehrabi Nejad MM, Zoorpaikar M, Hasanzadeh N, Sotoudeh-Paima S, Kolahi S, Gity M, Soltanian-Zadeh H. Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis. PLoS One 2023; 18:e0294899. [PMID: 38064442 PMCID: PMC10707659 DOI: 10.1371/journal.pone.0294899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 11/11/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19. OBJECTIVES This study investigates the performance of an AI-aided quantification model in predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with radiologists' performance. SUBJECTS AND METHODS A total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were recruited in this cross-sectional study. Quantification of the total and compromised lung parenchyma was performed by two expert radiologists using a volumetric image analysis software and compared against an AI-assisted package consisting of a modified U-Net model for segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for segmenting lung volume. The fraction of compromised lung parenchyma (%CL) was calculated. Based on clinical results, the subjects were divided into two categories: critical (n = 45) and noncritical (n = 45). All admission data were compared between the two groups. RESULTS There was an excellent agreement between the radiologist-obtained and AI-assisted measurements (intraclass correlation coefficient = 0.88, P < 0.001). Both the AI-assisted and radiologist-obtained %CLs were significantly higher in the critical subjects (P = 0.009 and 0.02, respectively) than in the noncritical subjects. In the multivariate logistic regression analysis to distinguish the critical subjects, an AI-assisted %CL ≥35% (odds ratio [OR] = 17.0), oxygen saturation level of <88% (OR = 33.6), immunocompromised condition (OR = 8.1), and other comorbidities (OR = 15.2) independently remained as significant variables in the models. Our proposed model obtained an accuracy of 83.9%, a sensitivity of 79.1%, and a specificity of 88.6% in predicting critical outcomes. CONCLUSIONS AI-assisted measurements are similar to quantitative radiologist-obtained measurements in determining lung involvement in COVID-19 subjects.
Collapse
Affiliation(s)
- Arvin Arian
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad-Mehdi Mehrabi Nejad
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mostafa Zoorpaikar
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Navid Hasanzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Saman Sotoudeh-Paima
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Shahriar Kolahi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Masoumeh Gity
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| |
Collapse
|
4
|
Shin HJ, Kim MH, Son NH, Han K, Kim EK, Kim YC, Park YS, Lee EH, Kyong T. Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients. Diagnostics (Basel) 2023; 13:2090. [PMID: 37370985 DOI: 10.3390/diagnostics13122090] [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: 05/20/2023] [Revised: 06/15/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
This study aimed to investigate the clinical implications and prognostic value of artificial intelligence (AI)-based results for chest radiographs (CXR) in coronavirus disease 2019 (COVID-19) patients. Patients who were admitted due to COVID-19 from September 2021 to March 2022 were retrospectively included. A commercial AI-based software was used to assess CXR data for consolidation and pleural effusion scores. Clinical data, including laboratory results, were analyzed for possible prognostic factors. Total O2 supply period, the last SpO2 result, and deterioration were evaluated as prognostic indicators of treatment outcome. Generalized linear mixed model and regression tests were used to examine the prognostic value of CXR results. Among a total of 228 patients (mean 59.9 ± 18.8 years old), consolidation scores had a significant association with erythrocyte sedimentation rate and C-reactive protein changes, and initial consolidation scores were associated with the last SpO2 result (estimate -0.018, p = 0.024). All consolidation scores during admission showed significant association with the total O2 supply period and the last SpO2 result. Early changing degree of consolidation score showed an association with deterioration (odds ratio 1.017, 95% confidence interval 1.005-1.03). In conclusion, AI-based CXR results for consolidation have potential prognostic value for predicting treatment outcomes in COVID-19 patients.
Collapse
Affiliation(s)
- Hyun Joo Shin
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Min Hyung Kim
- Division of Infectious Diseases, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Nak-Hoon Son
- Department of Statistics, Keimyung University, Daegu 42601, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Eun-Kyung Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Yong Chan Kim
- Division of Infectious Diseases, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Yoon Soo Park
- Division of Infectious Diseases, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Eun Hye Lee
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Taeyoung Kyong
- Department of Hospital Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| |
Collapse
|
5
|
Yoo SJ, Kim H, Witanto JN, Inui S, Yoon JH, Lee KD, Choi YW, Goo JM, Yoon SH. Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs. Eur J Radiol 2023; 164:110858. [PMID: 37209462 DOI: 10.1016/j.ejrad.2023.110858] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/10/2023] [Accepted: 04/29/2023] [Indexed: 05/22/2023]
Abstract
PURPOSE To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. MATERIALS AND METHODS This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54-375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243-1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent. RESULTS GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were -27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05-1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614-0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643-0.841 and 0.688-0.877, respectively. CONCLUSION The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.
Collapse
Affiliation(s)
- Seung-Jin Yoo
- Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea
| | | | - Shohei Inui
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Radiology, Japan Self-Defense Forces Central Hospital, Tokyo, Japan
| | - Jeong-Hwa Yoon
- Institute of Health Policy and Management, Medical Research Center, Seoul National University, Seoul, South Korea
| | - Ki-Deok Lee
- Division of Infectious diseases, Department of Internal Medicine, Myongji Hospital, Goyang, Korea
| | - Yo Won Choi
- Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea; MEDICALIP Co. Ltd., Seoul, Korea
| |
Collapse
|
6
|
Gasulla Ó, Ledesma-Carbayo MJ, Borrell LN, Fortuny-Profitós J, Mazaira-Font FA, Barbero Allende JM, Alonso-Menchén D, García-Bennett J, Del Río-Carrrero B, Jofré-Grimaldo H, Seguí A, Monserrat J, Teixidó-Román M, Torrent A, Ortega MÁ, Álvarez-Mon M, Asúnsolo A. Enhancing physicians' radiology diagnostics of COVID-19's effects on lung health by leveraging artificial intelligence. Front Bioeng Biotechnol 2023; 11:1010679. [PMID: 37152658 PMCID: PMC10157246 DOI: 10.3389/fbioe.2023.1010679] [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: 08/03/2022] [Accepted: 03/14/2023] [Indexed: 05/09/2023] Open
Abstract
Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19's effects on patients' lung health. Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU). Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians' diagnosis, and test for improvements on physicians' performance when using the prediction algorithm. Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%.
