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Anjuna R, Paulius S, Manuel GG, Audra B, Jurate N, Monika R. Diagnostic value of cardiothoracic ratio in patients with non-ischaemic cardiomyopathy: comparison to cardiovascular magnetic resonance imaging. Curr Probl Diagn Radiol 2024; 53:353-358. [PMID: 38281842 DOI: 10.1067/j.cpradiol.2024.01.011] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024]
Abstract
PURPOSE To determine the reliability of the cardiothoracic ratio (CTR) as a simple method to assess the cardiac size and function in patients with non-ischemic cardiomyopathy (NICM). METHODS In a sample of 91 patients (66 patients with diagnosed non-ischemic cardiomyopathy and 25 controls) we calculated the CTR on a posteroanterior chest radiograph and ventricular and atrial size based on accepted cardiovascular magnetic resonance (CMR) imaging values. Left and right ventricular ejection fraction was also calculated. The CTR and cardiac chamber size were compared between patients with NICM and healthy individuals. The distinction between normal and increased cardiac chamber size was made using published normal CMR reference values stratified by age and gender. RESULTS CTR values were higher in the NICM group (50.7±5.5 % Vs. 45.3±4.7 %, p<0.001). Likewise, LVEDVi, LV indexed mass, LA indexed volume, LA indexed area, and RA indexed area were higher, and LVEF and RVEF were lower in patients with non-ischemic cardiomyopathy (p < 0.05). In patients with non-ischemic cardiomyopathy, the greatest correlation between CTR and CMR values was with LVEDVi (ρ=0.4, p < 0.001), LA indexed volume (ρ=0.5, p < 0.001), LA indexed area (ρ=0.5, p < 0.001) and RA indexed area (ρ=0.4, p < 0.001). However, the correlation strength was only moderate. CONCLUSION Despite patients with NICM had higher CTR values than the control group, a substantial proportion of these patients showed normal CTRs (<50 %). This fact limits the usefulness of CTR to reliably predict NICM. Correlation between CTR and heart chamber dilation on CMR was only weak to moderate.
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Affiliation(s)
- Reghunath Anjuna
- Department of Radiology, Liverpool Heart and Chest Hospital, Liverpool, Thomas Drive L14 3 PE, United Kingdom
| | - Simkus Paulius
- Department of Radiology, Liverpool Heart and Chest Hospital, Liverpool, Thomas Drive L14 3 PE, United Kingdom; Department of Radiology, Lithuanian Health Sciences University Hospital Kaunas Clinics, Eiveniu 2, Kaunas 50161, Lithuania
| | - Gutierrez Gimeno Manuel
- Department of Radiology, Liverpool Heart and Chest Hospital, Liverpool, Thomas Drive L14 3 PE, United Kingdom
| | - Banisauskaite Audra
- Department of Radiology, Liverpool Heart and Chest Hospital, Liverpool, Thomas Drive L14 3 PE, United Kingdom; Department of Radiology, Lithuanian Health Sciences University Hospital Kaunas Clinics, Eiveniu 2, Kaunas 50161, Lithuania
| | - Noreikaite Jurate
- Department of Radiology, Liverpool Heart and Chest Hospital, Liverpool, Thomas Drive L14 3 PE, United Kingdom
| | - Radike Monika
- Department of Radiology, Liverpool Heart and Chest Hospital, Liverpool, Thomas Drive L14 3 PE, United Kingdom.
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J-P NA, SUSANTO AD, SAMOEDRO E, MANSYUR M, TUNGSAGUNWATTANA S, LERTROJANAPUNYA S, SUBHANNACHART P, SIRIRUTTANAPRUK S, DUMAVIBHAT N, ALGRANTI E, PARKER JE, HERING KG, KANAYAMA H, TAMURA T, KUSAKA Y, SUGANUMA N. Asian Intensive Reader of Pneumoconiosis program: examination for certification during 2008-2020. Ind Health 2024; 62:143-152. [PMID: 37407488 PMCID: PMC10995673 DOI: 10.2486/indhealth.2023-0010] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 06/27/2023] [Indexed: 07/07/2023]
Abstract
This study examined physicians' participation and performance in the examinations administered by the Asian Intensive Reader of Pneumoconiosis (AIR Pneumo) program from 2008 to 2020 and compared radiograph readings of physicians who passed with those who failed the examinations. Demography of the participants, participation trends, pass/fail rates, and proficiency scores were summarized; differences in reading the radiographs for pneumoconiosis of physicians who passed the examinations and those who failed were evaluated. By December 2020, 555 physicians from 20 countries had taken certification examinations; the number of participants increased in recent years. Reported background specialty training and work experience varied widely. Passing rate and mean proficiency score for participants who passed were 83.4% and 77.6 ± 9.4 in certification, and 76.8% and 88.1 ± 4.5 in recertification examinations. Compared with physicians who passed the examinations, physicians who failed tended to classify test radiographs as positive for pneumoconiosis and read a higher profusion; they likely missed large opacities and pleural plaques and had a lower accuracy in recognizing the shape of small opacities. Findings suggest that physicians who failed the examination tend to over-diagnose radiographs as positive for pneumoconiosis with higher profusion and have difficulty in correctly identifying small opacity shape.
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Affiliation(s)
- Naw Awn J-P
- Department of Environmental Medicine, Kochi Medical School,
Kochi University, Japan
| | - Agus Dwi SUSANTO
- Department of Pulmonology and Respiratory Medicine, Faculty
of Medicine, Universitas Indonesia, Persahabatan Hospital, Indonesia
| | - Erlang SAMOEDRO
- Department of Pulmonology and Respiratory Medicine, Faculty
of Medicine, Universitas Indonesia, Persahabatan Hospital, Indonesia
| | - Muchtaruddin MANSYUR
- Department of Community Medicine, Faculty of Medicine,
Universitas Indonesia & Southeast Asian Ministers of Education Regional Centre for
Food and Nutrition (SEAMEO RECFON), Indonesia
| | - Sutarat TUNGSAGUNWATTANA
- Department of Radiology, Central Chest Institute of Thailand,
Department of Medical Services, Ministry of Public Health, Thailand
| | - Saijai LERTROJANAPUNYA
- Department of Radiology, Central Chest Institute of Thailand,
Department of Medical Services, Ministry of Public Health, Thailand
| | - Ponglada SUBHANNACHART
- Department of Radiology, Central Chest Institute of Thailand,
Department of Medical Services, Ministry of Public Health, Thailand
| | | | - Narongpon DUMAVIBHAT
- Department of Preventive and Social Medicine, Faculty of
Medicine Siriraj Hospital, Mahidol University, Thailand
| | | | - John E. PARKER
- Pulmonary and Critical Care Medicine, Robert C. Byrd Health
Sciences Center, School of Medicine, West Virginia University, USA
| | - Kurt G. HERING
- Klinikum-Westfalen−Miner’s Hospital
(Knappschaftskrankenhaus), Germany
| | - Hitomi KANAYAMA
- Division of Environmental Health, Department of
International Social and Health Sciences, Faculty of Medical Sciences, University of
Fukui, Japan
| | - Taro TAMURA
- Department of Environmental Medicine and Public Health,
Faculty of Medicine, Shimane University, Japan
| | - Yukinori KUSAKA
- Health Care Center, Shimane Prefectural Federation of
Agricultural Cooperatives for Health and Welfare (JA Shimane Koseiren), Japan
| | - Narufumi SUGANUMA
- Department of Environmental Medicine, Kochi Medical School,
Kochi University, Japan
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3
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Ferrando Blanco D, Persiva Morenza Ó, Cabanzo Campos LB, Sánchez Martínez AL, Varona Porres D, Del Carpio Bellido Vargas LA, Andreu Soriano J. Utility of artificial intelligence for detection of pneumothorax on chest radiopgraphs done after transthoracic percutaneous transthoracic biopsy guided by computed tomography. Radiologia (Engl Ed) 2024; 66 Suppl 1:S40-S46. [PMID: 38642960 DOI: 10.1016/j.rxeng.2023.07.006] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/27/2023] [Indexed: 04/22/2024]
Abstract
OBJETIVE To assess the ability of an artificial intelligence software to detect pneumothorax in chest radiographs done after percutaneous transthoracic biopsy. MATERIAL AND METHODS We included retrospectively in our study adult patients who underwent CT-guided percutaneous transthoracic biopsies from lung, pleural or mediastinal lesions from June 2019 to June 2020, and who had a follow-up chest radiograph after the procedure. These chest radiographs were read to search the presence of pneumothorax independently by an expert thoracic radiologist and a radiodiagnosis resident, whose unified lecture was defined as the gold standard, and the result of each radiograph after interpretation by the artificial intelligence software was documented for posterior comparison with the gold standard. RESULTS A total of 284 chest radiographs were included in the study and the incidence of pneumothorax was 14.4%. There were no discrepancies between the two readers' interpretation of any of the postbiopsy chest radiographs. The artificial intelligence software was able to detect 41/41 of the present pneumothorax, implying a sensitivity of 100% and a negative predictive value of 100%, with a specificity of 79.4% and a positive predictive value of 45%. The accuracy was 82.4%, indicating that there is a high probability that an individual will be adequately classified by the software. It has also been documented that the presence of Port-a-cath is the cause of 8 of the 50 of false positives by the software. CONCLUSIONS The software has detected 100% of cases of pneumothorax in the postbiopsy chest radiographs. A potential use of this software could be as a prioritisation tool, allowing radiologists not to read immediately (or even not to read) chest radiographs classified as non-pathological by the software, with the confidence that there are no pathological cases.
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Affiliation(s)
- D Ferrando Blanco
- Servicio de Radiología, Hospital Universitari Vall d'Hebrón, Barcelona, Spain.
| | - Ó Persiva Morenza
- Servicio de Radiología, Hospital Universitari Vall d'Hebrón, Barcelona, Spain
| | - L B Cabanzo Campos
- Servicio de Radiología, Hospital Universitari Vall d'Hebrón, Barcelona, Spain
| | | | - D Varona Porres
- Servicio de Radiología, Hospital Universitari Vall d'Hebrón, Barcelona, Spain
| | | | - J Andreu Soriano
- Servicio de Radiología, Hospital Universitari Vall d'Hebrón, Barcelona, Spain
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Wang CH, Chang W, Lee MR, Tay J, Wu CY, Wu MC, Roth HR, Yang D, Zhao C, Wang W, Huang CH. Deep Learning-based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study. J Imaging Inform Med 2024; 37:589-600. [PMID: 38343228 PMCID: PMC11031502 DOI: 10.1007/s10278-023-00952-4] [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] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 04/20/2024]
Abstract
Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning-based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854-0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912-0.965, p-value < 0.001) compared with anterior-posterior (AUC 0.782, 95% CI 0.644-0.897) or portable anterior-posterior (AUC 0.869, 95% CI 0.814-0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823-0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765-0.904) and Shenzhen (AUC 0.806, 95% CI 0.771-0.839). A deep learning-based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.
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Affiliation(s)
- Chih-Hung Wang
- Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan
| | - Weishan Chang
- Department of Mathematics, National Taiwan University, Taipei, Taiwan
| | - Meng-Rui Lee
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Joyce Tay
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan
| | - Cheng-Yi Wu
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan
| | - Meng-Che Wu
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan
| | | | - Dong Yang
- NVIDIA Corporation, Bethesda, MD, USA
| | - Can Zhao
- NVIDIA Corporation, Bethesda, MD, USA
| | - Weichung Wang
- Institute of Applied Mathematical Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 106, Taiwan.
| | - Chien-Hua Huang
- Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan.
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5
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Brooks D, Wright SE, Beattie A, McAllister N, Anderson NH, Roy AI, Gonsalves P, Yates B, Graziadio S, Mackie A, Davidson J, Gopal SV, Whittle R, Zahed A, Barton L, Elameer M, Tuckett J, Holmes R, Sutcliffe A, Santamaria N, de Lalouviere LLH, Gupta S, Subramaniam J, Pearson JA, Brandwood M, Burnham R, Rostron AJ, Simpson AJ. Assessment of the comparative agreement between chest radiographs and CT scans in intensive care units. J Crit Care 2024; 82:154760. [PMID: 38492522 DOI: 10.1016/j.jcrc.2024.154760] [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: 12/24/2023] [Revised: 02/13/2024] [Accepted: 02/23/2024] [Indexed: 03/18/2024]
Abstract
PURPOSE Chest radiographs in critically ill patients can be difficult to interpret due to technical and clinical factors. We sought to determine the agreement of chest radiographs and CT scans, and the inter-observer variation of chest radiograph interpretation, in intensive care units (ICUs). METHODS Chest radiographs and corresponding thoracic computerised tomography (CT) scans (as reference standard) were collected from 45 ICU patients. All radiographs were analysed by 20 doctors (radiology consultants, radiology trainees, ICU consultants, ICU trainees) from 4 different centres, blinded to CT results. Specificity/sensitivity were determined for pleural effusion, lobar collapse and consolidation/atelectasis. Separately, Fleiss' kappa for multiple raters was used to determine inter-observer variation for chest radiographs. RESULTS The median sensitivity and specificity of chest radiographs for detecting abnormalities seen on CTs scans were 43.2% and 85.9% respectively. Diagnostic sensitivity for pleural effusion was significantly higher among radiology consultants but no specialty/experience distinctions were observed for specificity. Median inter-observer kappa coefficient among assessors was 0.295 ("fair"). CONCLUSIONS Chest radiographs commonly miss important radiological features in critically ill patients. Inter-observer agreement in chest radiograph interpretation is only "fair". Consultant radiologists are least likely to miss thoracic radiological abnormalities. The consequences of misdiagnosis by chest radiographs remain to be determined.
