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Ippolito D, Maino C, Gandola D, Franco PN, Miron R, Barbu V, Bologna M, Corso R, Breaban ME. Artificial Intelligence Applied to Chest X-ray: A Reliable Tool to Assess the Differential Diagnosis of Lung Pneumonia in the Emergency Department. Diseases 2023; 11:171. [PMID: 37987282 PMCID: PMC10660530 DOI: 10.3390/diseases11040171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Considering the large number of patients with pulmonary symptoms admitted to the emergency department daily, it is essential to diagnose them correctly. It is necessary to quickly solve the differential diagnosis between COVID-19 and typical bacterial pneumonia to address them with the best management possible. In this setting, an artificial intelligence (AI) system can help radiologists detect pneumonia more quickly. METHODS We aimed to test the diagnostic performance of an AI system in detecting COVID-19 pneumonia and typical bacterial pneumonia in patients who underwent a chest X-ray (CXR) and were admitted to the emergency department. The final dataset was composed of three sub-datasets: the first included all patients positive for COVID-19 pneumonia (n = 1140, namely "COVID-19+"), the second one included all patients with typical bacterial pneumonia (n = 500, "pneumonia+"), and the third one was composed of healthy subjects (n = 1000). Two radiologists were blinded to demographic, clinical, and laboratory data. The developed AI system was used to evaluate all CXRs randomly and was asked to classify them into three classes. Cohen's κ was used for interrater reliability analysis. The AI system's diagnostic accuracy was evaluated using a confusion matrix, and 95%CIs were reported as appropriate. RESULTS The interrater reliability analysis between the most experienced radiologist and the AI system reported an almost perfect agreement for COVID-19+ (κ = 0.822) and pneumonia+ (κ = 0.913). We found 96% sensitivity (95% CIs = 94.9-96.9) and 79.8% specificity (76.4-82.9) for the radiologist and 94.7% sensitivity (93.4-95.8) and 80.2% specificity (76.9-83.2) for the AI system in the detection of COVID-19+. Moreover, we found 97.9% sensitivity (98-99.3) and 88% specificity (83.5-91.7) for the radiologist and 97.5% sensitivity (96.5-98.3) and 83.9% specificity (79-87.9) for the AI system in the detection of pneumonia+ patients. Finally, the AI system reached an accuracy of 93.8%, with a misclassification rate of 6.2% and weighted-F1 of 93.8% in detecting COVID+, pneumonia+, and healthy subjects. CONCLUSIONS The AI system demonstrated excellent diagnostic performance in identifying COVID-19 and typical bacterial pneumonia in CXRs acquired in the emergency setting.
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Affiliation(s)
- Davide Ippolito
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
| | - Cesare Maino
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Davide Gandola
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Paolo Niccolò Franco
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Radu Miron
- Sentic Lab, Strada Elena Doamna 20, 700398 Iași, Romania; (R.M.); (V.B.)
| | - Vlad Barbu
- Sentic Lab, Strada Elena Doamna 20, 700398 Iași, Romania; (R.M.); (V.B.)
| | | | - Rocco Corso
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.); (P.N.F.); (R.C.)
| | - Mihaela Elena Breaban
- Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iasi, Strada General Henri Mathias Berthelot 16, 700483 Iași, Romania
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Candel FJ, Salavert M, Basaras M, Borges M, Cantón R, Cercenado E, Cilloniz C, Estella Á, García-Lechuz JM, Garnacho Montero J, Gordo F, Julián-Jiménez A, Martín-Sánchez FJ, Maseda E, Matesanz M, Menéndez R, Mirón-Rubio M, Ortiz de Lejarazu R, Polverino E, Retamar-Gentil P, Ruiz-Iturriaga LA, Sancho S, Serrano L. Ten Issues for Updating in Community-Acquired Pneumonia: An Expert Review. J Clin Med 2023; 12:6864. [PMID: 37959328 PMCID: PMC10649000 DOI: 10.3390/jcm12216864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Community-acquired pneumonia represents the third-highest cause of mortality in industrialized countries and the first due to infection. Although guidelines for the approach to this infection model are widely implemented in international health schemes, information continually emerges that generates controversy or requires updating its management. This paper reviews the most important issues in the approach to this process, such as an aetiologic update using new molecular platforms or imaging techniques, including the diagnostic stewardship in different clinical settings. It also reviews both the Intensive Care Unit admission criteria and those of clinical stability to discharge. An update in antibiotic, in oxygen, or steroidal therapy is presented. It also analyzes the management out-of-hospital in CAP requiring hospitalization, the main factors for readmission, and an approach to therapeutic failure or rescue. Finally, the main strategies for prevention and vaccination in both immunocompetent and immunocompromised hosts are reviewed.