Collapse
Affiliation(s)
- Óscar Gasulla
- Hospital Universitari de Bellvitge-Universitat de Barcelona, L´Hospitalet de Llobregat, Spain
- Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Alcala de Henares, Spain
| | - Maria J. Ledesma-Carbayo
- Biomedical Image Technologies, Universidad Politécnica de Madrid & CIBER BBN, ISCIII, Madrid, Spain
| | - Luisa N. Borrell
- Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Alcala de Henares, Spain
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, University of New York, New York, NY, United States
| | | | - Ferran A. Mazaira-Font
- Departament d'Econometria, Estadística i Economia Aplicada-Universitat de Barcelona, Barcelona, Spain
| | - Jose María Barbero Allende
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
| | - David Alonso-Menchén
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
| | - Josep García-Bennett
- Hospital Universitari de Bellvitge-Universitat de Barcelona, L´Hospitalet de Llobregat, Spain
| | - Belen Del Río-Carrrero
- Hospital Universitari de Bellvitge-Universitat de Barcelona, L´Hospitalet de Llobregat, Spain
| | - Hector Jofré-Grimaldo
- Hospital Universitari de Bellvitge-Universitat de Barcelona, L´Hospitalet de Llobregat, Spain
| | - Aleix Seguí
- Campus Nord, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Jorge Monserrat
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
| | - Miguel Teixidó-Román
- Departament d'Econometria, Estadística i Economia Aplicada-Universitat de Barcelona, Barcelona, Spain
| | - Adrià Torrent
- Departament d'Econometria, Estadística i Economia Aplicada-Universitat de Barcelona, Barcelona, Spain
| | - Miguel Ángel Ortega
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
| | - Melchor Álvarez-Mon
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
- Service of Internal Medicine and Immune System Diseases-Rheumatology, University Hospital Príncipe de Asturias, (CIBEREHD), Alcalá de Henares, Spain
| | - Angel Asúnsolo
- Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Alcala de Henares, Spain
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, University of New York, New York, NY, United States
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
| |
Collapse
|
7
|
Vasilev Y, Vladzymyrskyy A, Omelyanskaya O, Blokhin I, Kirpichev Y, Arzamasov K. AI-Based CXR First Reading: Current Limitations to Ensure Practical Value. Diagnostics (Basel) 2023; 13:diagnostics13081430. [PMID: 37189531 DOI: 10.3390/diagnostics13081430] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023] Open
Abstract
We performed a multicenter external evaluation of the practical and clinical efficacy of a commercial AI algorithm for chest X-ray (CXR) analysis (Lunit INSIGHT CXR). A retrospective evaluation was performed with a multi-reader study. For a prospective evaluation, the AI model was run on CXR studies; the results were compared to the reports of 226 radiologists. In the multi-reader study, the area under the curve (AUC), sensitivity, and specificity of the AI were 0.94 (CI95%: 0.87-1.0), 0.9 (CI95%: 0.79-1.0), and 0.89 (CI95%: 0.79-0.98); the AUC, sensitivity, and specificity of the radiologists were 0.97 (CI95%: 0.94-1.0), 0.9 (CI95%: 0.79-1.0), and 0.95 (CI95%: 0.89-1.0). In most regions of the ROC curve, the AI performed a little worse or at the same level as an average human reader. The McNemar test showed no statistically significant differences between AI and radiologists. In the prospective study with 4752 cases, the AUC, sensitivity, and specificity of the AI were 0.84 (CI95%: 0.82-0.86), 0.77 (CI95%: 0.73-0.80), and 0.81 (CI95%: 0.80-0.82). Lower accuracy values obtained during the prospective validation were mainly associated with false-positive findings considered by experts to be clinically insignificant and the false-negative omission of human-reported "opacity", "nodule", and calcification. In a large-scale prospective validation of the commercial AI algorithm in clinical practice, lower sensitivity and specificity values were obtained compared to the prior retrospective evaluation of the data of the same population.
Collapse
Affiliation(s)
- Yuriy Vasilev
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Anton Vladzymyrskyy
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
- Department of Information and Internet Technologies, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Trubetskaya Street, 8, Building 2, 119991 Moscow, Russia
| | - Olga Omelyanskaya
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Ivan Blokhin
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Yury Kirpichev
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Kirill Arzamasov
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| |
Collapse
|
8
|
Nair A, Procter A, Halligan S, Parry T, Ahmed A, Duncan M, Taylor M, Chouhan M, Gaunt T, Roberts J, van Vucht N, Campbell A, Davis LM, Jacob J, Hubbard R, Kumar S, Said A, Chan X, Cutfield T, Luintel A, Marks M, Stone N, Mallet S. Chest radiograph classification and severity of suspected COVID-19 by different radiologist groups and attending clinicians: multi-reader, multi-case study. Eur Radiol 2023; 33:2096-2104. [PMID: 36282308 PMCID: PMC9592875 DOI: 10.1007/s00330-022-09172-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 07/19/2022] [Accepted: 08/24/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To quantify reader agreement for the British Society of Thoracic Imaging (BSTI) diagnostic and severity classification for COVID-19 on chest radiographs (CXR), in particular agreement for an indeterminate CXR that could instigate CT imaging, from single and paired images. METHODS Twenty readers (four groups of five individuals)-consultant chest (CCR), general consultant (GCR), and specialist registrar (RSR) radiologists, and infectious diseases clinicians (IDR)-assigned BSTI categories and severity in addition to modified Covid-Radiographic Assessment of Lung Edema Score (Covid-RALES), to 305 CXRs (129 paired; 2 time points) from 176 guideline-defined COVID-19 patients. Percentage agreement with a consensus of two chest radiologists was calculated for (1) categorisation to those needing CT (indeterminate) versus those that did not (classic/probable, non-COVID-19); (2) severity; and (3) severity change on paired CXRs using the two scoring systems. RESULTS Agreement with consensus for the indeterminate category was low across all groups (28-37%). Agreement for other BSTI categories was highest for classic/probable for the other three reader groups (66-76%) compared to GCR (49%). Agreement for normal was similar across all radiologists (54-61%) but lower for IDR (31%). Agreement for a severe CXR was lower for GCR (65%), compared to the other three reader groups (84-95%). For all groups, agreement for changes across paired CXRs was modest. CONCLUSION Agreement for the indeterminate BSTI COVID-19 CXR category is low, and generally moderate for the other BSTI categories and for severity change, suggesting that the test, rather than readers, is limited in utility for both deciding disposition and serial monitoring. KEY POINTS • Across different reader groups, agreement for COVID-19 diagnostic categorisation on CXR varies widely. • Agreement varies to a degree that may render CXR alone ineffective for triage, especially for indeterminate cases. • Agreement for serial CXR change is moderate, limiting utility in guiding management.
Collapse
Affiliation(s)
- Arjun Nair
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
| | - Alexander Procter
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Steve Halligan
- Centre for Medical Imaging, University College London, UCL Centre for Medical Imaging, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Thomas Parry
- Centre for Medical Imaging, University College London, UCL Centre for Medical Imaging, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Asia Ahmed
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Mark Duncan
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Magali Taylor
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Manil Chouhan
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Trevor Gaunt
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - James Roberts
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Niels van Vucht
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Alan Campbell
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Laura May Davis
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Joseph Jacob
- Centre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, Floor 1, London, WC1V 6LJ, UK
| | - Rachel Hubbard
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Shankar Kumar
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Ammaarah Said
- Department of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Xinhui Chan
- Department of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Tim Cutfield
- Department of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Akish Luintel
- Department of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Michael Marks
- Department of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Neil Stone
- Department of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Sue Mallet
- Centre for Medical Imaging, University College London, UCL Centre for Medical Imaging, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| |
Collapse
|
9
|
Lee JH, Koh J, Jeon YK, Goo JM, Yoon SH. An Integrated Radiologic-Pathologic Understanding of COVID-19 Pneumonia. Radiology 2023; 306:e222600. [PMID: 36648343 PMCID: PMC9868683 DOI: 10.1148/radiol.222600] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/07/2022] [Accepted: 12/09/2022] [Indexed: 01/18/2023]
Abstract
This article reviews the radiologic and pathologic findings of the epithelial and endothelial injuries in COVID-19 pneumonia to help radiologists understand the fundamental nature of the disease. The radiologic and pathologic manifestations of COVID-19 pneumonia result from epithelial and endothelial injuries based on viral toxicity and immunopathologic effects. The pathologic features of mild and reversible COVID-19 pneumonia involve nonspecific pneumonia or an organizing pneumonia pattern, while the pathologic features of potentially fatal and irreversible COVID-19 pneumonia are characterized by diffuse alveolar damage followed by fibrosis or acute fibrinous organizing pneumonia. These pathologic responses of epithelial injuries observed in COVID-19 pneumonia are not specific to SARS-CoV-2 but rather constitute universal responses to viral pneumonia. Endothelial injury in COVID-19 pneumonia is a prominent feature compared with other types of viral pneumonia and encompasses various vascular abnormalities at different levels, including pulmonary thromboembolism, vascular engorgement, peripheral vascular reduction, a vascular tree-in-bud pattern, and lung perfusion abnormality. Chest CT with different imaging techniques (eg, CT quantification, dual-energy CT perfusion) can fully capture the various manifestations of epithelial and endothelial injuries. CT can thus aid in establishing prognosis and identifying patients at risk for deterioration.