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Affiliation(s)
- Daniel Brooks
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne NE2 4HH, UK; Emergency Department, John Hunter Hospital, New Lambton Heights, NSW 2305, Australia
| | - Stephen E Wright
- Intensive Care Unit, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, High Heaton, Newcastle Upon Tyne NE7 7DN, UK
| | - Anna Beattie
- Department of Radiology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
| | - Nadia McAllister
- Intensive Care Unit, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, High Heaton, Newcastle Upon Tyne NE7 7DN, UK
| | - Niall H Anderson
- Usher Institute, University of Edinburgh, Old Medial School, Teviot Place, Edinburgh EH8 9AG, UK
| | - Alistair I Roy
- Integrated Critical Care Unit, Sunderland Royal Hospital, Kayll Road, Sunderland SR4 7TP, UK
| | - Philip Gonsalves
- Department of Radiology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
| | - Bryan Yates
- Critical Care Unit, Northumbria Specialist Emergency Care Hospital, Northumbria Way, Cramlington NE23 6NZ, UK
| | - Sara Graziadio
- NIHR Newcastle In Vitro Diagnostics Co-operative, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; York Health Economics Consortium, University of York, York YO10 5NQ, UK
| | - Alasdair Mackie
- Department of Radiology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
| | - John Davidson
- Intensive Care Unit, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, High Heaton, Newcastle Upon Tyne NE7 7DN, UK
| | - Sandeep Vijaya Gopal
- Department of Radiology, Sunderland Royal Hospital, Kayll Road, Sunderland SR4 7TP, UK
| | - Robert Whittle
- Critical Care Unit, Northumbria Specialist Emergency Care Hospital, Northumbria Way, Cramlington NE23 6NZ, UK
| | - Asef Zahed
- Department of Radiology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
| | - Lorna Barton
- Critical Care Unit, Northumbria Specialist Emergency Care Hospital, Northumbria Way, Cramlington NE23 6NZ, UK
| | - Mathew Elameer
- Department of Radiology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
| | - John Tuckett
- Department of Radiology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
| | - Rob Holmes
- Department of Radiology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
| | - Alexandra Sutcliffe
- Intensive Care Unit, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, High Heaton, Newcastle Upon Tyne NE7 7DN, UK
| | - Nuria Santamaria
- Department of Radiology, Sunderland Royal Hospital, Kayll Road, Sunderland SR4 7TP, UK; Department of Radiology, Clatterbridge Cancer Centre, l, Liverpool L7 8YA, UK
| | - Luke la Hausse de Lalouviere
- Intensive Care Unit, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, High Heaton, Newcastle Upon Tyne NE7 7DN, UK
| | - Sanjay Gupta
- Department of Radiology, Northumbria Specialist Emergency Care Hospital, Northumbria Way, Cramlington NE23 6NZ, UK
| | - Jeevan Subramaniam
- Critical Care Unit, Northumbria Specialist Emergency Care Hospital, Northumbria Way, Cramlington NE23 6NZ, UK
| | - Janaki A Pearson
- Integrated Critical Care Unit, Sunderland Royal Hospital, Kayll Road, Sunderland SR4 7TP, UK; Intensive Care Unit, James Cook University Hospital, Middlesbrough TS4 3BW, UK
| | - Matthew Brandwood
- Integrated Critical Care Unit, Sunderland Royal Hospital, Kayll Road, Sunderland SR4 7TP, UK
| | - Richard Burnham
- Critical Care Unit, Northumbria Specialist Emergency Care Hospital, Northumbria Way, Cramlington NE23 6NZ, UK
| | - Anthony J Rostron
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne NE2 4HH, UK; Integrated Critical Care Unit, Sunderland Royal Hospital, Kayll Road, Sunderland SR4 7TP, UK
| | - A John Simpson
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne NE2 4HH, UK; NIHR Newcastle In Vitro Diagnostics Co-operative, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Respiratory Medicine, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK.
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Matsunaga T, Kono A, Matsuo H, Kitagawa K, Nishio M, Hashimura H, Izawa Y, Toba T, Ishikawa K, Katsuki A, Ohmura K, Murakami T. Development of Pericardial Fat Count Images Using a Combination of Three Different Deep-Learning Models: Image Translation Model From Chest Radiograph Image to Projection Image of Three-Dimensional Computed Tomography. Acad Radiol 2024; 31:822-829. [PMID: 37914626 DOI: 10.1016/j.acra.2023.09.014] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 11/03/2023]
Abstract
RATIONALE AND OBJECTIVES Pericardial fat (PF)-the thoracic visceral fat surrounding the heart-promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. To evaluate PF, we generated pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model. MATERIALS AND METHODS We reviewed data of 269 consecutive patients who underwent coronary computed tomography (CT). We excluded patients with metal implants, pleural effusion, history of thoracic surgery, or malignancy. Thus, the data of 191 patients were used. We generated PFCIs from the projection of three-dimensional CT images, wherein fat accumulation was represented by a high pixel value. Three different deep-learning models, including CycleGAN were combined in the proposed method to generate PFCIs from CXRs. A single CycleGAN-based model was used to generate PFCIs from CXRs for comparison with the proposed method. To evaluate the image quality of the generated PFCIs, structural similarity index measure (SSIM), mean squared error (MSE), and mean absolute error (MAE) of (i) the PFCI generated using the proposed method and (ii) the PFCI generated using the single model were compared. RESULTS The mean SSIM, MSE, and MAE were 8.56 × 10-1, 1.28 × 10-2, and 3.57 × 10-2, respectively, for the proposed model, and 7.62 × 10-1, 1.98 × 10-2, and 5.04 × 10-2, respectively, for the single CycleGAN-based model. CONCLUSION PFCIs generated from CXRs with the proposed model showed better performance than those generated with the single model. The evaluation of PF without CT may be possible using the proposed method.
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Affiliation(s)
- Takaaki Matsunaga
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
| | - Atsushi Kono
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
| | - Hidetoshi Matsuo
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
| | - Kaoru Kitagawa
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Japan (K.K., K.I.)
| | - Mizuho Nishio
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.).
| | - Hiromi Hashimura
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
| | - Yu Izawa
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan (Y.I., T.T.)
| | - Takayoshi Toba
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan (Y.I., T.T.)
| | - Kazuki Ishikawa
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Japan (K.K., K.I.)
| | | | | | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (T.M., A.K., H.M., H.H., T.M.)
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Takamatsu A, Ueno M, Yoshida K, Kobayashi T, Kobayashi S, Gabata T. Performance of artificial intelligence-based software for the automatic detection of lung lesions on chest radiographs of patients with suspected lung cancer. Jpn J Radiol 2024; 42:291-299. [PMID: 38032419 PMCID: PMC10899395 DOI: 10.1007/s11604-023-01503-1] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023]
Abstract
PURPOSE This study aimed to evaluate the performance of the commercially available artificial intelligence-based software CXR-AID for the automatic detection of pulmonary nodules on the chest radiographs of patients suspected of having lung cancer. MATERIALS AND METHODS This retrospective study included 399 patients with clinically suspected lung cancer who underwent CT and chest radiography within 1 month between June 2020 and May 2022. The candidate areas on chest radiographs identified by CXR-AID were categorized into target (properly detected areas) and non-target (improperly detected areas) areas. The non-target areas were further divided into non-target normal areas (false positives for normal structures) and non-target abnormal areas. The visibility score, characteristics and location of the nodules, presence of overlapping structures, and background lung score and presence of pulmonary disease were manually evaluated and compared between the nodules detected or undetected by CXR-AID. The probability indices calculated by CXR-AID were compared between the target and non-target areas. RESULTS Among the 450 nodules detected in 399 patients, 331 nodules detected in 313 patients were visible on chest radiographs during manual evaluation. CXR-AID detected 264 of these 331 nodules with a sensitivity of 0.80. The detection sensitivity increased significantly with the visibility score. No significant correlation was observed between the background lung score and sensitivity. The non-target area per image was 0.85, and the probability index of the non-target area was lower than that of the target area. The non-target normal area per image was 0.24. Larger and more solid nodules exhibited higher sensitivities, while nodules with overlapping structures demonstrated lower detection sensitivities. CONCLUSION The nodule detection sensitivity of CXR-AID on chest radiographs was 0.80, and the non-target and non-target normal areas per image were 0.85 and 0.24, respectively. Larger, solid nodules without overlapping structures were detected more readily by CXR-AID.
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Affiliation(s)
- Atsushi Takamatsu
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takaramachi, Kanazawa, Ishikawa, 920-8641, Japan
| | - Midori Ueno
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takaramachi, Kanazawa, Ishikawa, 920-8641, Japan
| | - Kotaro Yoshida
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takaramachi, Kanazawa, Ishikawa, 920-8641, Japan.
| | - Takeshi Kobayashi
- Department of Diagnostic and Interventional Radiology, Ishikawa Prefectural Central Hospital, Kanazawa, Ishikawa, 920-8530, Japan
| | - Satoshi Kobayashi
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takaramachi, Kanazawa, Ishikawa, 920-8641, Japan
| | - Toshifumi Gabata
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takaramachi, Kanazawa, Ishikawa, 920-8641, Japan
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Miranda-Schaeubinger M, Derbew HM, Ramirez A, Smith M, Jalloul M, Andronikou S, Otero HJ. Frequency of abnormal findings on chest radiograph after positive PPD in children and adolescents in an urban setting in the United States. Clin Imaging 2024; 105:110024. [PMID: 37989019 DOI: 10.1016/j.clinimag.2023.110024] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/17/2023] [Accepted: 11/05/2023] [Indexed: 11/23/2023]
Abstract
BACKGROUND Chest radiographs (CXR) for tuberculosis (TB) screening in children are valuable in high-burden settings. However, less certain in low prevalence contexts. In the United States, positive PPD is sufficient to treat for "latent" TB, or TB infection in asymptomatic patients. OBJECTIVE We sought to determine frequency of abnormal CXR findings after a positive purified protein derivative (PPD) test at a tertiary pediatric center in the United States. METHOD A retrospective evaluation was conducted of patients (0-18 years) with a CXR after a positive PPD (e.g., known exposure, employment, migratory requirements or before immunosuppression) between 2011 and 2021. Clinical information, demographics, and reason for PPD were recorded from health record. CXRs were evaluated using initial report and by a pediatric radiologist with special interest in TB and 8 years of experience. RESULT Of 485 patients, median [interquartile range (IQR)] age 8.5[3.3-14.4], abnormal CXRs were described in 5 (1%). Most common reasons for PPD included: close contact with someone with TB or with high risk for TB. Most patients 373 (76.9%) received treatment for latent TB, and 111 (22.9%) no treatment. One patient (0.2%) received treatment for active disease. Radiographic findings included isolated lymphadenopathy (n = 2), consolidation (n = 1), pleural fluid/thickening (n = 1) and a patient with lymphadenopathy and a calcified nodule (n = 1). CONCLUSION In our experience, prevalence of chest radiographs findings for patients with positive PPD was very low. Moreover, no cases of severe disease were seen and those with abnormal findings would not merit treatment change under current WHO guidelines.
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Affiliation(s)
- Monica Miranda-Schaeubinger
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA.
| | - Hermon Miliard Derbew
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Alexandra Ramirez
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Maretta Smith
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Mohammad Jalloul
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Savvas Andronikou
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA; University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Hansel J Otero
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA; University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
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9
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DeSanti RL, Gill KG, Swanson JO, Kory PD, Schmidt J, Cowan EA, Lasarev MR, Al-Subu AM. Comparison of chest radiograph and lung ultrasound in children with acute respiratory failure. J Ultrasound 2023; 26:861-870. [PMID: 37747593 PMCID: PMC10632347 DOI: 10.1007/s40477-023-00827-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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/17/2023] [Indexed: 09/26/2023] Open
Abstract
PURPOSE Chest x-ray (CXR) is the standard imaging used to evaluate children in acute respiratory distress and failure. Our objective was to compare the lung-imaging techniques of CXR and lung ultrasound (LUS) in the evaluation of children with acute respiratory failure (ARF) to quantify agreement and to determine which technique identified a higher frequency of pulmonary abnormalities. METHODS This was a secondary analysis of a prospective observational study evaluating the sensitivity and specificity of LUS in children with ARF from 12/2018 to 02/2020 completed at the University of Wisconsin-Madison (USA). Children > 37.0 weeks corrected gestational age and ≤ 18 years of age admitted to the PICU with ARF were evaluated with LUS. We compared CXR and LUS completed within 6 h of each other. Kappa statistics (k) adjusted for maximum attainable agreement (k/kmax) were used to quantify agreement between imaging techniques and descriptive statistics were used to describe the frequency of abnormalities. RESULTS Eighty-eight children had LUS completed, 32 with concomitant imaging completed within 6 h are included. There was fair agreement between LUS and CXR derived diagnoses with 58% agreement (k/kmax = 0.36). Evaluation of imaging patterns included: normal, 57% agreement (k = 0.032); interstitial pattern, 47% agreement (k = 0.003); and consolidation, 65% agreement (k = 0.29). CXR identified more imaging abnormalities than LUS. CONCLUSIONS There is fair agreement between CXR and LUS-derived diagnoses in children with ARF. Given this, clinicians should consider the benefits and limitations of specific imaging modalities when evaluating children with ARF. Additional studies are necessary to further define the role of LUS in pediatric ARF given the small sample size of our study.