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Affiliation(s)
- Francisco Javier Candel
- Clinical Microbiology & Infectious Diseases, Transplant Coordination, IdISSC & IML Health Research Institutes, Hospital Clínico Universitario San Carlos, 28040 Madrid, Spain
| | - Miguel Salavert
- Infectious Diseases Unit, La Fe (IIS) Health Research Institute, University Hospital La Fe, 46015 Valencia, Spain
| | - Miren Basaras
- Immunology, Microbiology and Parasitology Department, Faculty of Medicine and Nursing, University of País Vasco, 48940 Bizkaia, Spain;
| | - Marcio Borges
- Multidisciplinary Sepsis Unit, Intensive Medicine Department, University Hospital Son Llàtzer, 07198 Palma de Mallorca, Spain;
- Instituto de Investigación Sanitaria Islas Baleares (IDISBA), 07198 Mallorca, Spain
| | - Rafael Cantón
- Clinical Microbiology Service, University Hospital Ramón y Cajal, Institute Ramón y Cajal for Health Research (IRYCIS), 28034 Madrid, Spain;
- CIBER of Infectious Diseases (CIBERINFEC), National Institute of Health San Carlos III, 28034 Madrid, Spain;
| | - Emilia Cercenado
- Clinical Microbiology & Infectious Diseases Service, University Hospital Gregorio Marañón, 28009 Madrid, Spain;
| | - Catian Cilloniz
- IDIBAPS, CIBERES, 08007 Barcelona, Spain;
- Faculty of Health Sciences, Continental University, Huancayo 15304, Peru
| | - Ángel Estella
- Intensive Care Unit, INIBiCA, University Hospital of Jerez, Medicine Department, University of Cádiz, 11404 Jerez, Spain
| | | | - José Garnacho Montero
- Intensive Care Clinical Unit, Hospital Universitario Virgen Macarena, 41013 Sevilla, Spain;
| | - Federico Gordo
- Intensive Medicine Department, University Hospital of Henares, 28802 Madrid, Spain;
| | - Agustín Julián-Jiménez
- Emergency Department, University Hospital Toledo, University of Castilla La Mancha, 45007 Toledo, Spain;
| | | | - Emilio Maseda
- Anesthesiology Department, Hospital Quirón Salud Valle del Henares, 28850 Madrid, Spain;
| | - Mayra Matesanz
- Hospital at Home Unit, Clinic University Hospital San Carlos, 28040 Madrid, Spain;
| | - Rosario Menéndez
- Pneumology Service, La Fe (IIS) Health Research Institute, University Hospital La Fe, 46015 Valencia, Spain;
| | - Manuel Mirón-Rubio
- Hospital at Home Service, University of Torrejón, Torrejón de Ardoz, 28006 Madrid, Spain;
| | - Raúl Ortiz de Lejarazu
- National Influenza Center, Clinic University Hospital of Valladolid, University of Valladolid, 47003 Valladolid, Spain;
| | - Eva Polverino
- Pneumology Service, Hospital Vall d’Hebron, 08035 Barcelona, Spain;
- Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Barcelona Hospital Campus, 08035 Barcelona, Spain
- CIBER of Respiratory Diseases (CIBERES), Institute of Health San Carlos III, 28029 Madrid, Spain
| | - Pilar Retamar-Gentil
- CIBER of Infectious Diseases (CIBERINFEC), National Institute of Health San Carlos III, 28034 Madrid, Spain;
- Infectious Diseases & Microbiology Clinical Management Unit, University Hospital Virgen Macarena, IBIS, University of Seville, 41013 Sevilla, Spain
| | - Luis Alberto Ruiz-Iturriaga
- Pneumology Service, University Hospital Cruces, 48903 Barakaldo, Spain; (L.A.R.-I.); (L.S.)
- Faculty of Medicine and Nursing, University of País Vasco, 48940 Bizkaia, Spain
| | - Susana Sancho
- Intensive Medicine Department, University Hospital La Fe, 46015 Valencia, Spain;
| | - Leyre Serrano
- Pneumology Service, University Hospital Cruces, 48903 Barakaldo, Spain; (L.A.R.-I.); (L.S.)