Collapse
Affiliation(s)
- Jong Hyuk Lee
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Jaemoon Koh
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Yoon Kyung Jeon
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Jin Mo Goo
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Soon Ho Yoon
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| |
Collapse
|
10
|
Al-Yousif N, Komanduri S, Qurashi H, Korzhuk A, Lawal HO, Abourizk N, Schaefer C, Mitchell KJ, Dietz CM, Hughes EK, Brandt CS, Fitzgerald GM, Joyce R, Chaudhry AS, Kotok D, Rivera JD, Kim AI, Shettigar S, Lavina A, Girard CE, Gillenwater SR, Hadeh A, Bain W, Shah FA, Bittner M, Lu M, Prendergast N, Evankovich J, Golubykh K, Ramesh N, Jacobs JJ, Kessinger C, Methe B, Lee JS, Morris A, McVerry BJ, Kitsios GD. Inter-rater reliability and prognostic value of baseline Radiographic Assessment of Lung Edema (RALE) scores in observational cohort studies of inpatients with COVID-19. BMJ Open 2023; 13:e066626. [PMID: 36635036 PMCID: PMC9842602 DOI: 10.1136/bmjopen-2022-066626] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 12/16/2022] [Indexed: 01/14/2023] Open
Abstract
OBJECTIVES To reliably quantify the radiographic severity of COVID-19 pneumonia with the Radiographic Assessment of Lung Edema (RALE) score on clinical chest X-rays among inpatients and examine the prognostic value of baseline RALE scores on COVID-19 clinical outcomes. SETTING Hospitalised patients with COVID-19 in dedicated wards and intensive care units from two different hospital systems. PARTICIPANTS 425 patients with COVID-19 in a discovery data set and 415 patients in a validation data set. PRIMARY AND SECONDARY OUTCOMES We measured inter-rater reliability for RALE score annotations by different reviewers and examined for associations of consensus RALE scores with the level of respiratory support, demographics, physiologic variables, applied therapies, plasma host-response biomarkers, SARS-CoV-2 RNA load and clinical outcomes. RESULTS Inter-rater agreement for RALE scores improved from fair to excellent following reviewer training and feedback (intraclass correlation coefficient of 0.85 vs 0.93, respectively). In the discovery cohort, the required level of respiratory support at the time of CXR acquisition (supplemental oxygen or non-invasive ventilation (n=178); invasive-mechanical ventilation (n=234), extracorporeal membrane oxygenation (n=13)) was significantly associated with RALE scores (median (IQR): 20.0 (14.1-26.7), 26.0 (20.5-34.0) and 44.5 (34.5-48.0), respectively, p<0.0001). Among invasively ventilated patients, RALE scores were significantly associated with worse respiratory mechanics (plateau and driving pressure) and gas exchange metrics (PaO2/FiO2 and ventilatory ratio), as well as higher plasma levels of IL-6, soluble receptor of advanced glycation end-products and soluble tumour necrosis factor receptor 1 (p<0.05). RALE scores were independently associated with 90-day survival in a multivariate Cox proportional hazards model (adjusted HR 1.04 (1.02-1.07), p=0.002). We replicated the significant associations of RALE scores with baseline disease severity and mortality in the independent validation data set. CONCLUSIONS With a reproducible method to measure radiographic severity in COVID-19, we found significant associations with clinical and physiologic severity, host inflammation and clinical outcomes. The incorporation of radiographic severity assessments in clinical decision-making may provide important guidance for prognostication and treatment allocation in COVID-19.
Collapse
Affiliation(s)
- Nameer Al-Yousif
- Internal Medicine Residency Program, UPMC Mercy, Pittsburgh, Pennsylvania, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, MetroHealth Medical Center, Cleveland, Ohio, USA
| | - Saketram Komanduri
- Internal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, Pennsylvania, USA
| | - Hafiz Qurashi
- Internal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, Pennsylvania, USA
| | - Anatoliy Korzhuk
- Internal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, Pennsylvania, USA
| | - Halimat O Lawal
- Internal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, Pennsylvania, USA
| | - Nicholas Abourizk
- Internal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, Pennsylvania, USA
| | - Caitlin Schaefer
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Acute Lung Injury Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Kevin J Mitchell
- Computer Vision Group, Veytel LLC, Pittsburgh, Pennsylvania, USA
| | | | - Ellen K Hughes
- Computer Vision Group, Veytel LLC, Pittsburgh, Pennsylvania, USA
| | - Clara S Brandt
- Computer Vision Group, Veytel LLC, Pittsburgh, Pennsylvania, USA
| | | | - Robin Joyce
- Computer Vision Group, Veytel LLC, Pittsburgh, Pennsylvania, USA
| | - Asmaa S Chaudhry
- Computer Vision Group, Veytel LLC, Pittsburgh, Pennsylvania, USA
| | - Daniel Kotok
- Department of Pulmonary and Critical Care, Cleveland Clinic Florida, Weston, Florida, USA
| | - Jose D Rivera
- Department of Pulmonary and Critical Care, Cleveland Clinic Florida, Weston, Florida, USA
| | - Andrew I Kim
- Department of Pulmonary and Critical Care, Cleveland Clinic Florida, Weston, Florida, USA
| | - Shruti Shettigar
- Department of Pulmonary and Critical Care, Cleveland Clinic Florida, Weston, Florida, USA
| | - Allen Lavina
- Department of Pulmonary and Critical Care, Cleveland Clinic Florida, Weston, Florida, USA
| | - Christine E Girard
- Department of Pulmonary and Critical Care, Cleveland Clinic Florida, Weston, Florida, USA
| | - Samantha R Gillenwater
- Department of Pulmonary and Critical Care, Cleveland Clinic Florida, Weston, Florida, USA
| | - Anas Hadeh
- Department of Pulmonary and Critical Care, Cleveland Clinic Florida, Weston, Florida, USA
| | - William Bain
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Acute Lung Injury Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Faraaz A Shah
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Acute Lung Injury Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Matthew Bittner
- Internal Medicine Residency Program, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michael Lu
- Internal Medicine Residency Program, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Niall Prendergast
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - John Evankovich
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Acute Lung Injury Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Konstantin Golubykh
- Internal Medicine Residency Program, UPMC Pinnacle Harrisburg, Harrisburg, Pennsylvania, USA
| | - Navitha Ramesh
- Department of Pulmonary and Critical Care, UPMC Pinnacle Harrisburg, Harrisburg, Pennsylvania, USA
| | - Jana J Jacobs
- Department of Medicine, Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Cathy Kessinger
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Barbara Methe
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Janet S Lee
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Acute Lung Injury Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Alison Morris
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Acute Lung Injury Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Bryan J McVerry
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Acute Lung Injury Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Georgios D Kitsios
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Acute Lung Injury Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| |
Collapse
|
11
|
Govindarajan A, Govindarajan A, Tanamala S, Chattoraj S, Reddy B, Agrawal R, Iyer D, Srivastava A, Kumar P, Putha P. Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study. Diagnostics (Basel) 2022; 12:2724. [PMID: 36359565 PMCID: PMC9689183 DOI: 10.3390/diagnostics12112724] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/03/2022] [Indexed: 11/10/2023] Open
Abstract
In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However, the current workload in extensive health care facilities and lack of well-trained radiologists is a significant challenge in the patient care pathway. Therefore, an accurate, reliable, and fast computer-aided diagnosis (CAD) system capable of detecting abnormalities in chest X-rays is crucial in improving the radiological workflow. In this prospective multicenter quality-improvement study, we have evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings. Methods: A team of radiologists used the AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multicentre study. This study took place in a large radiology network in India between June 2021 and March 2022. Results: A total of 65,604 chest X-rays were processed during the study period. The overall performance of AI achieved in detecting normal and abnormal chest X-rays was good. The high negatively predicted value (NPV) of 98.9% was achieved. The AI performance in terms of area under the curve (AUC), NPV for the corresponding subabnormalities obtained were blunted CP angle (0.97, 99.5%), hilar dysmorphism (0.86, 99.9%), cardiomegaly (0.96, 99.7%), reticulonodular pattern (0.91, 99.9%), rib fracture (0.98, 99.9%), scoliosis (0.98, 99.9%), atelectasis (0.96, 99.9%), calcification (0.96, 99.7%), consolidation (0.95, 99.6%), emphysema (0.96, 99.9%), fibrosis (0.95, 99.7%), nodule (0.91, 99.8%), opacity (0.92, 99.2%), pleural effusion (0.97, 99.7%), and pneumothorax (0.99, 99.9%). Additionally, the turnaround time (TAT) decreased by about 40.63% from pre-qXR period to post-qXR period. Conclusions: The AI-based chest X-ray solution (qXR) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways.