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Affiliation(s)
- Ryan L DeSanti
- Department of Pediatrics, Drexel College of Medicine, St Christopher's Hospital for Children, Philadelphia, PA, USA.
- Department of Critical Care Medicine, St Christopher's Hospital for Children, 160 East Erie Avenue, Third Floor Suite, Office A3-20k, Philadelphia, PA, 19143, USA.
| | - Kara G Gill
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Jonathan O Swanson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Pierre D Kory
- Department of Medicine, Advocate Aurora Health Care, St Luke's Medical Center, Milwaukee, WI, USA
| | - Jessica Schmidt
- Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Eileen A Cowan
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Michael R Lasarev
- Department of Biostatistics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Awni M Al-Subu
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
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Ueno M, Yoshida K, Takamatsu A, Kobayashi T, Aoki T, Gabata T. Deep learning-based automatic detection for pulmonary nodules on chest radiographs: The relationship with background lung condition, nodule characteristics, and location. Eur J Radiol 2023; 166:111002. [PMID: 37499478 DOI: 10.1016/j.ejrad.2023.111002] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/11/2023] [Accepted: 07/20/2023] [Indexed: 07/29/2023]
Abstract
PURPOSE Computer-aided diagnosis (CAD), which assists in the interpretation of chest radiographs, is becoming common. However, few studies have evaluated the benefits and pitfalls of CAD in the real world. This study aimed to evaluate the independent performance of commercially available deep learning-based automatic detection (DLAD) software, EIRL Chest X-ray Lung Nodule, in a cohort that included patients with background pulmonary abnormalities often encountered in clinical situations. METHODS Patients with clinically suspected lung cancer for whom chest radiography was performed within a month before or after CT scan between June 2020 and May 2022 in our institution were enrolled. The reference standard was created using a bounding box annotated by two radiologists with reference to the CT. The visibility score, characteristics, location of the pulmonary nodules, presence of overlapping structures or pulmonary disease, and background lung score were manually determined. RESULTS We included 388 patients. The DLAD software detected 222 of the 322 nodules visible on manual evaluation, with a sensitivity of 0.689 and a false-positive rate of 0.168. The detectability of the DLAD software was significantly lower for small and subsolid and nodules with overlapping structures. The visibility score and sensitivity of detection by the DLAD software were positively correlated. The relationship between the background lung score and detection by the DLAD software was unclear. CONCLUSION The standalone performance of DLAD in detecting pulmonary nodules exhibited a sensitivity of 0.689 and a false-positive rate of 0.168. Understanding the characteristics of DLAD is crucial when interpreting chest radiographs with the assistance of the DLAD.
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Affiliation(s)
- Midori Ueno
- Department of Radiology, Kanazawa University Graduate School of Medical Science, 1-13 Takaramachi, Kanazawa City, Ishikawa Prefecture 920-8641, Japan; Department of Radiology, University of Occupational and Environmental Health School of Medicine, 1-1 Iseigaoka, Kitakyushu City, Fukuoka Prefecture 807-8555, Japan.
| | - Kotaro Yoshida
- Department of Radiology, Kanazawa University Graduate School of Medical Science, 1-13 Takaramachi, Kanazawa City, Ishikawa Prefecture 920-8641, Japan.
| | - Atsushi Takamatsu
- Department of Radiology, Kanazawa University Graduate School of Medical Science, 1-13 Takaramachi, Kanazawa City, Ishikawa Prefecture 920-8641, Japan.
| | - Takeshi Kobayashi
- Department of Diagnostic and Interventional Radiology, Ishikawa Prefectural Central Hospital, 1-2, Kuratsuki-Higashi, Kanazawa City, Ishikawa Prefecture 920-8530, Japan.
| | - Takatoshi Aoki
- Department of Radiology, University of Occupational and Environmental Health School of Medicine, 1-1 Iseigaoka, Kitakyushu City, Fukuoka Prefecture 807-8555, Japan.
| | - Toshifumi Gabata
- Department of Radiology, Kanazawa University Graduate School of Medical Science, 1-13 Takaramachi, Kanazawa City, Ishikawa Prefecture 920-8641, Japan.
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11
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Busch F, Xu L, Sushko D, Weidlich M, Truhn D, Müller-Franzes G, Heimer MM, Niehues SM, Makowski MR, Hinsche M, Vahldiek JL, Aerts HJ, Adams LC, Bressem KK. Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs. Comput Methods Programs Biomed 2023; 234:107505. [PMID: 37003043 DOI: 10.1016/j.cmpb.2023.107505] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 02/17/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs. METHODS To improve the efficiency of the segmentation process, we introduced a "human-in-the-loop" segmentation workflow with an active learning approach, looking at five major anatomical structures in the chest (heart, lungs, mediastinum, trachea, and clavicles). This allowed us to decrease the time needed for segmentation by 32% and select the most complex cases to utilize human expert annotators efficiently. After annotation of 2,000 CXRs from different Level 1 medical centers at Charité - University Hospital Berlin, there was no relevant improvement in model performance, and the annotation process was stopped. A 5-layer U-ResNet was trained for 150 epochs using a combined soft Dice similarity coefficient (DSC) and cross-entropy as a loss function. DSC, Jaccard index (JI), Hausdorff distance (HD) in mm, and average symmetric surface distance (ASSD) in mm were used to assess model performance. External validation was performed using an independent external test dataset from Aachen University Hospital (n = 20). RESULTS The final training, validation, and testing dataset consisted of 1900/50/50 segmentation masks for each anatomical structure. Our model achieved a mean DSC/JI/HD/ASSD of 0.93/0.88/32.1/5.8 for the lung, 0.92/0.86/21.65/4.85 for the mediastinum, 0.91/0.84/11.83/1.35 for the clavicles, 0.9/0.85/9.6/2.19 for the trachea, and 0.88/0.8/31.74/8.73 for the heart. Validation using the external dataset showed an overall robust performance of our algorithm. CONCLUSIONS Using an efficient computer-aided segmentation method with active learning, our anatomy-based model achieves comparable performance to state-of-the-art approaches. Instead of only segmenting the non-overlapping portions of the organs, as previous studies did, a closer approximation to actual anatomy is achieved by segmenting along the natural anatomical borders. This novel anatomy approach could be useful for developing pathology models for accurate and quantifiable diagnosis.
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Affiliation(s)
- Felix Busch
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; Department of Anesthesiology, Division of Operative Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
| | - Lina Xu
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Dmitry Sushko
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Matthias Weidlich
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Gustav Müller-Franzes
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Maurice M Heimer
- Department of Radiology, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Stefan M Niehues
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Marcus R Makowski
- Department of Radiology, Technical University of Munich, Munich, Germany
| | - Markus Hinsche
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Janis L Vahldiek
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Hugo Jwl Aerts
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Departments of Radiation Oncology and Radiology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
| | - Lisa C Adams
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Keno K Bressem
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
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12
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Sriboonyong T, Katanyuwong P, Vaewpanich J. A unilateral whiteout lung in child with multisystem inflammatory syndrome associated with COVID-19 due to SARS-CoV-2: one case report of a boy. BMC Pulm Med 2023; 23:157. [PMID: 37143019 PMCID: PMC10157560 DOI: 10.1186/s12890-023-02428-1] [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: 12/07/2022] [Accepted: 04/10/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Multisystem inflammatory syndrome in children (MIS-C) is a relatively new and rare complication of COVID-19. This complication seems to develop after the infection rather than during the acute phase of COVID-19. This report aims to describe a case of MIS-C in an 8-year-old Thai boy who presented with unilateral lung consolidation. Unilateral whiteout lung is not a common pediatric chest radiograph finding in MIS-C, but this is attributed to severe acute respiratory failure. CASE PRESENTATION An 8-year-old boy presented with persistent fever for seven days, right cervical lymphadenopathy, and dyspnea for 12 h. The clinical and biochemical findings were compatible with MIS-C. Radiographic features included total opacity of the right lung and CT chest found consolidation and ground-glass opacities of the right lung. He was treated with intravenous immunoglobulin and methylprednisolone, and he dramatically responded to the treatment. He was discharged home in good condition after 8 days of treatment. CONCLUSION Unilateral whiteout lung is not a common pediatric chest radiographic finding in MIS-C, but when it is encountered, a timely and accurate diagnosis is required to avoid delays and incorrect treatment. We describe a pediatric patient with unilateral lung consolidation from the inflammatory process.
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Affiliation(s)
- Tidarat Sriboonyong
- Division of Pediatric Pulmonology, Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Poomiporn Katanyuwong
- Division of Pediatric Cardiology, Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Jarin Vaewpanich
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
- Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand.
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13
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Chutivanidchayakul F, Suwatanapongched T, Petnak T. Clinical and chest radiographic features of missed lung cancer and their association with patient outcomes. Clin Imaging 2023; 99:73-81. [PMID: 37121220 DOI: 10.1016/j.clinimag.2023.03.017] [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: 12/14/2022] [Revised: 03/10/2023] [Accepted: 03/23/2023] [Indexed: 05/02/2023]
Abstract
PURPOSE To examine clinical and chest radiographic features of missed lung cancer (MLC) and explore their association with patient outcomes. METHODS We retrospectively reviewed chest radiographs obtained at least six months before lung cancer (LC) diagnosis in 95 patients to identify the first positive chest radiograph showing MLC. We assessed chest radiographic features of MLC and their association with patient outcomes. RESULTS Seventy-five (78.9%) patients (39 men, 36 women; mean age, 64.5 ± 10.5 years) had MLC. The median diagnostic delay was 31.3 months (6.6-128.0 months). The median MLC size was 16 mm (5-57 mm), and 54.7%, 68.0%, and 74.7% of MLC were in the left lung, the middle/lower zones, and the outer two-thirds of the lung, respectively. MLC exhibited a round/oval shape, partly/poorly defined margin, irregular/spiculated border, a density less than the aortic knob, and anatomical superimposition in 57.3%, 77.3%, 61.3%, 85.3%, and 88.0% of cases, respectively. Thirty-five (46.7%) patients had stage III + IV LC at diagnosis. Thirty-one (41.3%) patients died. MLC in the inner one-third of the lung, exhibiting a density equal to/greater than the aortic knob, or superimposed by midline structures was significantly associated with stage III + IV LC at diagnosis. The 3-year all-cause mortality significantly increased when MLC was in the upper zone, superimposed by pulmonary vessels, superimposed by pulmonary vessels plus ribs, or superimposed by pulmonary vessels plus in the inner one-third of the lung. CONCLUSION MLC with some radiographic features pertaining to their location, density, and superimposed structures was found to portend a worse outcome.
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Affiliation(s)
- Fonthip Chutivanidchayakul
- Division of Diagnostic Radiology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Thitiporn Suwatanapongched
- Division of Diagnostic Radiology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
| | - Tananchai Petnak
- Division of Pulmonary and Pulmonary Critical Care Medicine, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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Yim J, Chang SA, Yeung DF, Sayre EC, Gin K, Jue J, Nair P, Tsang MYC, Luong C, Tsang TSM. Quantification of pleural effusions by two-dimensional transthoracic echocardiography. J Echocardiogr 2023; 21:33-9. [PMID: 35974215 DOI: 10.1007/s12574-022-00586-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 06/28/2022] [Accepted: 07/31/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE There is lack of validated methods for quantifying the size of pleural effusion from standard transthoracic (TTE) windows. The purpose of this study is to determine whether pleural effusion (Peff) measured from routine two-dimensional (2D) TTE views correlate with chest radiograph (CXR). MATERIALS AND METHODS We retrospectively identified all inpatients who underwent a TTE and CXR within 2 days in a large tertiary care center. Peff was measured on TTE from parasternal long axis (PLAX), apical four-chamber (A4C), and subcostal views and on CXR. Logistic regression models were used determine optimal cut points to predict moderate or greater Peff. RESULTS In 200 patients (mean age 69.3 ± 14.3 years, 49.5% female), we found statistically significant associations between Peff size assessed by all TTE views and CXR, with weak to moderate correlation (PLAX length: 0.21 (95% CI [0.05, 0.35]); PLAX depth: 0.21 (95% CI [0.05, 0.35]); A4C left: 0.31 (95% CI [0.13, 0.46]); A4C right: 0.39 (95% CI [0.17, 0.57]); subcostal: 0.38 (95% CI [0.07, 0.61]). The best TTE thresholds for predicting moderate or greater left-sided Peff on CXR was PLAX length left > = 8.6 cm (sensitivity 78%, specificity 54%, PPV 26%, and NPV 92%). The best TTE thresholds for predicting moderate or greater right-sided Peff on CXR was A4C right > = 2.6 cm (sensitivity 87%, specificity 60%, PPV 37%, and NPV 94%). CONCLUSIONS We identified statistically significant associations with Peff size measured on TTE and CXR. The predictive ability of TTE to identify moderate or large pleural effusion is limited.