- Faculty of Medicine and Nursing, University of País Vasco, 48940 Bizkaia, Spain
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Ibba S, Tancredi C, Fantesini A, Cellina M, Presta R, Montanari R, Papa S, Alì M. How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders. Eur J Radiol 2023; 165:110917. [PMID: 37327548 DOI: 10.1016/j.ejrad.2023.110917] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/16/2023] [Accepted: 05/31/2023] [Indexed: 06/18/2023]
Abstract
PURPOSE In this study we investigate how patients perceive the interaction between artificial intelligence (AI) and radiologists by designing a survey. METHOD We created a survey focused on the application of Artificial Intelligence in radiology which consisted of 20 questions distributed in three sections:Only completed questionnaires were considered for analysis. RESULTS 2119 subjects completed the survey. Among them, 1216 respondents were over 60 years old, showing interest in AI even though they were not digital natives. Although >45% of the respondents reported a high level of education, only 3% said they were AI experts. 87% of respondents favored using AI to support diagnosis but would like to be informed. Only 10% would consult another specialist if their doctor used AI support. Most respondents (76%) said they would not feel comfortable if the diagnosis was made by the AI alone, highlighting the importance of the physician's role in the emotional management of the patient. Finally, 36% of respondents were willing to discuss the topic further in a focus group. CONCLUSION Patients' perception of the use of AI in radiology was positive, although still strictly linked to the supervision of the radiologist. Respondents showed interest and willingness to learn more about AI in the medical field, confirming how patients' confidence in AI technology and its acceptance is central to its widespread use in clinical practice.
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Affiliation(s)
- Simona Ibba
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Chiara Tancredi
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Arianna Fantesini
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy.
| | - Roberta Presta
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Roberto Montanari
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Marco Alì
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy; Bracco Imaging S.p.A., Via Egidio Folli, 50, 20134 Milan, Italy.
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Liu Y, Xing W, Zhao M, Lin M. A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module. Neural Comput Appl 2023; 35:1-13. [PMID: 37362575 PMCID: PMC10147369 DOI: 10.1007/s00521-023-08604-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 04/11/2023] [Indexed: 06/28/2023]
Abstract
During the past three years, the coronavirus disease 2019 (COVID-19) has swept the world. The rapid and accurate recognition of covid-19 pneumonia are ,therefore, of great importance. To handle this problem, we propose a new pipeline of deep learning framework for diagnosing COVID-19 pneumonia via chest X-ray images from normal, COVID-19, and other pneumonia patients. In detail, the self-trained YOLO-v4 network was first used to locate and segment the thoracic region, and the output images were scaled to the same size. Subsequently, the pre-trained convolutional neural network was adopted to extract the features of X-ray images from 13 convolutional layers, which were fused with the original image to form a 14-dimensional image matrix. It was then put into three parallel pyramid multi-layer perceptron (MLP)-Mixer modules for comprehensive feature extraction through spatial fusion and channel fusion based on different scales so as to grasp more extensive feature correlation. Finally, by combining all image features from the 14-channel output, the classification task was achieved using two fully connected layers as well as Softmax classifier for classification. Extensive simulations based on a total of 4099 chest X-ray images were conducted to verify the effectiveness of the proposed method. Experimental results indicated that our proposed method can achieve the best performance in almost all cases, which is good for auxiliary diagnosis of COVID-19 and has great clinical application potential.