Collapse
|
12
|
Worku ET, Yeung F, Anstey C, Shekar K. The impact of reduction in intensity of mechanical ventilation upon venovenous ECMO initiation on radiographically assessed lung edema scores: A retrospective observational study. Front Med (Lausanne) 2022; 9:1005192. [PMID: 36203770 PMCID: PMC9531725 DOI: 10.3389/fmed.2022.1005192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 08/26/2022] [Indexed: 11/26/2022] Open
Abstract
Background Patients with severe acute respiratory distress syndrome (ARDS) typically receive ultra-protective ventilation after extracorporeal membrane oxygenation (ECMO) is initiated. While the benefit of ECMO appears to derive from supporting “lung rest”, reductions in the intensity of mechanical ventilation, principally tidal volume limitation, may manifest radiologically. This study evaluated the relative changes in radiographic assessment of lung edema (RALE) score upon venovenous ECMO initiation in patients with severe ARDS. Methods Digital chest x-rays (CXR) performed at baseline immediately before initiation of ECMO, and at intervals post (median 1.1, 2.1, and 9.6 days) were reviewed in 39 Adult ARDS patients. One hundred fifty-six digital images were scored by two independent, blinded radiologists according to the RALE (Radiographic Assessment of Lung Edema) scoring criteria. Ventilatory data, ECMO parameters and fluid balance were recorded at corresponding time points. Multivariable analysis was performed analyzing the change in RALE score over time relative to baseline. Results The RALE score demonstrated excellent inter-rater agreement in this novel application in an ECMO cohort. Mean RALE scores increased from 28 (22–37) at baseline to 35 (26–42) (p < 0.001) on D1 of ECMO; increasing RALE was associated with higher baseline APACHE III scores [ß value +0.19 (0.08, 0.30) p = 0.001], and greater reductions in tidal volume [ß value −2.08 (−3.07, −1.10) p < 0.001] after ECMO initiation. Duration of mechanical ventilation, and ECMO support did not differ between survivors and non-survivors. Conclusions The magnitude of reductions in delivered tidal volumes correlated with increasing RALE scores (radiographic worsening) in ARDS patients receiving ECMO. Implications for patient centered outcomes remain unclear. There is a need to define appropriate ventilator settings on venovenous ECMO, counterbalancing the risks vs. benefits of optimal “lung rest” against potential atelectrauma.
Collapse
Affiliation(s)
- Elliott T. Worku
- Adult Intensive Care Services, The Prince Charles Hospital, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Elliott T. Worku
| | - Francis Yeung
- Adult Intensive Care Services, The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Chris Anstey
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- School of Medicine, Griffith University, Sunshine Coast Campus, Birtinya, QLD, Australia
| | - Kiran Shekar
- Adult Intensive Care Services, The Prince Charles Hospital, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| |
Collapse
|
13
|
Valk CM, Zimatore C, Mazzinari G, Pierrakos C, Sivakorn C, Dechsanga J, Grasso S, Beenen L, Bos LDJ, Paulus F, Schultz MJ, Pisani L. The RALE Score Versus the CT Severity Score in Invasively Ventilated COVID-19 Patients-A Retrospective Study Comparing Their Prognostic Capacities. Diagnostics (Basel) 2022; 12:2072. [PMID: 36140474 PMCID: PMC9497927 DOI: 10.3390/diagnostics12092072] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Quantitative radiological scores for the extent and severity of pulmonary infiltrates based on chest radiography (CXR) and computed tomography (CT) scan are increasingly used in critically ill invasively ventilated patients. This study aimed to determine and compare the prognostic capacity of the Radiographic Assessment of Lung Edema (RALE) score and the chest CT Severity Score (CTSS) in a cohort of invasively ventilated patients with acute respiratory distress syndrome (ARDS) due to COVID-19. METHODS Two-center retrospective observational study, including consecutive invasively ventilated COVID-19 patients. Trained scorers calculated the RALE score of first available CXR and the CTSS of the first available CT scan. The primary outcome was ICU mortality; secondary outcomes were duration of ventilation in survivors, length of stay in ICU, and hospital-, 28-, and 90-day mortality. Prognostic accuracy for ICU death was expressed using odds ratios and Area Under the Receiver Operating Characteristic curves (AUROC). RESULTS A total of 82 patients were enrolled. The median RALE score (22 [15-37] vs. 26 [20-39]; p = 0.34) and the median CTSS (18 [16-21] vs. 21 [18-23]; p = 0.022) were both lower in ICU survivors compared to ICU non-survivors, although only the difference in CTSS reached statistical significance. While no association was observed between ICU mortality and RALE score (OR 1.35 [95%CI 0.64-2.84]; p = 0.417; AUC 0.50 [0.44-0.56], this was noticed with the CTSS (OR, 2.31 [1.22-4.38]; p = 0.010) although with poor prognostic capacity (AUC 0.64 [0.57-0.69]). The correlation between the RALE score and CTSS was weak (r2 = 0.075; p = 0.012). CONCLUSIONS Despite poor prognostic capacity, only CTSS was associated with ICU mortality in our cohort of COVID-19 patients.
Collapse
Affiliation(s)
- Christel M. Valk
- Department of Intensive Care & Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Location ‘AMC’, 1105 AZ Amsterdam, The Netherlands
| | - Claudio Zimatore
- Department of Intensive Care & Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Location ‘AMC’, 1105 AZ Amsterdam, The Netherlands
- Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, 70124 Bari, Italy
| | - Guido Mazzinari
- Department of Anaesthesiology and Critical Care, Hospital Universitario y Politecnico la Fe, 46026 Valencia, Spain
- Perioperative Medicine Research Group, Instituto de Investigación Sanitaria la Fe, 46026 Valencia, Spain
| | - Charalampos Pierrakos
- Department of Intensive Care & Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Location ‘AMC’, 1105 AZ Amsterdam, The Netherlands
- Department of Intensive Care, Centre Hospitalier Universitaire Brussels, 1020 Brussels, Belgium
| | - Chaisith Sivakorn
- Intensive Care Unit, NHS Foundation Trust, University College London Hospitals, London NW1 2BU, UK
| | - Jutamas Dechsanga
- Division of Pulmonary and Critical Care, Department of Medicine, Chonburi Hospital, Chonburi 20000, Thailand
| | - Salvatore Grasso
- Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, 70124 Bari, Italy
| | - Ludo Beenen
- Department of Radiology, Amsterdam UMC, Location ‘AMC’, 1105 AZ Amsterdam, The Netherlands
| | - Lieuwe D. J. Bos
- Department of Intensive Care & Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Location ‘AMC’, 1105 AZ Amsterdam, The Netherlands
- Department of Pulmonology, Amsterdam UMC, Location ‘AMC’, 1105 AZ Amsterdam, The Netherlands
| | - Frederique Paulus
- Department of Intensive Care & Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Location ‘AMC’, 1105 AZ Amsterdam, The Netherlands
| | - Marcus J. Schultz
- Department of Intensive Care & Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Location ‘AMC’, 1105 AZ Amsterdam, The Netherlands
- Mahidol-Oxford Tropical Medicine Research Unit (MORU), Mahidol University, Bangkok 10400, Thailand
- Nuffield Department of Medicine, University of Oxford, Oxford OX1 2JD, UK
| | - Luigi Pisani
- Department of Intensive Care & Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Location ‘AMC’, 1105 AZ Amsterdam, The Netherlands
- Mahidol-Oxford Tropical Medicine Research Unit (MORU), Mahidol University, Bangkok 10400, Thailand
- Anaesthesia and Intensive Care Unit, Miulli Regional Hospital, 70021 Acquaviva Delle Fonti, Italy
| |
Collapse
|
14
|
Ahn JS, Ebrahimian S, McDermott S, Lee S, Naccarato L, Di Capua JF, Wu MY, Zhang EW, Muse V, Miller B, Sabzalipour F, Bizzo BC, Dreyer KJ, Kaviani P, Digumarthy SR, Kalra MK. Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency. JAMA Netw Open 2022; 5:e2229289. [PMID: 36044215 PMCID: PMC9434361 DOI: 10.1001/jamanetworkopen.2022.29289] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
IMPORTANCE The efficient and accurate interpretation of radiologic images is paramount. OBJECTIVE To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader performance and efficiency in interpreting chest radiograph abnormalities. DESIGN, SETTING, AND PARTICIPANTS This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray [MIMIC-CXR]) and Massachusetts General Hospital (MGH). MAIN OUTCOMES AND MEASURES The ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding. RESULTS A total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean [SD] age, 63 [16] years; 133 men [53.2%])-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.816 [95% CI, 0.732-0.882] vs 0.567 [95% CI, 0.524-0.611]; pneumonia, 0.887 [95% CI, 0.834-0.928] vs 0.673 [95% CI, 0.632-0.714]; pleural effusion, 0.872 [95% CI, 0.808-0.921] vs 0.889 [95% CI, 0.862-0.917]; pneumothorax, 0.988 [95% CI, 0.932-1.000] vs 0.792 [95% CI, 0.756-0.827]). AI-aided interpretation was associated with significantly improved reader sensitivities for all target findings, without negative impacts on the specificity. Overall, the AUROCs of readers improved for all 4 target findings, with significant improvements in detection of pneumothorax and nodule. The reporting time with AI was 10% lower than without AI (40.8 vs 36.9 seconds; difference, 3.9 seconds; 95% CI, 2.9-5.2 seconds; P < .001). CONCLUSIONS AND RELEVANCE These findings suggest that AI-aided interpretation was associated with improved reader performance and efficiency for identifying major thoracic findings on a chest radiograph.