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Venugopalan Nair A, Kumar D, McInnes M, Hadi AA, Valiyakath Subair HS, Khyatt OA, Almashhadani MA, Jacob B, Vasudevan A, Ashruf MZ, Al-Heidous M, Kuttikatt Soman D. Utility of chest radiograph severity scoring in emergency department for predicting outcomes in COVID-19: A study of 1275 patients. Clin Imaging 2023; 95:65-70. [PMID: 36623355 PMCID: PMC9794386 DOI: 10.1016/j.clinimag.2022.12.002] [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] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/07/2022] [Accepted: 12/07/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To measure the reliability and reproducibility of a chest radiograph severity score (CSS) in prognosticating patient's severity of disease and outcomes at the time of disease presentation in the emergency department (ED) with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS We retrospectively studied 1275 consecutive RT-PCR confirmed COVID-19 adult patients presenting to ED from March 2020 through June 2020. Chest radiograph severity score was assessed for each patient by two blinded radiologists. Clinical and laboratory parameters were collected. The rate of admission to intensive care unit, mechanical ventilation or death up to 60 days after the baseline chest radiograph were collected. Primary outcome was defined as occurrence of ICU admission or death. Multivariate logistic regression was performed to evaluate the relationship between clinical parameters, chest radiograph severity score, and primary outcome. RESULTS CSS of 3 or more was associated with ICU admission (78 % sensitivity; 73.1 % specificity; area under curve 0.81). CSS and pre-existing diabetes were independent predictors of primary outcome (odds ratio, 7; 95 % CI: 3.87, 11.73; p < 0.001 & odds ratio, 2; 95 % CI: 1-3.4, p 0.02 respectively). No significant difference in primary outcome was observed for those with history of hypertension, asthma, chronic kidney disease or coronary artery disease. CONCLUSION Semi-quantitative assessment of CSS at the time of disease presentation in the ED predicted outcomes in adults of all age with COVID-19.
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Affiliation(s)
- Anirudh Venugopalan Nair
- Dept of Clinical Radiology, NHS Salisbury Foundation Trust, Wiltshire, United Kingdom; Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar.
| | - Devendra Kumar
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
| | - Matthew McInnes
- The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Ahmed Akram Hadi
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
| | | | - Omar Ammar Khyatt
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
| | | | - Bamil Jacob
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
| | | | | | - Mahmoud Al-Heidous
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
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Ryu J, Eom S, Kim HC, Kim CO, Rhee Y, You SC, Hong N. Chest X-ray-based opportunistic screening of sarcopenia using deep learning. J Cachexia Sarcopenia Muscle 2023; 14:418-428. [PMID: 36457204 PMCID: PMC9891971 DOI: 10.1002/jcsm.13144] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 10/26/2022] [Accepted: 10/31/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Early detection and management of sarcopenia is of clinical importance. We aimed to develop a chest X-ray-based deep learning model to predict presence of sarcopenia. METHODS Data of participants who visited osteoporosis clinic at Severance Hospital, Seoul, South Korea, between January 2020 and June 2021 were used as derivation cohort as split to train, validation and test set (65:15:20). A community-based older adults cohort (KURE) was used as external test set. Sarcopenia was defined based on Asian Working Group 2019 guideline. A deep learning model was trained to predict appendicular lean mass (ALM), handgrip strength (HGS) and chair rise test performance from chest X-ray images; then the machine learning model (SARC-CXR score) was built using the age, sex, body mass index and chest X-ray predicted muscle parameters along with estimation uncertainty values. RESULTS Mean age of the derivation cohort (n = 926; women n = 700, 76%; sarcopenia n = 141, 15%) and the external test (n = 149; women n = 95, 64%; sarcopenia n = 18, 12%) cohort was 61.4 and 71.6 years, respectively. In the internal test set (a hold-out set, n = 189, from the derivation cohort) and the external test set (n = 149), the concordance correlation coefficient for ALM prediction was 0.80 and 0.76, with an average difference of 0.18 ± 2.71 and 0.21 ± 2.28, respectively. Gradient-weight class activation mapping for deep neural network models to predict ALM and HGS commonly showed highly weight pixel values at bilateral lung fields and part of the cardiac contour. SARC-CXR score showed good discriminatory performance for sarcopenia in both internal test set [area under the receiver-operating characteristics curve (AUROC) 0.813, area under the precision-recall curve (AUPRC) 0.380, sensitivity 0.844, specificity 0.739, F1-score 0.540] and external test set (AUROC 0.780, AUPRC 0.440, sensitivity 0.611, specificity 0.855, F1-score 0.458). Among SARC-CXR model features, predicted low ALM from chest X-ray was the most important predictor of sarcopenia based on SHapley Additive exPlanations values. Higher estimation uncertainty of HGS contributed to elevate the predicted risk of sarcopenia. In internal test set, SARC-CXR score showed better discriminatory performance than SARC-F score (AUROC 0.813 vs. 0.691, P = 0.029). CONCLUSIONS Chest X-ray-based deep leaning model improved detection of sarcopenia, which merits further investigation.
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Affiliation(s)
- Jin Ryu
- Department of Internal Medicine, Severance Hospital, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Sujeong Eom
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyeon Chang Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.,Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea
| | - Chang Oh Kim
- Division of Geriatrics, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Yumie Rhee
- Department of Internal Medicine, Severance Hospital, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.,Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea
| | - Namki Hong
- Department of Internal Medicine, Severance Hospital, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, South Korea.,Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea
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17
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Oliver M, Renou A, Allou N, Moscatelli L, Ferdynus C, Allyn J. Image augmentation and automated measurement of endotracheal-tube-to-carina distance on chest radiographs in intensive care unit using a deep learning model with external validation. Crit Care 2023; 27:40. [PMID: 36698191 PMCID: PMC9878756 DOI: 10.1186/s13054-023-04320-0] [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: 08/08/2022] [Accepted: 11/26/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Chest radiographs are routinely performed in intensive care unit (ICU) to confirm the correct position of an endotracheal tube (ETT) relative to the carina. However, their interpretation is often challenging and requires substantial time and expertise. The aim of this study was to propose an externally validated deep learning model with uncertainty quantification and image segmentation for the automated assessment of ETT placement on ICU chest radiographs. METHODS The CarinaNet model was constructed by applying transfer learning to the RetinaNet model using an internal dataset of ICU chest radiographs. The accuracy of the model in predicting the position of the ETT tip and carina was externally validated using a dataset of 200 images extracted from the MIMIC-CXR database. Uncertainty quantification was performed using the level of confidence in the ETT-carina distance prediction. Segmentation of the ETT was carried out using edge detection and pixel clustering. RESULTS The interrater agreement was 0.18 cm for the ETT tip position, 0.58 cm for the carina position, and 0.60 cm for the ETT-carina distance. The mean absolute error of the model on the external test set was 0.51 cm for the ETT tip position prediction, 0.61 cm for the carina position prediction, and 0.89 cm for the ETT-carina distance prediction. The assessment of ETT placement was improved by complementing the human interpretation of chest radiographs with the CarinaNet model. CONCLUSIONS The CarinaNet model is an efficient and generalizable deep learning algorithm for the automated assessment of ETT placement on ICU chest radiographs. Uncertainty quantification can bring the attention of intensivists to chest radiographs that require an experienced human interpretation. Image segmentation provides intensivists with chest radiographs that are quickly interpretable and allows them to immediately assess the validity of model predictions. The CarinaNet model is ready to be evaluated in clinical studies.
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Affiliation(s)
- Matthieu Oliver
- grid.440886.60000 0004 0594 5118Methodological Support Unit, Reunion University Hospital, Saint-Denis, France ,grid.440886.60000 0004 0594 5118Clinical Informatics Department, Reunion University Hospital, Saint-Denis, France
| | - Amélie Renou
- grid.440886.60000 0004 0594 5118Intensive Care Unit, Reunion University Hospital, Saint-Denis, France
| | - Nicolas Allou
- grid.440886.60000 0004 0594 5118Methodological Support Unit, Reunion University Hospital, Saint-Denis, France ,grid.440886.60000 0004 0594 5118Intensive Care Unit, Reunion University Hospital, Saint-Denis, France ,grid.440886.60000 0004 0594 5118Clinical Informatics Department, Reunion University Hospital, Saint-Denis, France
| | - Lucas Moscatelli
- grid.440886.60000 0004 0594 5118Radiology, Reunion University Hospital, Saint-Denis, France
| | - Cyril Ferdynus
- grid.440886.60000 0004 0594 5118Methodological Support Unit, Reunion University Hospital, Saint-Denis, France ,grid.440886.60000 0004 0594 5118Clinical Informatics Department, Reunion University Hospital, Saint-Denis, France ,Clinical Research Department, INSERM CIC 1410, F-97410 Saint-Pierre, France
| | - Jerôme Allyn
- grid.440886.60000 0004 0594 5118Methodological Support Unit, Reunion University Hospital, Saint-Denis, France ,grid.440886.60000 0004 0594 5118Intensive Care Unit, Reunion University Hospital, Saint-Denis, France ,grid.440886.60000 0004 0594 5118Clinical Informatics Department, Reunion University Hospital, Saint-Denis, France
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18
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J-P NA, Susanto AD, Samoedro E, Mansyur M, Tungsagunwattana S, Lertrojanapunya S, Subhannachart P, Siriruttanapruk S, Dumavibhat N, Algranti E, Parker JE, Hering KG, Kanayama H, Tamura T, Kusaka Y, Suganuma N. Inter-observer agreement and accuracy in classifying radiographs for pneumoconiosis among Asian physicians taking AIR Pneumo certification examination. Ind Health 2022; 60:459-469. [PMID: 34803130 PMCID: PMC9539454 DOI: 10.2486/indhealth.2021-0210] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/13/2021] [Indexed: 06/13/2023]
Abstract
This study examined inter-observer agreement and diagnostic accuracy in classifying radiographs for pneumoconiosis among Asian physicians taking the AIR Pneumo examination. We compared agreement and diagnostic accuracy for parenchymal and pleural lesions across residing countries, specialty training, and work experience using data on 93 physicians. Physicians demonstrated fair to good agreement with kappa values 0.30 (95% CI: 0.20-0.40), 0.29 (95% CI: 0.23-0.36), 0.59 (95% CI: 0.52-0.67), and 0.65 (95% CI: 0.55-0.74) in classifying pleural plaques, small opacity shapes, small opacity profusion, and large opacities, respectively. Kappa values among Asian countries ranging from 0.25 to 0.55 (pleural plaques), 0.47 to 0.73 (small opacity profusion), and 0.55 to 0.69 (large opacity size). The median Youden's J index (interquartile range) for classifying pleural plaque, small opacity, and large opacity was 61.1 (25.5), 76.8 (29.3), and 88.9 (23.3), respectively. Radiologists and recent graduates showed superior performance than other groups regarding agreement and accuracy in classifying all types of lesions. In conclusion, Asian physicians taking the AIR Pneumo examination were better at classifying parenchymal lesions than pleural plaques using the ILO classification. The degree of agreement and accuracy was different among countries and was associated with background specialty training.