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Affiliation(s)
- Yiwen Liu
- College of Information Science and Technology, Donghua University, Shanghai, People’s Republic of China
| | - Wenyu Xing
- School of Information Science and Technology, Fudan University, Shanghai, People’s Republic of China
| | - Mingbo Zhao
- College of Information Science and Technology, Donghua University, Shanghai, People’s Republic of China
- Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong People’s Republic of China
| | - Mingquan Lin
- Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong People’s Republic of China
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Li D, Pehrson LM, Tøttrup L, Fraccaro M, Bonnevie R, Thrane J, Sørensen PJ, Rykkje A, Andersen TT, Steglich-Arnholm H, Stærk DMR, Borgwardt L, Hansen KL, Darkner S, Carlsen JF, Nielsen MB. Inter- and Intra-Observer Agreement When Using a Diagnostic Labeling Scheme for Annotating Findings on Chest X-rays-An Early Step in the Development of a Deep Learning-Based Decision Support System. Diagnostics (Basel) 2022; 12:diagnostics12123112. [PMID: 36553118 PMCID: PMC9776917 DOI: 10.3390/diagnostics12123112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/21/2022] [Accepted: 11/26/2022] [Indexed: 12/14/2022] Open
Abstract
Consistent annotation of data is a prerequisite for the successful training and testing of artificial intelligence-based decision support systems in radiology. This can be obtained by standardizing terminology when annotating diagnostic images. The purpose of this study was to evaluate the annotation consistency among radiologists when using a novel diagnostic labeling scheme for chest X-rays. Six radiologists with experience ranging from one to sixteen years, annotated a set of 100 fully anonymized chest X-rays. The blinded radiologists annotated on two separate occasions. Statistical analyses were done using Randolph's kappa and PABAK, and the proportions of specific agreements were calculated. Fair-to-excellent agreement was found for all labels among the annotators (Randolph's Kappa, 0.40-0.99). The PABAK ranged from 0.12 to 1 for the two-reader inter-rater agreement and 0.26 to 1 for the intra-rater agreement. Descriptive and broad labels achieved the highest proportion of positive agreement in both the inter- and intra-reader analyses. Annotating findings with specific, interpretive labels were found to be difficult for less experienced radiologists. Annotating images with descriptive labels may increase agreement between radiologists with different experience levels compared to annotation with interpretive labels.
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Affiliation(s)
- Dana Li
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
- Correspondence:
| | - Lea Marie Pehrson
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark
| | | | | | | | | | - Peter Jagd Sørensen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Alexander Rykkje
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Tobias Thostrup Andersen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Henrik Steglich-Arnholm
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Dorte Marianne Rohde Stærk
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Lotte Borgwardt
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Kristoffer Lindskov Hansen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Sune Darkner
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Jonathan Frederik Carlsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Michael Bachmann Nielsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
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Qu Y, Meng Y, Fan H, Xu RX. Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia. Infrared Phys Technol 2022; 123:104201. [PMID: 35599723 PMCID: PMC9106596 DOI: 10.1016/j.infrared.2022.104201] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 05/01/2022] [Accepted: 05/01/2022] [Indexed: 06/15/2023]
Abstract
Rapid screening and early treatment of lung infection are essential for effective control of many epidemics such as Coronavirus Disease 2019 (COVID-19). Recent studies have demonstrated the potential correlation between lung infection and the change of back skin temperature distribution. Based on these findings, we propose to use low-cost, portable and rapid thermal imaging in combination with image-processing algorithms and machine learning analysis for non-invasive and safe detection of pneumonia. The proposed method was tested in 69 subjects (30 normal adults, 11 cases of fever without pneumonia, 19 cases of general pneumonia and 9 cases of COVID-19) where both RGB and thermal images were acquired from the back of each subject. The acquired images were processed automatically in order to extract multiple location and shape features that distinguish normal subjects from pneumonia patients at a high accuracy of 93 % . Furthermore, daily assessment of two pneumonia patients by the proposed method accurately predicted the clinical outcomes, coincident with those of laboratory tests. Our pilot study demonstrated the technical feasibility of portable and intelligent thermal imaging for screening and therapeutic assessment of pneumonia. The method can be potentially implemented in under-resourced regions for more effective control of respiratory epidemics.
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Affiliation(s)
- Yingjie Qu
- Department of Intelligence Science and Technology, Anhui Polytechnic University, Wuhu, Anhui 241000, China
| | - Yuquan Meng
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Hua Fan
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Ronald X Xu
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangshu 215009, China
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AlNuaimi D, AlKetbi R. The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic. BJR Open 2022; 4:20210075. [PMID: 36105414 PMCID: PMC9459850 DOI: 10.1259/bjro.20210075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 04/12/2022] [Accepted: 05/09/2022] [Indexed: 11/05/2022] Open
Abstract
Artificial intelligence (AI) plays a crucial role in the future development of all healthcare sectors ranging from clinical assistance of physicians by providing accurate diagnosis, prognosis and treatment to the development of vaccinations and aiding in the combat against the Covid-19 global pandemic. AI has an important role in diagnostic radiology where the algorithms can be trained by large datasets to accurately provide a timely diagnosis of the radiological images given. This has led to the development of several AI algorithms that can be used in regions of scarcity of radiologists during the current pandemic by simply denoting the presence or absence of Covid-19 pneumonia in PCR positive patients on plain chest radiographs as well as in helping to levitate the over-burdened radiology departments by accelerating the time for report delivery. Plain chest radiography is the most common radiological study in the emergency department setting and is readily available, fast and a cheap method that can be used in triaging patients as well as being portable in the medical wards and can be used as the initial radiological examination in Covid-19 positive patients to detect pneumonic changes. Numerous studies have been done comparing several AI algorithms to that of experienced thoracic radiologists in plain chest radiograph reports measuring accuracy of each in Covid-19 patients. The majority of studies have reported performance equal or higher to that of the well-experienced thoracic radiologist in predicting the presence or absence of Covid-19 pneumonic changes in the provided chest radiographs.