Collapse
Affiliation(s)
| | - Shadi Ebrahimian
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Internal Medicine, Icahn School of Medicine at Mount Sinai, Elmhurst Hospital Center, Elmhurst, New York
| | - Shaunagh McDermott
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | | | - Laura Naccarato
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - John F. Di Capua
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Markus Y. Wu
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Eric W. Zhang
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Victorine Muse
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Benjamin Miller
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| | - Farid Sabzalipour
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| | - Bernardo C. Bizzo
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| | - Keith J. Dreyer
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| | - Parisa Kaviani
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Subba R. Digumarthy
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mannudeep K. Kalra
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| |
Collapse
|
15
|
Danilov VV, Litmanovich D, Proutski A, Kirpich A, Nefaridze D, Karpovsky A, Gankin Y. Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow. Sci Rep 2022; 12:12791. [PMID: 35896761 PMCID: PMC9326426 DOI: 10.1038/s41598-022-15013-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 06/16/2022] [Indexed: 11/30/2022] Open
Abstract
In this study, we propose a two-stage workflow used for the segmentation and scoring of lung diseases. The workflow inherits quantification, qualification, and visual assessment of lung diseases on X-ray images estimated by radiologists and clinicians. It requires the fulfillment of two core stages devoted to lung and disease segmentation as well as an additional post-processing stage devoted to scoring. The latter integrated block is utilized, mainly, for the estimation of segment scores and computes the overall severity score of a patient. The models of the proposed workflow were trained and tested on four publicly available X-ray datasets of COVID-19 patients and two X-ray datasets of patients with no pulmonary pathology. Based on a combined dataset consisting of 580 COVID-19 patients and 784 patients with no disorders, our best-performing algorithm is based on a combination of DeepLabV3 + , for lung segmentation, and MA-Net, for disease segmentation. The proposed algorithms' mean absolute error (MAE) of 0.30 is significantly reduced in comparison to established COVID-19 algorithms; BS-net and COVID-Net-S, possessing MAEs of 2.52 and 1.83 respectively. Moreover, the proposed two-stage workflow was not only more accurate but also computationally efficient, it was approximately 11 times faster than the mentioned methods. In summary, we proposed an accurate, time-efficient, and versatile approach for segmentation and scoring of lung diseases illustrated for COVID-19 and with broader future applications for pneumonia, tuberculosis, pneumothorax, amongst others.
Collapse
|
16
|
Al-Yousif N, Komanduri S, Qurashi H, Korzhuk A, Lawal HO, Abourizk N, Schaefer C, Mitchell KJ, Dietz CM, Hughes EK, Brandt CS, Fitzgerald GM, Joyce R, Chaudhry AS, Kotok D, Rivera JD, Kim AI, Shettigar S, Lavina A, Girard CE, Gillenwater SR, Hadeh A, Bain W, Shah FA, Bittner M, Lu M, Prendergast N, Evankovich J, Golubykh K, Ramesh N, Jacobs JJ, Kessinger C, Methé B, Lee JS, Morris A, McVerry BJ, Kitsios GD. Radiographic Assessment of Lung Edema (RALE) Scores are Highly Reproducible and Prognostic of Clinical Outcomes for Inpatients with COVID-19. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.06.10.22276249. [PMID: 35734089 PMCID: PMC9216727 DOI: 10.1101/2022.06.10.22276249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
INTRODUCTION Chest imaging is necessary for diagnosis of COVID-19 pneumonia, but current risk stratification tools do not consider radiographic severity. We quantified radiographic heterogeneity among inpatients with COVID-19 with the Radiographic Assessment of Lung Edema (RALE) score on Chest X-rays (CXRs). METHODS We performed independent RALE scoring by ≥2 reviewers on baseline CXRs from 425 inpatients with COVID-19 (discovery dataset), we recorded clinical variables and outcomes, and measured plasma host-response biomarkers and SARS-CoV-2 RNA load from subjects with available biospecimens. RESULTS We found excellent inter-rater agreement for RALE scores (intraclass correlation co-efficient=0.93). The required level of respiratory support at the time of baseline CXRs (supplemental oxygen or non-invasive ventilation [n=178]; invasive-mechanical ventilation [n=234], extracorporeal membrane oxygenation [n=13]) was significantly associated with RALE scores (median [interquartile range]: 20.0[14.1-26.7], 26.0[20.5-34.0] and 44.5[34.5-48.0], respectively, p<0.0001). Among invasively-ventilated patients, RALE scores were significantly associated with worse respiratory mechanics (plateau and driving pressure) and gas exchange metrics (PaO2/FiO2 and ventilatory ratio), as well as higher plasma levels of IL-6, sRAGE and TNFR1 levels (p<0.05). RALE scores were independently associated with 90-day survival in a multivariate Cox proportional hazards model (adjusted hazard ratio 1.04[1.02-1.07], p=0.002). We validated significant associations of RALE scores with baseline severity and mortality in an independent dataset of 415 COVID-19 inpatients. CONCLUSION Reproducible assessment of radiographic severity revealed significant associations with clinical and physiologic severity, host-response biomarkers and clinical outcome in COVID-19 pneumonia. Incorporation of radiographic severity assessments may provide prognostic and treatment allocation guidance in patients hospitalized with COVID-19.