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Affiliation(s)
- Naw Awn J-P
- Department of Environmental Medicine, Kochi Medical School, Kochi University, Japan
| | - Agus Dwi Susanto
- Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Indonesia, Persahabatan Hospital, Indonesia
| | - Erlang Samoedro
- Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Indonesia, Persahabatan Hospital, Indonesia
| | - Muchtaruddin Mansyur
- Department of Community Medicine, Faculty of Medicine, Universitas Indonesia & Southeast Asian Ministers of Education Regional Centre for Food and Nutrition (SEAMEO RECFON), Indonesia
| | - Sutarat Tungsagunwattana
- Department of radiology, Central Chest Institute of Thailand, Department of Medical Services, Ministry of Public Health, Thailand
| | - Saijai Lertrojanapunya
- Department of radiology, Central Chest Institute of Thailand, Department of Medical Services, Ministry of Public Health, Thailand
| | - Ponglada Subhannachart
- Department of radiology, Central Chest Institute of Thailand, Department of Medical Services, Ministry of Public Health, Thailand
| | | | - Narongpon Dumavibhat
- Department of Preventive and Social Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand
| | | | - John E Parker
- Pulmonary and Critical Care Medicine, Robert C. Byrd Health Sciences Center, School of Medicine, West Virginia University, USA
| | - Kurt G Hering
- Department of Diagnostic Radiology, Radio-oncology and Nuclear Medicine, Radiological Clinic, Miner's Hospital. Klinikum-Westfalen (Knappschaftskrankenhaus), Germany
| | - Hitomi Kanayama
- Division of Environmental Health, Department of International Social and Health Sciences, Faculty of Medical Sciences, University of Fukui, Japan
| | | | - Yukinori Kusaka
- School of Medical Sciences, University of Fukui, Japan
- Kochi Medical School, Kochi University, Japan
| | - Narufumi Suganuma
- Department of Environmental Medicine, Kochi Medical School, Kochi University, Japan
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19
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Drummond D, Hadchouel A, Petit A, Khen-Dunlop N, Lozach C, Delacourt C, Berteloot L. Strategies for recognizing pneumonia look-alikes. Eur J Pediatr 2022; 181:3565-3575. [PMID: 35906335 DOI: 10.1007/s00431-022-04575-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 11/03/2022]
Abstract
Community-acquired pneumonia is a common diagnosis in children. Among the many children whose symptoms and/or chest X-ray is consistent with community-acquired pneumonia, it can be difficult to distinguish the rare cases of differential diagnoses that require specific management. The aim of this educational article is to provide clinicians with a series of questions to ask themselves in order to detect a possible differential diagnosis of pneumonia in children. The value of this approach is illustrated by 13 real clinical cases in which a child was misdiagnosed as having lobar pneumonia. What is Known: • When a lobar pneumonia is diagnosed, an appropriate antibiotic treatment leads to the resolution of the clinical signs in most cases. • However, several diseases can be look-alikes for pneumonia and mislead the practitioner. What is New: • This article provides a new approach to identify differential diagnoses of pneumonia in children. • It is illustrated by 13 real-life situations of children misdiagnosed as having pneumonia.
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Affiliation(s)
- David Drummond
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 rue de Sèvres, 75015, Paris, France. .,Université de Paris, Paris, France.
| | - Alice Hadchouel
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 rue de Sèvres, 75015, Paris, France.,Université de Paris, Paris, France
| | - Arnaud Petit
- Department of Pediatric Hematology and Oncology, University Hospital Trousseau, AP-HP, Paris, France.,Paris-Sorbonne University, Paris, France
| | - Naziha Khen-Dunlop
- Department of Pediatric Surgery, University Hospital Necker-Enfants Malades, AP-HP, Paris, France
| | - Cécile Lozach
- Department of Pediatric Radiology, University Hospital Necker-Enfants Malades, AP-HP, Paris, France
| | - Christophe Delacourt
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 rue de Sèvres, 75015, Paris, France.,Université de Paris, Paris, France
| | - Laureline Berteloot
- Department of Pediatric Radiology, University Hospital Necker-Enfants Malades, AP-HP, Paris, France
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20
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Yoo H, Kim EY, Kim H, Choi YR, Kim MY, Hwang SH, Kim YJ, Cho YJ, Jin KN. Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort. Korean J Radiol 2022; 23:1009-1018. [PMID: 36175002 PMCID: PMC9523233 DOI: 10.3348/kjr.2022.0189] [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: 03/20/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 01/17/2023] Open
Abstract
Objective This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. Materials and Methods This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). Results Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. Conclusion This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.
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Affiliation(s)
- Hyunsuk Yoo
- Lunit Inc, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea
| | - Eun Young Kim
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea
| | - Ye Ra Choi
- Department of Radiology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul, Korea
| | - Moon Young Kim
- Department of Radiology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul, Korea
| | - Sung Ho Hwang
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Young Joong Kim
- Department of Radiology, Konyang University Hospital, Konyang University College of Medicine, Daejeon, Korea
| | - Young Jun Cho
- Department of Radiology, Konyang University Hospital, Konyang University College of Medicine, Daejeon, Korea
| | - Kwang Nam Jin
- Department of Radiology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul, Korea.
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21
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Giannelli F, Cozzi D, Cavigli E, Campolmi I, Rinaldi F, Giachè S, Rogasi PG, Miele V, Bartolucci M. Lung ultrasound (LUS) in pulmonary tuberculosis: correlation with chest CT and X-ray findings. J Ultrasound 2022; 25:625-634. [PMID: 35001323 PMCID: PMC9402828 DOI: 10.1007/s40477-021-00636-1] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 11/09/2021] [Indexed: 11/30/2022] Open
Abstract
AIMS The aim is to describe lung ultrasound (LUS) findings in a cohort of patients with suspected pulmonary tuberculosis (PTB) and compare them with computed tomography (CT) and chest x-ray (CXR) findings in order to evaluate the potentiality of LUS in TB diagnosis. METHODS In this prospective study, 82 subjects with suspected TB were enrolled after being evaluated with CXR and chest CT. LUS was performed by blinded radiologists within 3 days after admission. A semiquantitative index was used: score 1 (lesions that extend for about 1-15% of the affected zone), score 2 (15-40%) and score 3 (40-100%). RESULTS Microbiological analysis confirmed TB diagnosis in 58/82 (70.7%). CT was positive in all patients, LUS in 79/82 (96.3%) CXR in 78/82 (95.1%) and adding LUS and CXR in 100%. In PTB patients we found a great number of lungs zones with micronodules and with total findings than non-TPB patients (p < 0.05). Overall LUS sensitivity was 80%, greater for micronodules (82%) and nodules (95%), lower for consolidation with air bronchogram (72%) and cavitations (33%). We reported 5 complicated pleural effusion at LUS, only 1 in CT. CXR overall sensitivity was 81%. Adding CXR and LUS findings we reported a sensitivity of 90%. CONCLUSIONS LUS could be considered a valid, non-invasive and cost-effective diagnostic tool especially in world regions where CT were not available, also in addiction with CXR. TRIAL REGISTRATION This study was approved by the Ethics Committee of our University Hospital (rif. CEAVC 14,816).
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Affiliation(s)
- Federico Giannelli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
| | - Edoardo Cavigli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Irene Campolmi
- Department of Infectious and Tropical Diseases, Careggi University Hospital, Florence, Italy
| | - Francesca Rinaldi
- Department of Infectious and Tropical Diseases, Careggi University Hospital, Florence, Italy
| | - Susanna Giachè
- Department of Infectious and Tropical Diseases, Careggi University Hospital, Florence, Italy
| | - Pier Giorgio Rogasi
- Department of Infectious and Tropical Diseases, Careggi University Hospital, Florence, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Maurizio Bartolucci
- Department of Radiology, Santo Stefano Hospital, ASL Toscana Centro, Prato, Italy
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22
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Kim KD, Cho K, Kim M, Lee KH, Lee S, Lee SM, Lee KH, Kim N. Enhancing deep learning based classifiers with inpainting anatomical side markers (L/R markers) for multi-center trials. Comput Methods Programs Biomed 2022; 220:106705. [PMID: 35462346 DOI: 10.1016/j.cmpb.2022.106705] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/14/2022] [Accepted: 02/20/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE The protocol for placing anatomical side markers (L/R markers) in chest radiographs varies from one hospital or department to another. However, the markers have strong signals that can be useful for deep learning-based classifier to predict diseases. We aimed to enhance the performance of a deep learning-based classifiers in multi-center datasets by inpainting the L/R markers. METHODS The L/R marker was detected with using the EfficientDet detection network; only the detected regions were inpainted using a generative adversarial network (GAN). To analyze the effect of the inpainting in detail, deep learning-based classifiers were trained using original images, marker-inpainted images, and original images clipped using the min-max value of the marker-inpainted images. Binary classification, multi-class classification, and multi-task learning with segmentation and classification were developed and evaluated. Furthermore, the performances of the network on internal and external validation datasets were compared using DeLong's test for two correlated receiver operating characteristic (ROC) curves in binary classification and Stuart-Maxwell test for marginal homogeneity in multi-class classification and multi-task learning. In addition, the qualitative results of activation maps were evaluated using the gradient-class activation map (Grad-CAM). RESULTS Marker-inpainting preprocessing improved the classification performances. In the binary classification based on the internal validation, the area under the curves (AUCs) and accuracies were 0.950 and 0.900 for the model trained on the min-max clipped images and 0.911 and 0.850 for the model trained on the original images, respectively (P-value=0.006). In the external validation, the AUCs and accuracies were 0.858 and 0.677 for the model trained using the inpainted images and 0.723 and 0.568 for the model trained using the original images (P-value<0.001), respectively. In addition, the models trained using the marker inpainted images showed the best performance in multi-class classification and multi-task learning. Furthermore, the activation maps obtained using the Grad-CAM improved with the proposed method. The 5-fold validation results also showed improvement trend according to the preprocessing strategies. CONCLUSIONS Inpainting an L/R marker significantly enhanced the classifier's performance and robustness, especially in internal and external studies, which could be useful in developing a more robust and accurate deep learning-based classifier for multi-center trials. The code for detection is available at: https://github.com/mi2rl/MI2RLNet. And the code for inpainting is available at: https://github.com/mi2rl/L-R-marker-inpainting.
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Affiliation(s)
- Ki Duk Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea
| | - Kyungjin Cho
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Mingyu Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea
| | - Kyung Hwa Lee
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Seungjun Lee
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Sang Min Lee
- Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea
| | - Kyung Hee Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
| | - Namkug Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea; Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea.
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23
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Cho K, Seo J, Kyung S, Kim M, Hong GS, Kim N. Bone suppression on pediatric chest radiographs via a deep learning-based cascade model. Comput Methods Programs Biomed 2022; 215:106627. [PMID: 35032722 DOI: 10.1016/j.cmpb.2022.106627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 12/05/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Bone suppression images (BSIs) of chest radiographs (CXRs) have been proven to improve diagnosis of pulmonary diseases. To acquire BSIs, dual-energy subtraction (DES) or a deep-learning-based model trained with DES-based BSIs have been used. However, neither technique could be applied to pediatric patients owing to the harmful effects of DES. In this study, we developed a novel method for bone suppression in pediatric CXRs. METHODS First, a model using digitally reconstructed radiographs (DRRs) of adults, which were used to generate pseudo-CXRs from computed tomography images, was developed by training a 2-channel contrastive-unpaired-image-translation network. Second, this model was applied to 129 pediatric DRRs to generate the paired training data of pseudo-pediatric CXRs. Finally, by training a U-Net with these paired data, a bone suppression model for pediatric CXRs was developed. RESULTS The evaluation metrics were peak signal to noise ratio, root mean absolute error and structural similarity index measure at soft-tissue and bone region of the lung. In addition, an expert radiologist scored the effectiveness of BSIs on a scale of 1-5. The obtained result of 3.31 ± 0.48 indicates that the BSIs show homogeneous bone removal despite subtle residual bone shadow. CONCLUSION Our method shows that the pixel intensity at soft-tissue regions was preserved, and bones were well subtracted; this can be useful for detecting early pulmonary disease in pediatric CXRs.
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Affiliation(s)
- Kyungjin Cho
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea
| | - Jiyeon Seo
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea
| | - Sunggu Kyung
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea
| | - Mingyu Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, Korea
| | - Gil-Sun Hong
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine & Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu Seoul 05505, Republic of Korea.
| | - Namkug Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, Korea; Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.
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24
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Wallis TJM, Welham B, Kong A, Morelli T, Azim A, Horno J, Wilkinson M, Burke H, Freeman A, Wilkinson TMA, Jones MG, Marshall BG. Predicting the risk of chest radiograph abnormality 12-weeks post hospitalisation with SARS CoV-2 PCR confirmed COVID-19. Respir Res 2022; 23:297. [PMID: 36316730 PMCID: PMC9620600 DOI: 10.1186/s12931-022-02217-0] [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: 02/25/2022] [Accepted: 10/09/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Routine follow-up of patients hospitalised with COVID-19 is recommended, however due to the ongoing high number of infections this is not without significant health resource and economic burden. In a previous study we investigated the prevalence of, and risk factors for, persistent chest radiograph (CXR) abnormalities post-hospitalisation with COVID-19 and identified a 5-point composite score that strongly predicted risk of persistent CXR abnormality at 12-weeks. Here we sought to validate and refine our findings in an independent cohort of patients. METHODOLOGY A single-centre prospective study of consecutive patients attending a virtual post-hospitalisation COVID-19 clinic and CXR as part of their standard clinical care between 2nd March - 22nd June 2021. Inpatient and follow-up CXRs were scored by the assessing clinician for extent of pulmonary infiltrates (0-4 in each lung) with complete resolution defined as a follow-up score of zero. RESULTS 182 consecutive patients were identified of which 31% had persistent CXR abnormality at 12-weeks. Patients with persistent CXR abnormality were significantly older (p < 0.001), had a longer hospital length of stay (p = 0.005), and had a higher incidence of both level 2 or 3 facility admission (level 2/3 care) (p = 0.003) and ever-smoking history (p = 0.038). Testing our composite score in the present cohort we found it predicted persistent CXR abnormality with reasonable accuracy (area under the receiver operator curve [AUROC 0.64]). Refining this score replacing obesity with Age ≥ 50 years, we identify the SHADE-750 score (1-point each for; Smoking history, Higher-level care (level 2/3 admission), Age ≥ 50 years, Duration of admission ≥ 15 days and Enzyme-lactate dehydrogenase (LDH ≥ 750U/L), that accurately predicted risk of persistent CXR abnormality, both in the present cohort (AUROC 0.73) and when retrospectively applied to our 1st cohort (AUROC 0.79). Applied to both cohorts combined (n = 213) it again performed strongly (AUROC 0.75) with all patients with a score of zero (n = 18) having complete CXR resolution at 12-weeks. CONCLUSIONS In two independent cohorts of patients hospitalised with COVID-19, we identify a 5-point score which accurately predicts patients at risk of persistent CXR abnormality at 12-weeks. This tool could be used by clinicians to identify patients in which radiological follow-up may not be required.