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Affiliation(s)
- Dana AlNuaimi
- Westford University-UCAM, Sharjah, United Arab Emirates
| | - Reem AlKetbi
- Dubai Health Authority, Dubai, United Arab Emirates
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Liu Q, Ding H. Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence. Front Neurorobot 2022; 16:820028. [PMID: 35645761 PMCID: PMC9131050 DOI: 10.3389/fnbot.2022.820028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 02/21/2022] [Indexed: 11/16/2022] Open
Abstract
The present work aims to accelerate sports development in China and promote technological innovation in the artificial intelligence (AI) field. After analyzing the application and development of AI, it is introduced into sports and applied to table tennis competitions and training. The principle of the trajectory prediction of the table tennis ball (TTB) based on AI is briefly introduced. It is found that the difficulty of predicting TTB trajectories lies in rotation measurement. Accordingly, the rotation and trajectory of TTB are predicted using some AI algorithms. Specifically, a TTB detection algorithm is designed based on the Feature Fusion Network (FFN). For feature exaction, the cross-layer connection network is used to strengthen the learning ability of convolutional neural networks (CNNs) and streamline network parameters to improve the network detection response. The experimental results demonstrate that the trained CNN can reach a detection accuracy of over 98%, with a detection response within 5.3 ms, meeting the requirements of the robot vision system of the table tennis robot. By comparison, the traditional Color Segmentation Algorithm has advantages in detection response, with unsatisfactory detection accuracy, especially against TTB's color changes. Thus, the algorithm reported here can immediately hit the ball with high accuracy. The research content provides a reference for applying AI to TTB trajectory and rotation prediction and has significant value in popularizing table tennis.
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Affiliation(s)
| | - Hairong Ding
- Shanghai Polytechnic University, Shanghai, China
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9
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Yu AC, Mohajer B, Eng J. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. Radiol Artif Intell 2022; 4:e210064. [PMID: 35652114 DOI: 10.1148/ryai.210064] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/09/2022] [Accepted: 04/12/2022] [Indexed: 01/17/2023]
Abstract
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis. Materials and Methods In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics. Results Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance. Conclusion Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.Keywords: Meta-Analysis, Computer Applications-Detection/Diagnosis, Neural Networks, Computer Applications-General (Informatics), Epidemiology, Technology Assessment, Diagnosis, Informatics Supplemental material is available for this article. © RSNA, 2022.
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Affiliation(s)
- Alice C Yu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - Bahram Mohajer
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - John Eng
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
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Krishnamurthy S, Srinivasan K, Qaisar SM, Vincent PMDR, Chang CY. Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis. Comput Math Methods Med 2021; 2021:8036304. [PMID: 34552660 PMCID: PMC8452401 DOI: 10.1155/2021/8036304] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 08/30/2021] [Indexed: 12/11/2022]
Abstract
Pneumonitis is an infectious disease that causes the inflammation of the air sac. It can be life-threatening to the very young and elderly. Detection of pneumonitis from X-ray images is a significant challenge. Early detection and assistance with diagnosis can be crucial. Recent developments in the field of deep learning have significantly improved their performance in medical image analysis. The superior predictive performance of the deep learning methods makes them ideal for pneumonitis classification from chest X-ray images. However, training deep learning models can be cumbersome and resource-intensive. Reusing knowledge representations of public models trained on large-scale datasets through transfer learning can help alleviate these challenges. In this paper, we compare various image classification models based on transfer learning with well-known deep learning architectures. The Kaggle chest X-ray dataset was used to evaluate and compare our models. We apply basic data augmentation and fine-tune our feed-forward classification head on the models pretrained on the ImageNet dataset. We observed that the DenseNet201 model outperforms other models with an AUROC score of 0.966 and a recall score of 0.99. We also visualize the class activation maps from the DenseNet201 model to interpret the patterns recognized by the model for prediction.
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Affiliation(s)
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, India
| | - Saeed Mian Qaisar
- Electrical and Computer Engineering Department, Effat University, Jeddah, Saudi Arabia
| | - P. M. Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
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