Collapse
|
17
|
Gharaibeh M, Elheis M, Khasawneh R, Al-Omari M, Jibril M, Dilki K, El-Obeid E, Altalhi M, Abualigah L. Chest Radiograph Severity Scores, Comorbidity Prevalence, and Outcomes of Patients with Coronavirus Disease Treated at the King Abdullah University Hospital in Jordan: A Retrospective Study. Int J Gen Med 2022; 15:5103-5110. [PMID: 35620646 PMCID: PMC9128829 DOI: 10.2147/ijgm.s360851] [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: 02/02/2022] [Accepted: 05/12/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction Hospitalized patients with coronavirus disease (COVID-19) often undergo chest x-ray (CXR). Utilizing CXR findings could reduce the cost of COVID-19 treatment and the resultant pressure on the Jordanian healthcare system. Methods We evaluated the association between the CXR severity score, based on the Radiographic Assessment of Lung Edema (RALE) scoring system, and outcomes of patients with COVID-19. The main objective of this work is to assess the role of the RALE scoring system in predicting in-hospital mortality and clinical outcomes of patients with COVID-19. Adults with a positive severe acute respiratory syndrome COVID-19 two reverse-transcription polymerase chain reaction test results and a baseline CXR image, obtained in November 2020, were included. The RALE severity scores were calculated by expert radiologists and categorized as normal, mild, moderate, and severe. Chi-square tests and multivariable logistic regression were used to assess the association between the severity category and admission location and clinical characteristics. Results Based on the multivariable regression analysis, it has been found that male sex, hypertension, and the RALE severity score were significantly associated with in-hospital mortality. The baseline RALE severity score was associated with the need for critical care (P<0.001), in-hospital mortality (P<0.001), and the admission location (P=0.002). Discussion The utilization of RALE severity scores helps to predict clinical outcomes and promote prudent use of resources during the COVID-19 pandemic.
Collapse
Affiliation(s)
- Maha Gharaibeh
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Mwaffaq Elheis
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Ruba Khasawneh
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Mamoon Al-Omari
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Mohammad Jibril
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Khalid Dilki
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Eyhab El-Obeid
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Maryam Altalhi
- Department of Management Information System, College of Business Administration, Taif University, Taif, 21944, Saudi Arabia
| | - Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan
| |
Collapse
|
18
|
Karthik R, Menaka R, Hariharan M, Won D. CT-based severity assessment for COVID-19 using weakly supervised non-local CNN. Appl Soft Comput 2022; 121:108765. [PMID: 35370523 PMCID: PMC8962065 DOI: 10.1016/j.asoc.2022.108765] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/28/2022] [Accepted: 03/17/2022] [Indexed: 01/09/2023]
Abstract
Evaluating patient criticality is the foremost step in administering appropriate COVID-19 treatment protocols. Learning an Artificial Intelligence (AI) model from clinical data for automatic risk-stratification enables accelerated response to patients displaying critical indicators. Chest CT manifestations including ground-glass opacities and consolidations are a reliable indicator for prognostic studies and show variability with patient condition. To this end, we propose a novel attention framework to estimate COVID-19 severity as a regression score from a weakly annotated CT scan dataset. It takes a non-locality approach that correlates features across different parts and spatial scales of the 3D scan. An explicit guidance mechanism from limited infection labeling drives attention refinement and feature modulation. The resulting encoded representation is further enriched through cross-channel attention. The attention model also infuses global contextual awareness into the deep voxel features by querying the base CT scan to mine relevant features. Consequently, it learns to effectively localize its focus region and chisel out the infection precisely. Experimental validation on the MosMed dataset shows that the proposed architecture has significant potential in augmenting existing methods as it achieved a 0.84 R-squared score and 0.133 mean absolute difference.
Collapse
Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems & School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems & School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
| | - M Hariharan
- Cisco Systems India Pvt Ltd, Bangalore, India
| | - Daehan Won
- System Sciences and Industrial Engineering, Binghamton University, NY, USA
| |
Collapse
|
19
|
Microscopic Imaging and Labeling Dataset for the Detection of Pneumocystis jirovecii Using Methenamine Silver Staining Method. DATA 2022. [DOI: 10.3390/data7050056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Pneumocystis jirovecii pneumonia is one of the diseases that most affects immunocompromised patients today, and under certain circumstances, it can be fatal. On the other hand, more and more automatic tools based on artificial intelligence are required every day to help diagnose diseases and thus optimize the resources of the healthcare system. It is therefore important to develop techniques and mechanisms that enable early diagnosis. One of the most widely used techniques in diagnostic laboratories for the detection of its etiological agent, Pneumocystis jirovecii, is optical microscopy. Therefore, an image dataset of 29 different patients is presented in this work, which can be used to detect whether a patient is positive or negative for this fungi. These images were taken in at least four random positions on the specimen holder. The dataset consists of a total of 137 RGB images. Likewise, it contains realistic, annotated, and high-quality microscope images. In addition, we provide image segmentation and labeling that can also be used in numerous studies based on artificial intelligence implementation. The labeling was also validated by an expert, allowing it to be used as a reference in the training of automatic algorithms with supervised learning methods and thus to develop diagnostic assistance systems. Therefore, the dataset will open new opportunities for researchers working in image segmentation, detection, and classification problems related to Pneumocystis jirovecii pneumonia diagnosis.
Collapse
|
20
|
Valk CMA, Zimatore C, Mazzinari G, Pierrakos C, Sivakorn C, Dechsanga J, Grasso S, Beenen L, Bos LDJ, Paulus F, Schultz MJ, Pisani L. The Prognostic Capacity of the Radiographic Assessment for Lung Edema Score in Patients With COVID-19 Acute Respiratory Distress Syndrome-An International Multicenter Observational Study. Front Med (Lausanne) 2022; 8:772056. [PMID: 35071263 PMCID: PMC8766516 DOI: 10.3389/fmed.2021.772056] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/24/2021] [Indexed: 01/08/2023] Open
Abstract
Background: The radiographic assessment for lung edema (RALE) score has an association with mortality in patients with acute respiratory distress syndrome (ARDS). It is uncertain whether the RALE scores at the start of invasive ventilation or changes thereof in the next days have prognostic capacities in patients with COVID-19 ARDS. Aims and Objectives: To determine the prognostic capacity of the RALE score for mortality and duration of invasive ventilation in patients with COVID-19 ARDS. Methods: An international multicenter observational study included consecutive patients from 6 ICUs. Trained observers scored the first available chest X-ray (CXR) obtained within 48 h after the start of invasive ventilation (“baseline CXR”) and each CXRs thereafter up to day 14 (“follow-up CXR”). The primary endpoint was mortality at day 90. The secondary endpoint was the number of days free from the ventilator and alive at day 28 (VFD-28). Results: A total of 350 CXRs were scored in 139 patients with COVID-19 ARDS. The RALE score of the baseline CXR was high and was not different between survivors and non-survivors (33 [24–38] vs. 30 [25–38], P = 0.602). The RALE score of the baseline CXR had no association with mortality (hazard ratio [HR], 1.24 [95% CI 0.88–1.76]; P = 0.222; area under the receiver operating characteristic curve (AUROC) 0.50 [0.40–0.60]). A change in the RALE score over the first 14 days of invasive ventilation, however, had an independent association with mortality (HR, 1.03 [95% CI 1.01–1.05]; P < 0.001). When the event of death was considered, there was no significant association between the RALE score of the baseline CXR and the probability of being liberated from the ventilator (HR 1.02 [95% CI 0.99–1.04]; P = 0.08). Conclusion: In this cohort of patients with COVID-19 ARDS, with high RALE scores of the baseline CXR, the RALE score of the baseline CXR had no prognostic capacity, but an increase in the RALE score in the next days had an association with higher mortality.