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Affiliation(s)
- Tim JM Wallis
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Benjamin Welham
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Alex Kong
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Tommaso Morelli
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Adnan Azim
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Jose Horno
- grid.123047.30000000103590315Department of Respiratory Medicine, University Hospital Southampton, Southampton, UK
| | - Miranda Wilkinson
- grid.123047.30000000103590315Department of Respiratory Medicine, University Hospital Southampton, Southampton, UK
| | - Hannah Burke
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Anna Freeman
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Thomas MA Wilkinson
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Mark G Jones
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
| | - Benjamin G Marshall
- grid.5491.90000 0004 1936 9297Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, School of Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, University of Southampton, Southampton, UK
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Tran KH, Nguyen-Thi KH, Pham NC, Dang CT. Loeffler's syndrome in a child: A rare radiological and histopathological diagnosis. Radiol Case Rep 2021; 17:245-249. [PMID: 34840639 PMCID: PMC8607134 DOI: 10.1016/j.radcr.2021.10.044] [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: 09/24/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 11/19/2022] Open
Abstract
Loffler syndrome is an uncommon, self-limited, benign pulmonary eosinophilia that usually lasts less than a month. Abnormal chest radiography occurs in 95% of patients; however, computed tomography findings are not well described. We present clinical features, radiological, and pathological findings of Loeffler's syndrome with secondary bacterial pneumonia in a child. He presented with dry cough, hemoptysis 2 times, chest pain for 1 week. Blood tests revealed high C-reactive protein levels and eosinophilia. On the initial computed tomography (CT) scan, a lesion was discovered at the upper edge of the right lung hilum. The lesion developed in size, together with right pleural effusion, on the repeated CT scan. A lung biopsy revealed a substantial number of inflammatory cells, including eosinophils and neutrophils. After ruling all other possibilities, Loffler's syndrome was confirmed. As a result of antibiotic treatment, favorable outcomes were confirmed by improving clinical symptoms and follow-up chest CT scans. A close combination of pulmonary symptoms, peripheral blood eosinophilia, abnormal chest imaging, and histopathological findings must be taken to confirm the diagnosis of Loeffler's syndrome.
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Affiliation(s)
- Kiem Hao Tran
- Department of Health, Provincial People's Committee, Hue city, 530000, Viet Nam.,Pediatric Center, Hue Central Hospital, Hue city, 530000, Viet Nam
| | - Kim Hoa Nguyen-Thi
- Department of Health, Provincial People's Committee, Hue city, 530000, Viet Nam
| | - Nguyen Cuong Pham
- Pathology Department, Hue Central Hospital, Hue city, 530000, Viet Nam
| | - Cong Thuan Dang
- Department of Histology, Embryology, Pathology and Forensic Medicine, University of Medicine and Pharmacy, Hue University, 6 Ngo Quyen street, Hue city, 530000, Viet Nam
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26
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Govindarajan S, Swaminathan R. Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks. APPL INTELL 2021; 51:2764-75. [PMID: 34764563 DOI: 10.1007/s10489-020-01941-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2020] [Indexed: 12/24/2022]
Abstract
In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets. Significant biomarkers representing critical image features are extracted from CNN by experimentally investigating on cross-validation methods and hyperparameter settings. The performance of the network is evaluated using standard metrics. Perturbation based occlusion sensitivity maps are employed on the features obtained from the classification model to visualise the localization of abnormal areas. Results demonstrate that the simplified CNN model with optimised parameters is able to extract significant features with a sensitivity of 97.35% and F-measure of 96.71% to detect COVID-19 images. The algorithm achieves an Area Under the Curve-Receiver Operating Characteristic score of 99.4% with Matthews correlation coefficient of 0.93. High value of Diagnostic odds ratio is also obtained. Occlusion sensitivity maps provide precise localization of abnormal regions by identifying COVID-19 conditions. As early detection through chest radiographic images are useful for automated screening of the disease, this method appears to be clinically relevant in providing a visual diagnostic solution using a simplified and efficient model.
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27
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Hyun M, Lee JY, Park JS, Kim JY, Kim HA. Comparison of the Characteristics of Asymptomatic and Presymptomatic Patients with Coronavirus Disease 2019 in the Republic of Korea. J Epidemiol Glob Health 2021. [PMID: 34757529 DOI: 10.1007/s44197-021-00011-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 10/15/2021] [Indexed: 01/08/2023] Open
Abstract
PURPOSE This retrospective study aimed to evaluate the baseline characteristics of asymptomatic patients with coronavirus disease 2019 at admission and to follow-up their clinical manifestations and radiological findings during hospitalization. METHODS Patients with coronavirus disease 2019 who were asymptomatic at admission were divided into two groups-those with no symptoms until discharge (group A) and those who developed symptoms after admission (group B). Patients who could not express their own symptoms were excluded. RESULTS Overall, 127 patients were enrolled in the study, of whom 19 and 108 were assigned to groups A and B, respectively. The mean age and median C-reactive protein level were higher in group B than in group A. All patients in group A and one-third of patients in group B had normal initial chest radiographs; 15.8% and 48.1% of patients in groups A and B, respectively, had pneumonia during hospitalization. One patient in group B, whose condition was not severe at the time of admission, deteriorated due to aggravated pneumonia and was transferred to a tertiary hospital. CONCLUSION We summarize the clinical characteristics during hospitalization of patients with coronavirus disease 2019 who were purely asymptomatic at the time of admission. The majority of asymptomatic patients with coronavirus disease 2019 were discharged without significant events during hospitalization. However, it may be difficult to predict subsequent events from initial chest radiographs or oxygen saturation at admission.
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Kim JH, Han SG, Cho A, Shin HJ, Baek SE. Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study. BMC Med Inform Decis Mak 2021; 21:311. [PMID: 34749731 PMCID: PMC8573755 DOI: 10.1186/s12911-021-01679-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 06/21/2021] [Accepted: 11/01/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the effect of deep learning-based assistive technology on CR interpretation (DLCR), although its relevance to ED physicians remains unclear. This study aimed to investigate whether DLCR supports CR interpretation and the clinical decision-making of ED physicians. METHODS We conducted a prospective interventional study using a web-based performance assessment system. Study participants were recruited through the official notice targeting board for certified emergency physicians and residents working at the present ED. Of the eight ED physicians who volunteered to participate in the study, seven ED physicians were included, while one participant declared withdrawal during performance assessment. Seven physicians' CR interpretations and clinical decision-making were assessed based on the clinical data from 388 patients, including detecting the target lesion with DLCR. Participant performance was evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy analyses; decision-making consistency was measured by kappa statistics. ED physicians with < 24 months of experience were defined as 'inexperienced'. RESULTS Among the 388 simulated cases, 259 (66.8%) had CR abnormality. Their median value of abnormality score measured by DLCR was 59.3 (31.77, 76.25) compared to a score of 3.35 (1.57, 8.89) for cases of normal CR. There was a difference in performance between ED physicians working with and without DLCR (AUROC: 0.801, P < 0.001). The diagnostic sensitivity and accuracy of CR were higher for all ED physicians working with DLCR than for those working without it. The overall kappa value for decision-making consistency was 0.902 (95% confidence interval [CI] 0.884-0.920); concurrently, the kappa value for the experienced group was 0.956 (95% CI 0.934-0.979), and that for the inexperienced group was 0.862 (95% CI 0.835-0.889). CONCLUSIONS This study presents preliminary evidence that ED physicians using DLCR in a clinical setting perform better at CR interpretation than their counterparts who do not use this technology. DLCR use influenced the clinical decision-making of inexperienced physicians more strongly than that of experienced physicians. These findings require prospective validation before DLCR can be recommended for use in routine clinical practice.
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Affiliation(s)
- Ji Hoon Kim
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.,Department of Preventive Medicine , Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Sang Gil Han
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Ara Cho
- Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Hye Jung Shin
- Department of Research Affairs, Biostatistics Collaboration Unit, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Song-Ee Baek
- Department of Radiology, Division of Emergency Radiology, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
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Simkus P, Gutierrez Gimeno M, Banisauskaite A, Noreikaite J, McCreavy D, Penha D, Arzanauskaite M. Limitations of cardiothoracic ratio derived from chest radiographs to predict real heart size: comparison with magnetic resonance imaging. Insights Imaging 2021; 12:158. [PMID: 34731329 PMCID: PMC8566609 DOI: 10.1186/s13244-021-01097-0] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 09/23/2021] [Indexed: 12/29/2022] Open
Abstract
Background Cardiothoracic ratio (CTR) in chest radiographs is still widely used to estimate cardiac size despite the advent of newer imaging techniques. We hypothesise that a universal CTR cut-off value of 50% is a poor indicator of cardiac enlargement. Our aim was to compare CTR with volumetric and functional parameters derived from cardiac magnetic resonance imaging (MRI). Methods 309 patients with a chest radiograph and cardiac MRI acquired within a month were reviewed to assess how CTR correlates with multiple cardiac MRI variables: bi-ventricular EDV (absolute and indexed to body surface area), EF, indexed total heart volume and bi-atrial areas. In addition, we have also determined CTR accuracy by creating multiple ROC curves with the described variables. Results All cardiac MRI variables correlate weakly but statistically significantly with CTR. This weak correlation is explained by a substantial overlap of cardiac MRI parameters in patients with normal and increased CTR. For all variables, CTR was only mildly to moderately better than a chance to discriminate cardiac enlargement (AUC 0.6–0.7). Large CTR values (> 55%) are specific but not sensitive, while low CTR values (< 45%) are sensitive but not specific. Values in between are not sensitive nor specific. Conclusions CTR correlates weakly with true chamber size assessed by gold standard cardiac MRI and has a weak discriminatory power. Thus, clinical decisions based on intermediate CTRs (45–55%) should be avoided. Large CTRs (> 55%) are likely indicative of true heart chamber enlargement. Low CTRs (< 45%) are likely indicative of normal heart size. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01097-0.
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Affiliation(s)
- Paulius Simkus
- Radiology Department, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, L14 3PE, UK
| | - Manuel Gutierrez Gimeno
- Radiology Department, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, L14 3PE, UK
| | - Audra Banisauskaite
- Radiology Department, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, L14 3PE, UK.,Department of Radiology, Lithuanian University of Health Sciences, Eiveniu 2, 50161, Kaunas, Lithuania
| | - Jurate Noreikaite
- Radiology Department, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, L14 3PE, UK
| | - David McCreavy
- Radiology Department, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, L14 3PE, UK
| | - Diana Penha
- Radiology Department, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, L14 3PE, UK
| | - Monika Arzanauskaite
- Radiology Department, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, L14 3PE, UK. .,Cardiovascular Research Center-ICCC, Hospital de La Santa Creu I Sant Pau, IIB-Sant Pau, Barcelona, Spain.