Collapse
Affiliation(s)
- Christel M A Valk
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Amsterdam, Netherlands
| | - Claudio Zimatore
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Amsterdam, Netherlands.,Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy
| | - Guido Mazzinari
- Department of Anaesthesiology and Critical Care, Hospital Universitario y Politecnico la Fe, Valencia, Spain.,Perioperative Medicine Research Group, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
| | - Charalampos Pierrakos
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Amsterdam, Netherlands.,Department of Intensive Care, Centre Hospitalier Universitaire Brussels, Brussels, Belgium
| | - Chaisith Sivakorn
- Department of Clinical Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Jutamas Dechsanga
- Division of Pulmonary and Critical Care, Department of Medicine, Chonburi Hospital, Chonburi, Thailand
| | - Salvatore Grasso
- Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy
| | - Ludo Beenen
- Department of Radiology, Amsterdam UMC, Amsterdam, Netherlands
| | - Lieuwe D J Bos
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Amsterdam, Netherlands.,Department of Pulmonology, Amsterdam UMC, Amsterdam, Netherlands
| | - Frederique Paulus
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Amsterdam, Netherlands.,Center of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, Netherlands
| | - Marcus J Schultz
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Amsterdam, Netherlands.,Mahidol-Oxford Tropical Medicine Research Unit (MORU), Mahidol University, Bangkok, Thailand.,Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Luigi Pisani
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Amsterdam UMC, Amsterdam, Netherlands.,Mahidol-Oxford Tropical Medicine Research Unit (MORU), Mahidol University, Bangkok, Thailand.,Anaesthesia and Intensive Care Unit, Miulli Regional Hospital, Acquaviva delle Fonti, Italy
| |
Collapse
|
21
|
Pal A, Ali A, Young TR, Oostenbrink J, Prabhakar A, Prabhakar A, Deacon N, Arnold A, Eltayeb A, Yap C, Young DM, Tang A, Lakshmanan S, Lim YY, Pokarowski M, Kakodkar P. Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic. World J Radiol 2021; 13:258-282. [PMID: 34630913 PMCID: PMC8473437 DOI: 10.4329/wjr.v13.i9.258] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/28/2021] [Accepted: 08/04/2021] [Indexed: 02/06/2023] Open
Abstract
Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, over 103214008 cases have been reported, with more than 2231158 deaths as of January 31, 2021. Although the gold standard for diagnosis of this disease remains the reverse-transcription polymerase chain reaction of nasopharyngeal and oropharyngeal swabs, its false-negative rates have ignited the use of medical imaging as an important adjunct or alternative. Medical imaging assists in identifying the pathogenesis, the degree of pulmonary damage, and the characteristic features in each imaging modality. This literature review collates the characteristic radiographic findings of COVID-19 in various imaging modalities while keeping the preliminary focus on chest radiography, computed tomography (CT), and ultrasound scans. Given the higher sensitivity and greater proficiency in detecting characteristic findings during the early stages, CT scans are more reliable in diagnosis and serve as a practical method in following up the disease time course. As research rapidly expands, we have emphasized the CO-RADS classification system as a tool to aid in communicating the likelihood of COVID-19 suspicion among healthcare workers. Additionally, the utilization of other scoring systems such as MuLBSTA, Radiological Assessment of Lung Edema, and Brixia in this pandemic are reviewed as they integrate the radiographic findings into an objective scoring system to risk stratify the patients and predict the severity of disease. Furthermore, current progress in the utilization of artificial intelligence via radiomics is evaluated. Lastly, the lesson from the first wave and preparation for the second wave from the point of view of radiology are summarized.
Collapse
Affiliation(s)
- Aman Pal
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Abulhassan Ali
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Timothy R Young
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Juan Oostenbrink
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Akul Prabhakar
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Amogh Prabhakar
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Nina Deacon
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Amar Arnold
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Ahmed Eltayeb
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Charles Yap
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - David M Young
- Department of Computer Science, Yale University, New Haven, CO 06520, United States
| | - Alan Tang
- Department of Health Science, Duke University, Durham, NC 27708, United States
| | - Subramanian Lakshmanan
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Ying Yi Lim
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Martha Pokarowski
- The Hospital for Sick Kids, University of Toronto, Toronto M5S, Ontario, Canada
| | - Pramath Kakodkar
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| |
Collapse
|
22
|
Au-Yong I, Higashi Y, Giannotti E, Fogarty A, Morling JR, Grainge M, Race A, Juurlink I, Simmonds M, Briggs S, Cruikshank S, Hammond-Pears S, West J, Crooks CJ, Card T. Chest Radiograph Scoring Alone or Combined with Other Risk Scores for Predicting Outcomes in COVID-19. Radiology 2021; 302:460-469. [PMID: 34519573 PMCID: PMC8475750 DOI: 10.1148/radiol.2021210986] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background Radiographic severity may help predict patient deterioration and
outcomes from COVID-19 pneumonia. Purpose To assess the reliability and reproducibility of three chest radiograph
reporting systems (radiographic assessment of lung edema [RALE], Brixia,
and percentage opacification) in patients with proven SARS-CoV-2
infection and examine the ability of these scores to predict adverse
outcomes both alone and in conjunction with two clinical scoring
systems, National Early Warning Score 2 (NEWS2) and International Severe
Acute Respiratory and Emerging Infection Consortium: Coronavirus
Clinical Characterization Consortium (ISARIC-4C) mortality. Materials and Methods This retrospective cohort study used routinely collected clinical data
of patients with polymerase chain reaction–positive SARS-CoV-2
infection admitted to a single center from February 2020 through July
2020. Initial chest radiographs were scored for RALE, Brixia, and
percentage opacification by one of three radiologists. Intra- and
interreader agreement were assessed with intraclass correlation
coefficients. The rate of admission to the intensive care unit (ICU) or
death up to 60 days after scored chest radiograph was estimated. NEWS2
and ISARIC-4C mortality at hospital admission were calculated. Daily
risk for admission to ICU or death was modeled with Cox proportional
hazards models that incorporated the chest radiograph scores adjusted
for NEWS2 or ISARIC-4C mortality. Results Admission chest radiographs of 50 patients (mean age, 74 years ±
16 [standard deviation]; 28 men) were scored by all three radiologists,
with good interreader reliability for all scores, as follows: intraclass
correlation coefficients were 0.87 for RALE (95% CI: 0.80, 0.92), 0.86
for Brixia (95% CI: 0.76, 0.92), and 0.72 for percentage opacification
(95% CI: 0.48, 0.85). Of 751 patients with a chest radiograph, those
with greater than 75% opacification had a median time to ICU admission
or death of just 1–2 days. Among 628 patients for whom data were
available (median age, 76 years [interquartile range, 61–84
years]; 344 men), opacification of 51%–75% increased risk for ICU
admission or death by twofold (hazard ratio, 2.2; 95% CI: 1.6, 2.8), and
opacification greater than 75% increased ICU risk by fourfold (hazard
ratio, 4.0; 95% CI: 3.4, 4.7) compared with opacification of
0%–25%, when adjusted for NEWS2 score. Conclusion Brixia, radiographic assessment of lung edema, and percentage
opacification scores all reliably helped predict adverse outcomes in
SARS-CoV-2 infection. © RSNA, 2021 Online supplemental material is available for this
article. See also the editorial by Little in this issue.
Collapse
Affiliation(s)
- Iain Au-Yong
- Department of Radiology, Nottingham University Hospitals NHS Trust, NG7 2UH
| | - Yutaro Higashi
- Department of Radiology, Nottingham University Hospitals NHS Trust, NG7 2UH
| | | | - Andrew Fogarty
- Nottingham University Hospitals NHS Trust.,Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH
| | - Joanne R Morling
- Nottingham University Hospitals NHS Trust.,Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH
| | - Matthew Grainge
- Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB
| | | | | | | | | | | | | | - Joe West
- Nottingham University Hospitals NHS Trust.,Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH.,East Midlands Academic Health Science Network, University of Nottingham, Nottingham, NG7 2TU
| | - Colin J Crooks
- Nottingham University Hospitals NHS Trust.,Translational Medical Sciences, School of Medicine, University of Nottingham, NG7 2UH.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH
| | - Timothy Card
- Nottingham University Hospitals NHS Trust.,Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH
| |
Collapse
|
23
|
Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. J Am Med Inform Assoc 2021; 28:2050-2067. [PMID: 34151987 PMCID: PMC8344463 DOI: 10.1093/jamia/ocab098] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. MATERIALS AND METHODS We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. RESULTS In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. DISCUSSION Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. CONCLUSION There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.
Collapse
Affiliation(s)
- Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
| | - Yahan Zhang
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Tianchen Lyu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
| |
Collapse
|
24
|
Rodríguez-Rodríguez I, Rodríguez JV, Shirvanizadeh N, Ortiz A, Pardo-Quiles DJ. Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8578. [PMID: 34444327 PMCID: PMC8393243 DOI: 10.3390/ijerph18168578] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/02/2021] [Accepted: 08/11/2021] [Indexed: 01/01/2023]
Abstract
The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.
Collapse
Affiliation(s)
- Ignacio Rodríguez-Rodríguez
- Protein Structure and Bioinformatics Resech Group, Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden;
| | - José-Víctor Rodríguez
- Departamento de Tecnologías de la Información y las Comunicaciones, School of Telecommunications Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain;
| | - Niloofar Shirvanizadeh
- Protein Structure and Bioinformatics Resech Group, Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden;
| | - Andrés Ortiz
- Departamento de Ingeniería de Comunicaciones, School of Telecommunications Engineering, Universidad de Málaga, 29071 Málaga, Spain;
| | - Domingo-Javier Pardo-Quiles
- Departamento de Tecnologías de la Información y las Comunicaciones, School of Telecommunications Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain;
| |
Collapse
|
25
|
Arru C, Ebrahimian S, Falaschi Z, Hansen JV, Pasche A, Lyhne MD, Zimmermann M, Durlak F, Mitschke M, Carriero A, Nielsen-Kudsk JE, Kalra MK, Saba L. Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia. Clin Imaging 2021; 80:58-66. [PMID: 34246044 PMCID: PMC8247202 DOI: 10.1016/j.clinimag.2021.06.036] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 12/20/2022]
Abstract
Purpose Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods The multicenter, ethical committee-approved, retrospective study included non-contrast-enhanced chest CT of 221 SARS-CoV-2 positive patients from Italy (n = 196 patients; mean age 64 ± 16 years) and Denmark (n = 25; mean age 69 ± 13 years). A thoracic radiologist graded presence, type and extent of pulmonary opacities and severity of motion artifacts in each lung lobe on all chest CTs. Thin-section CT images were processed with CT Pneumonia Analysis Prototype (Siemens Healthineers) which yielded segmentation masks from a deep learning (DL) algorithm to derive features of lung abnormalities such as opacity scores, mean HU, as well as volume and percentage of all-attenuation and high-attenuation (opacities >−200 HU) opacities. Separately, whole lung radiomics were obtained for all CT exams. Analysis of variance and multiple logistic regression were performed for data analysis. Results Moderate to severe respiratory motion artifacts affected nearly one-quarter of chest CTs in patients. Subjective severity assessment, DL-based features and radiomics predicted patient outcome (AUC 0.76 vs AUC 0.88 vs AUC 0.83) and need for ICU admission (AUC 0.77 vs AUC 0.0.80 vs 0.82). Excluding chest CT with motion artifacts, the performance of DL-based and radiomics features improve for predicting ICU admission. Conclusion DL-based and radiomics features of pulmonary opacities from chest CT were superior to subjective assessment for differentiating patients with favorable and adverse outcomes.
Collapse
Affiliation(s)
- Chiara Arru
- Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Shadi Ebrahimian
- Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA.
| | | | - Jacob Valentin Hansen
- Department of Cardiology, Department of Clinical Medicine, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200 Aarhus N, Denmark.
| | | | - Mads Dam Lyhne
- Department of Cardiology, Department of Clinical Medicine, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200 Aarhus N, Denmark.
| | | | - Felix Durlak
- Siemens Healthcare GmbH, Diagnostic Imaging, Erlangen, Germany.
| | | | | | - Jens Erik Nielsen-Kudsk
- Department of Cardiology, Department of Clinical Medicine, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200 Aarhus N, Denmark
| | | | - Luca Saba
- Azienda Ospedaliera Universitaria di Cagliari, Cagliari, Italy
| |
Collapse
|
26
|
Zimatore C, Pisani L, Lippolis V, Warren MA, Calfee CS, Ware LB, Algera AG, Smit MR, Grasso S, Schultz MJ. Accuracy of the Radiographic Assessment of Lung Edema Score for the Diagnosis of ARDS. Front Physiol 2021; 12:672823. [PMID: 34122143 PMCID: PMC8188799 DOI: 10.3389/fphys.2021.672823] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/28/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Bilateral opacities on chest radiographs are part of the Berlin Definition for Acute Respiratory Distress Syndrome (ARDS) but have poor interobserver reliability. The “Radiographic Assessment of Lung Edema” (RALE) score was recently proposed for evaluation of the extent and density of alveolar opacities on chest radiographs of ARDS patients. The current study determined the accuracy of the RALE score for the diagnosis and the prognosis of ARDS. Methods:Post-hoc analysis of a cohort of invasively ventilated intensive care unit (ICU) patients expected to need invasive ventilation for >24 h. The Berlin Definition was used as the gold standard. The RALE score was calculated for the first available chest radiograph after start of ventilation in the ICU. The primary endpoint was the diagnostic accuracy for ARDS of the RALE score. Secondary endpoints included the prognostic value of the RALE score for ICU and hospital mortality, and the association with ARDS severity, and the PaO2/FiO2. Receiver operating characteristic (ROC) curves were constructed, and the optimal cutoff was used to determine sensitivity, specificity and the negative and positive predictive value of the RALE score for ARDS. Results: The study included 131 patients, of whom 30 had ARDS (11 mild, 15 moderate, and 4 severe ARDS). The first available chest radiograph was obtained median 0 [0 to 1] days after start of invasive ventilation in ICU. Compared to patients without ARDS, a higher RALE score was found in patients with ARDS (24 [interquartile range (IQR) 16–30] vs. 6 [IQR 3–11]; P < 0.001), with RALE scores of 20 [IQR 14–24], 26 [IQR 16–32], and 32 [IQR 19–36] for mild, moderate and severe ARDS, respectively, (P = 0.166). The area under the ROC for ARDS was excellent (0.91 [0.86–0.96]). The best cutoff for ARDS diagnosis was 10 with 100% sensitivity, 71% specificity, 51% positive predictive value and 100% negative predictive value. The RALE score was not associated with ICU or hospital mortality, and weakly correlated with the PaO2/FiO2. Conclusion: In this cohort of invasively ventilated ICU patients, the RALE score had excellent diagnostic accuracy for ARDS.
Collapse
Affiliation(s)
- Claudio Zimatore
- Department of Intensive Care, Academic Medical Center, Amsterdam, Netherlands.,Department of Emergency and Organ Transplantation, School of Medicine, University of Bari Aldo Moro, Bari, Italy
| | - Luigi Pisani
- Department of Intensive Care, Academic Medical Center, Amsterdam, Netherlands.,Mahidol-Oxford Tropical Medicine Research Unit (MORU), Mahidol University, Bangkok, Thailand.,Department of Anesthesia and Perioperative Medicine, Regional General Hospital F. Miulli, Acquaviva delle Fonti, Italy
| | | | - Melissa A Warren
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Carolyn S Calfee
- Department of Medicine and Department of Anesthesia, Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Lorraine B Ware
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, United States.,Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Anna Geke Algera
- Department of Intensive Care, Academic Medical Center, Amsterdam, Netherlands
| | - Marry R Smit
- Department of Intensive Care, Academic Medical Center, Amsterdam, Netherlands
| | - Salvatore Grasso
- Department of Emergency and Organ Transplantation, School of Medicine, University of Bari Aldo Moro, Bari, Italy
| | - Marcus J Schultz
- Department of Intensive Care, Academic Medical Center, Amsterdam, Netherlands.,Mahidol-Oxford Tropical Medicine Research Unit (MORU), Mahidol University, Bangkok, Thailand
| |
Collapse
|
27
|
AI applications in robotics, diagnostic image analysis and precision medicine: Current limitations, future trends, guidelines on CAD systems for medicine. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100596] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
|
28
|
Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1655] [Impact Index Per Article: 413.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
Collapse
Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| |
Collapse
|