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Kuzniewski CT, Kizhner O, Donnelly EF, Henry TS, Amin AN, Kandathil A, Kelly AM, Laroia AT, Lee E, Martin MD, Morris MF, Raptis CA, Sirajuddin A, Wu CC, Kanne JP. ACR Appropriateness Criteria® Chronic Cough. J Am Coll Radiol 2021; 18:S305-S319. [PMID: 34794590 DOI: 10.1016/j.jacr.2021.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 08/26/2021] [Indexed: 11/20/2022]
Abstract
Chronic cough is defined by a duration lasting at least 8 weeks. The most common causes of chronic cough include smoking-related lung disease, upper airway cough syndrome, asthma, gastroesophageal reflux disease, and nonasthmatic eosinophilic bronchitis. The etiology of chronic cough in some patients may be difficult to localize to an isolated source and is often multifactorial. The complex pathophysiology, clinical presentation, and variable manifestations of chronic cough underscore the challenges faced by clinicians in the evaluation and management of these patients. Imaging plays a role in the initial evaluation, although there is a lack of high-quality evidence guiding which modalities are useful and at what point in time the clinical evaluation should be performed. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Affiliation(s)
| | | | - Edwin F Donnelly
- Panel Chair and Chief, Thoracic Imaging, The Ohio State University Wexner Medical Center, Columbus, Ohio; and Co-Chair, Physics Module Committee, RSBA
| | - Travis S Henry
- Panel Vice-Chair, University of California San Francisco, San Francisco, California; Course Co-Director, HRCT Course, ACR Education Center, Reston Virginia; and Division Chief, Cardiothoracic Radiology, Duke University Hospital
| | - Alpesh N Amin
- University of California Irvine, Irvine, California; American College of Physicians
| | | | | | | | - Elizabeth Lee
- University of Michigan Health System, Ann Arbor, Michigan
| | - Maria D Martin
- Director of Diversity and Inclusion, Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | | | | | | | - Carol C Wu
- Deputy Chair Ad Interim, The University of Texas MD Anderson Cancer Center, Houston, Texas; Chair, Society of Thoracic Radiology Big Data Committee; and Chair, Thoracic Use Cases Panel - ACR DSI
| | - Jeffrey P Kanne
- Specialty Chair, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
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31
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Xin KZ, Li D, Yi PH. Limited generalizability of deep learning algorithm for pediatric pneumonia classification on external data. Emerg Radiol 2021; 29:107-113. [PMID: 34648114 PMCID: PMC8515154 DOI: 10.1007/s10140-021-01954-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 03/25/2021] [Accepted: 06/08/2021] [Indexed: 11/06/2022]
Abstract
Purpose (1) Develop a deep learning system (DLS) to identify pneumonia in pediatric chest radiographs, and (2) evaluate its generalizability by comparing its performance on internal versus external test datasets. Methods Radiographs of patients between 1 and 5 years old from the Guangzhou Women and Children’s Medical Center (Guangzhou dataset) and NIH ChestXray14 dataset were included. We utilized 5232 radiographs from the Guangzhou dataset to train a ResNet-50 deep convolutional neural network (DCNN) to identify pediatric pneumonia. DCNN testing was performed on a holdout set of 624 radiographs from the Guangzhou dataset (internal test set) and 383 radiographs from the NIH ChestXray14 dataset (external test set). Receiver operating characteristic curves were generated, and area under the curve (AUC) was compared via DeLong parametric method. Colored heatmaps were generated using class activation mapping (CAM) to identify important image pixels for DCNN decision-making. Results The DCNN achieved AUC of 0.95 and 0.54 for identifying pneumonia on internal and external test sets, respectively (p < 0.0001). Heatmaps generated by the DCNN showed the algorithm focused on clinically relevant features for images from the internal test set, but not for images from the external test set. Conclusion Our model had high performance when tested on an internal dataset but significantly lower accuracy when tested on an external dataset. Likewise, marked differences existed in the clinical relevance of features highlighted by heatmaps generated from internal versus external datasets. This study underscores potential limitations in the generalizability of such DLS models.
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Affiliation(s)
- Kevin Z Xin
- Transitional Year Program, Mount Carmel Health System, Grove City, OH, USA
| | - David Li
- University of Ottawa Faculty of Medicine, Ottawa, ON, Canada.,University of Maryland Intelligent Imaging (UMII) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Paul H Yi
- University of Maryland Intelligent Imaging (UMII) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA. .,Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
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32
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Brakohiapa EK, Botwe BO, Sarkodie BD. Gender and Age Differences in Cardiac Size Parameters of Ghanaian Adults: Can One Parameter Fit All? Part Two. Ethiop J Health Sci 2021; 31:561-572. [PMID: 34483613 PMCID: PMC8365486 DOI: 10.4314/ejhs.v31i3.13] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 09/09/2020] [Indexed: 11/22/2022] Open
Abstract
Background The cardiothoracic ratio (CTR) is a radiographic parameter commonly used in assessing the size of the heart. This study evaluated the gender and age-based differences in the average cardiothoracic ratios, and transverse cardiac diameters (TCD) of adults in Ghana. Method Plain chest radiography reports of 2004 patients (without known chest related diseases) generated by two radiologists with at least 15 years' experience from July 2016 to June 2020 were retrospectively analyzed for this study. The CTR for each radiograph was calculated using the formula CTR=(TCD÷TTD)×100, where TCD and TTD represent transverse cardiac diameters and transverse thoracic diameters, respectively. Data were analyzed with the statistical package for social sciences version 23. The independent t-test and One-way Analysis of Variance tests were used in the analyses. Results A total of 2004 patients' chest x-rays were used in the analyses. The ages of the patients ranged from 20–86 years old with a mean of 39.4±14.04 years. The mean CTR for males was 46.6 ± 3.7% while that of females was 47.7±3.7%. The difference in the overall CTR among the gender groupings was statistically significant (p = 0.001). There were statistically significant differences between the gender categories among patients in the following age groups: 30–39 (p=0.046), 40–49 (p=0.001), 50–59 (p=0.001) and 60–69 (p=0.001). Conclusion The study reveals there are significant gender and age-related differences in cardiac size parameters obtained from routine, frontal chest radiographs. These differences, if considered, may result in early and appropriate treatment of cardiac pathology in some age groups.
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Affiliation(s)
| | - Benard Ohene Botwe
- Department of Radiography, University of Ghana School of Biomedical &Allied Health Sciences
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Abstract
BACKGROUND AND OBJECTIVE A routine chest radiograph is mandatory in many institutions as a part of pre-employment screening. The usefulness of this has been studied over the years keeping in mind the added time, cost, and radiation concerns. Studies conducted outside India have shown different results, some for and some against it. To our knowledge, there is no published data from India on this issue. MATERIALS AND METHODS A retrospective review of the reports of 4113 pre-employment chest radiographs done between 2007 and 2009 was conducted. RESULTS Out of 4113 radiographs, 24 (0.58%) candidates required further evaluation based on findings from the screening chest radiograph. Out of these, 7 (0.17%) candidates required appropriate further treatment. INTERPRETATION AND CONCLUSIONS The percentage of significant abnormalities detected which needed further medical intervention was small (0.17%). Although the individual radiation exposure is very small, the large numbers done nation-wide would significantly add to the community radiation, with added significant cost and time implications. We believe that pre-employment chest radiographs should be restricted to candidates in whom there is relevant history and/or clinical findings suggestive of cardiopulmonary disease.
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Affiliation(s)
- V John Samuel
- Radiologist, Christian Fellowship Hospital, Oddanchatram, Tamil Nadu, India
| | - Sridhar Gibikote
- Department of Radiology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Henry Kirupakaran
- Department of Staff Health, Christian Medical College, Vellore, Tamil Nadu, India
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Das KM, Lee EY, Singh R, Enani MA, Al Dossari K, Van Gorkom K, Larsson SG, Langer RD. Follow-up chest radiographic findings in patients with MERS-CoV after recovery. Indian J Radiol Imaging 2021; 27:342-349. [PMID: 29089687 PMCID: PMC5644332 DOI: 10.4103/ijri.ijri_469_16] [Citation(s) in RCA: 233] [Impact Index Per Article: 77.7] [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] [Indexed: 12/18/2022] Open
Abstract
PURPOSE To evaluate the follow-up chest radiographic findings in patients with Middle East respiratory syndrome coronavirus (MERS-CoV) who were discharged from the hospital following improved clinical symptoms. MATERIALS AND METHODS Thirty-six consecutive patients (9 men, 27 women; age range 21-73 years, mean ± SD 42.5 ± 14.5 years) with confirmed MERS-CoV underwent follow-up chest radiographs after recovery from MERS-CoV. The 36 chest radiographs were obtained at 32 to 230 days with a median follow-up of 43 days. The reviewers systemically evaluated the follow-up chest radiographs from 36 patients for lung parenchymal, airway, pleural, hilar and mediastinal abnormalities. Lung parenchyma and airways were assessed for consolidation, ground-glass opacity (GGO), nodular opacity and reticular opacity (i.e., fibrosis). Follow-up chest radiographs were also evaluated for pleural thickening, pleural effusion, pneumothorax and lymphadenopathy. Patients were categorized into two groups: group 1 (no evidence of lung fibrosis) and group 2 (chest radiographic evidence of lung fibrosis) for comparative analysis. Patient demographics, length of ventilations days, number of intensive care unit (ICU) admission days, chest radiographic score, chest radiographic deterioration pattern (Types 1-4) and peak lactate dehydrogenase level were compared between the two groups using the student t-test, Mann-Whitney U test and Fisher's exact test. RESULTS Follow-up chest radiographs were normal in 23 out of 36 (64%) patients. Among the patients with abnormal chest radiographs (13/36, 36%), the following were found: lung fibrosis in 12 (33%) patients GGO in 2 (5.5%) patients, and pleural thickening in 2 (5.5%) patients. Patients with lung fibrosis had significantly greater number of ICU admission days (19 ± 8.7 days; P value = 0.001), older age (50.6 ± 12.6 years; P value = 0.02), higher chest radiographic scores [10 (0-15.3); P value = 0.04] and higher peak lactate dehydrogenase levels (315-370 U/L; P value = 0.001) when compared to patients without lung fibrosis. CONCLUSION Lung fibrosis may develop in a substantial number of patients who have recovered from Middle East respiratory syndrome coronavirus (MERS-CoV). Significantly greater number of ICU admission days, older age, higher chest radiographic scores, chest radiographic deterioration patterns and peak lactate dehydrogenase levels were noted in the patients with lung fibrosis on follow-up chest radiographs after recovery from MERS-CoV.
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Affiliation(s)
- Karuna M Das
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, UAE.,Department of Medical Imaging, King Fahad Medical City, Riyadh, KSA
| | - Edward Y Lee
- Department of Radiology and Medicine, Pulmonary Division, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Rajvir Singh
- Department of Cardiology and Biostatistics, Hamad Medical Corporation, Doha, Qatar
| | - Mushira A Enani
- Department of Medicine (Infectious Disease), King Fahad Medical City, Riyadh, KSA
| | | | - Klaus Van Gorkom
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, UAE
| | - Sven G Larsson
- Department of Medical Imaging, King Fahad Medical City, Riyadh, KSA
| | - Ruth D Langer
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, UAE
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35
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Sadiq Z, Rana S, Mahfoud Z, Raoof A. Systematic review and meta-analysis of chest radiograph (CXR) findings in COVID-19. Clin Imaging 2021; 80:229-238. [PMID: 34364071 PMCID: PMC8313779 DOI: 10.1016/j.clinimag.2021.06.039] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [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: 12/28/2020] [Revised: 06/24/2021] [Accepted: 06/30/2021] [Indexed: 01/08/2023]
Abstract
Chest radiography (CXR) is most likely to be the utilized modality for diagnosing COVID-19 and following up on any lung-associated abnormalities. This review provides a meta-analysis of the current literature on CXR imaging findings to determine the most common appearances of lung abnormalities in COVID-19 patients in order to equip medical researchers and healthcare professionals in their efforts to combat this pandemic. Twelve studies met the inclusion criteria and were analyzed. The inclusion criteria consisted of: (1) published in English literature; (2) original research study; (3) sample size of at least 5 patients; (4) reporting clinical characteristics of COVID-19 patients as well as CXR imaging features; and (5) noting the number of patients with each corresponding imaging feature. A total of 1948 patients were included in this study. To perform the meta-analysis, a random-effects model calculated the pooled prevalence and 95% confidence intervals of abnormal CXR imaging findings. Seventy-four percent (74%) (95% CI: 51–92%) of patients with COVID-19 had an abnormal CXR at the initial time of diagnosis or sometime during the disease course. While there was no single feature on CXR that was diagnostic of COVID-19 viral pneumonia, a characteristic set of findings were obvious. The most common abnormalities were consolidation (28%, 95% CI: 8–54%) and ground-glass opacities (29%, 95% CI: 10–53%). The distribution was most frequently bilateral (43%, 95% CI: 27–60%), peripheral (51%, 95% CI: 36–66%), and basal zone (56%, 95% CI: 37–74%) predominant. Contrary to parenchymal abnormalities, pneumothorax (1%, 95% CI: 0–3%) and pleural effusions (6%, 95% CI: 1–16%) were rare.
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Affiliation(s)
- Zuhair Sadiq
- Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, Qatar.
| | - Shehroz Rana
- Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Ziyad Mahfoud
- Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Ameed Raoof
- Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, Qatar
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Muien MZA, Jeyaprahasam K, Krisnan T, Ng CY, Teh YG. Rare late-presentation congenital diaphragmatic hernia mimicking a tension pneumothorax. Radiol Case Rep 2021; 16:2542-5. [PMID: 34276850 DOI: 10.1016/j.radcr.2021.06.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 06/08/2021] [Accepted: 06/08/2021] [Indexed: 11/20/2022] Open
Abstract
Congenital Diaphragmatic Hernia (CDH) is due to a defect in the diaphragm and is usually detected soon after birth. However, in rare cases, asymptomatic CDHs can be missed and present later in life. Late-presentation CDH can be misdiagnosed as tension pneumothorax leading to iatrogenic complications. We report a case of a 10-year-old boy who presented with non-specific symptoms of vomiting and occasional breathlessness, but was subsequently diagnosed as late-presentation CDH. This case highlights the role of imaging in the diagnosis and management of late-presenting CDH. The role of CT imaging as an invaluable tool to further evaluate equivocal radiographic findings in CDH is discussed.
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Jeong HG, Kim BJ, Kim T, Kang J, Kim JY, Kim J, Kim JT, Park JM, Kim JG, Hong JH, Lee KB, Park TH, Kim DH, Oh CW, Han MK, Bae HJ. Classification of cardioembolic stroke based on a deep neural network using chest radiographs. EBioMedicine 2021; 69:103466. [PMID: 34229276 PMCID: PMC8264106 DOI: 10.1016/j.ebiom.2021.103466] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/11/2021] [Accepted: 06/11/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Although chest radiographs have not been utilised well for classifying stroke subtypes, they could provide a plethora of information on cardioembolic stroke. This study aimed to develop a deep convolutional neural network that could diagnose cardioembolic stroke based on chest radiographs. METHODS Overall, 4,064 chest radiographs of consecutive patients with acute ischaemic stroke were collected from a prospectively maintained stroke registry. Chest radiographs were randomly partitioned into training/validation (n = 3,255) and internal test (n = 809) datasets in an 8:2 ratio. A densely connected convolutional network (ASTRO-X) was trained to diagnose cardioembolic stroke based on chest radiographs. The performance of ASTRO-X was evaluated using the area under the receiver operating characteristic curve. Gradient-weighted class activation mapping was used to evaluate the region of focus of ASTRO-X. External testing was performed with 750 chest radiographs of patients with acute ischaemic stroke from 7 hospitals. FINDINGS The areas under the receiver operating characteristic curve of ASTRO-X were 0.86 (95% confidence interval [CI], 0.83-0.89) and 0.82 (95% CI, 0.79-0.85) during the internal and multicentre external testing, respectively. The gradient-weighted class activation map demonstrated that ASTRO-X was focused on the area where the left atrium was located. Compared with cases predicted as non-cardioembolism by ASTRO-X, cases predicted as cardioembolism by ASTRO-X had higher left atrial volume index and lower left ventricular ejection fraction in echocardiography. INTERPRETATION ASTRO-X, a deep neural network developed to diagnose cardioembolic stroke based on chest radiographs, demonstrated good classification performance and biological plausibility. FUNDING Grant No. 14-2020-046 and 08-2016-051 from the Seoul National University Bundang Research Fund and NRF-2020M3E5D9079768 from the National Research Foundation of Korea.
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Affiliation(s)
- Han-Gil Jeong
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Beom Joon Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Tackeun Kim
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam, Korea.
| | - Jihoon Kang
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jun Yup Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Joonghee Kim
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Joon-Tae Kim
- Department of Neurology, Chonnam National University Hospital, Gwangju, Korea
| | - Jong-Moo Park
- Department of Neurology, Nowon Eulji Medical Center, Eulji University, Seoul, Korea
| | - Jae Guk Kim
- Department of Neurology, Eulji University Hospital, Daejeon, Korea
| | - Jeong-Ho Hong
- Department of Neurology, Keimyung University Dongsan Medical Center, Daegu, Korea
| | - Kyung Bok Lee
- Department of Neurology, Soonchunhyang University Hospital, Seoul, Korea
| | - Tai Hwan Park
- Department of Neurology, Seoul Medical Center, Korea
| | - Dae-Hyun Kim
- Department of Neurology, Dong-A University Hospital, Busan, Korea
| | - Chang Wan Oh
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Moon-Ku Han
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Korea
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Mogami H, Onoike Y, Miyano H, Arakawa K, Inoue H, Sakae K, Kawakami T. Lung cancer screening by single-shot dual-energy subtraction using flat-panel detector. Jpn J Radiol 2021; 39:1168-1173. [PMID: 34173973 PMCID: PMC8639557 DOI: 10.1007/s11604-021-01163-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 01/14/2021] [Accepted: 06/20/2021] [Indexed: 11/28/2022]
Abstract
Purpose The purpose of this study was to evaluate the usefulness of single-shot dual-energy subtraction (DES) method using a flat-panel detector for lung cancer screening Materials and methods The subjects were 13,315 residents (5801 males and 7514 females) aged 50 years or older (50–97 years, with an intermediate value of 68 years) who underwent lung cancer screening for a period of 1 year and 6 months from January 2019 to June 2020. We investigated whether the number of lung cancers detected, the detection rate, and the rate of required scrutiny changed, when DES images were added to the judgment based on conventional chest radiography. Results When DES images were added, the number and percentage of cancer detection increased from 16 (0.12%) to 23 (0.17%) (P < 0.05). Five of the newly detected 7 lung cancers were in the early stages of resectable cancer. The rate of participants requiring scrutiny increased slightly from 1.1 to 1.3%. Conclusion DES method improved the detection of lung cancer in screening. The increase in the percentage of participants requiring scrutiny was negligible.
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Affiliation(s)
- Hiroshi Mogami
- Ehime General Healthcare Association, 1-10-5, Misake-cho, Matsuyama, Ehime, 790-0814, Japan.
| | - Yumiko Onoike
- Ehime General Healthcare Association, 1-10-5, Misake-cho, Matsuyama, Ehime, 790-0814, Japan
| | - Hiroshi Miyano
- Ehime General Healthcare Association, 1-10-5, Misake-cho, Matsuyama, Ehime, 790-0814, Japan
| | - Kenji Arakawa
- Ehime General Healthcare Association, 1-10-5, Misake-cho, Matsuyama, Ehime, 790-0814, Japan
| | - Hiromi Inoue
- Ehime General Healthcare Association, 1-10-5, Misake-cho, Matsuyama, Ehime, 790-0814, Japan
| | - Kouji Sakae
- Ehime General Healthcare Association, 1-10-5, Misake-cho, Matsuyama, Ehime, 790-0814, Japan
| | - Toshiaki Kawakami
- Ehime General Healthcare Association, 1-10-5, Misake-cho, Matsuyama, Ehime, 790-0814, Japan
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Yi PH, Wei J, Kim TK, Shin J, Sair HI, Hui FK, Hager GD, Lin CT. Radiology "forensics": determination of age and sex from chest radiographs using deep learning. Emerg Radiol 2021; 28:949-54. [PMID: 34089126 DOI: 10.1007/s10140-021-01953-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 05/24/2021] [Indexed: 01/23/2023]
Abstract
PURPOSE To develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR). METHODS We obtained 112,120 frontal CXRs from the NIH ChestX-ray14 database performed in 48,780 females (44%) and 63,340 males (56%) ranging from 1 to 95 years old. The dataset was split into training (70%), validation (10%), and test (20%) datasets, and used to fine-tune ResNet-18 DCNNs pretrained on ImageNet for (1) determination of sex (using entire dataset and only pediatric CXRs); (2) determination of age < 18 years old or ≥ 18 years old (using entire dataset); and (3) determination of age < 11 years old or 11-18 years old (using only pediatric CXRs). External testing was performed on 662 CXRs from China. Area under the receiver operating characteristic curve (AUC) was used to evaluate DCNN test performance. RESULTS DCNNs trained to determine sex on the entire dataset and pediatric CXRs only had AUCs of 1.0 and 0.91, respectively (p < 0.0001). DCNNs trained to determine age < or ≥ 18 years old and < 11 vs. 11-18 years old had AUCs of 0.99 and 0.96 (p < 0.0001), respectively. External testing showed AUC of 0.98 for sex (p = 0.01) and 0.91 for determining age < or ≥ 18 years old (p < 0.001). CONCLUSION DCNNs can accurately predict sex from CXRs and distinguish between adult and pediatric patients in both American and Chinese populations. The ability to glean demographic information from CXRs may aid forensic investigations, as well as help identify novel anatomic landmarks for sex and age.
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Çallı E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K. Deep learning for chest X-ray analysis: A survey. Med Image Anal 2021; 72:102125. [PMID: 34171622 DOI: 10.1016/j.media.2021.102125] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [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: 03/16/2021] [Revised: 05/17/2021] [Accepted: 05/27/2021] [Indexed: 12/14/2022]
Abstract
Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs published before March 2021, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Detailed descriptions of all publicly available datasets are included and commercial systems in the field are described. A comprehensive discussion of the current state of the art is provided, including caveats on the use of public datasets, the requirements of clinically useful systems and gaps in the current literature.
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Affiliation(s)
- Erdi Çallı
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands.
| | - Ecem Sogancioglu
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Bram van Ginneken
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Kicky G van Leeuwen
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Keelin Murphy
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
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Egglestone A, Dietz-Collin G, Eardley W, Baker P. Chin-on-Chest in Neck of Femur Fracture (COCNOF) sign: A simple radiographic predictor of frailty and mortality in hip fracture patients. Injury 2021; 52:1494-1499. [PMID: 33143868 DOI: 10.1016/j.injury.2020.10.098] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 10/21/2020] [Accepted: 10/26/2020] [Indexed: 02/02/2023]
Abstract
INTRODUCTION Fragility fractures are a significant public health challenge often occurring as a result of frailty. Identifying patients who have increased risk of adverse outcomes can aid treating teams in managing these patients appropriately. We hypothesise that the appearance of the patient's head overlapping the lung fields (named Chin on Chest in Neck of Femur sign (COCNOF)) in the admission chest radiograph was a predictor of increased mortality at 3, 6 and 12 months. METHODS All consecutive patients admitted with hip fracture between 1st January - 31st December 2019 were analysed. We collected patient characteristics, AMTS score, ASA grade, length of stay, place of discharge, Nottingham Hip Fracture Score, Rockwood Frailty score, Charlson Comorbidity Index and presence of COCNOF sign. The main outcome measures were mortality at 90 days, six months and 12 months following admission. RESULTS 469 patients with a mean age of 81.9 (SD 8.4) were included. 18% of patients were COCNOF positive. Univariate analysis showed positive COCNOF sign to be associated with higher mortality at 90 days (19.1 vs 10.8%; RR 1.95, 95%CI 1.05 - 3.63,p=0.03), six months (31.5% vs 14.2%; RR 2.77, 95%CI 1.62 - 4.72, p<0.001) and twelve months (41.6% vs 17.1%; RR 3.45, 95%CI 1.62-4.72, p<0.001). In the multivariate regression models the strongest predictors of mortality were age, gender and CCI it is therefore likely that the COCNOF sign is acting as a surrogate marker of these variables within the univariate models. CONCLUSION Our results suggest that COCNOF sign is a simple radiographic marker which can be used to identify patients with higher levels of frailty and increased risk of mortality following hip fracture.
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Affiliation(s)
- Anthony Egglestone
- Trauma and Orthopaedics registrar, James Cook University Hospital, United Kingdom.
| | - Gemma Dietz-Collin
- Hip Specialist Nurse practitioner, James Cook University Hospital, United Kingdom
| | - Will Eardley
- Consultant Trauma and Orthopaedic Surgeon, James Cook University Hospital, United Kingdom
| | - Paul Baker
- Consultant Trauma and Orthopaedic Surgeon, James Cook University Hospital, United Kingdom
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Govindarajan S, Swaminathan R. Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs. Stud Health Technol Inform 2021; 281:510-1. [PMID: 34042625 DOI: 10.3233/SHTI210219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image analysis using artificial intelligence is crucial to detect subtle and non-specific alterations of Tuberculosis (TB). For this, the healthy and TB CRs are subjected to lung field segmentation. SURF and SIFT keypoints are extracted from the segmented lung images. Statistical features from keypoints, its scale and orientation are computed. Discrimination of TB from healthy is performed using SVM. Results show that the SURF and SIFT methods are able to extract local keypoint information in CRs. Linear SVM is found to perform better with precision of 88.9% and AUC of 91% in TB detection for combined features. Hence, the application of keypoint techniques is found to have clinical relevance in the automated screening of non-specific TB abnormalities using CRs.
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Wallis TJM, Heiden E, Horno J, Welham B, Burke H, Freeman A, Dexter L, Fazleen A, Kong A, McQuitty C, Watson M, Poole S, Brendish NJ, Clark TW, Wilkinson TMA, Jones MG, Marshall BG. Risk factors for persistent abnormality on chest radiographs at 12-weeks post hospitalisation with PCR confirmed COVID-19. Respir Res 2021; 22:157. [PMID: 34020644 PMCID: PMC8139368 DOI: 10.1186/s12931-021-01750-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [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: 01/04/2021] [Accepted: 05/17/2021] [Indexed: 02/06/2023] Open
Abstract
Background The long-term consequences of COVID-19 remain unclear. There is concern a proportion of patients will progress to develop pulmonary fibrosis. We aimed to assess the temporal change in CXR infiltrates in a cohort of patients following hospitalisation for COVID-19.
Methods We conducted a single-centre prospective cohort study of patients admitted to University Hospital Southampton with confirmed SARS-CoV2 infection between 20th March and 3rd June 2020. Patients were approached for standard-of-care follow-up 12-weeks after hospitalisation. Inpatient and follow-up CXRs were scored by the assessing clinician for extent of pulmonary infiltrates; 0–4 per lung (Nil = 0, < 25% = 1, 25–50% = 2, 51–75% = 3, > 75% = 4).
Results 101 patients with paired CXRs were included. Demographics: 53% male with a median (IQR) age 53.0 (45–63) years and length of stay 9 (5–17.5) days. The median CXR follow-up interval was 82 (77–86) days with median baseline and follow-up CXR scores of 4.0 (3–5) and 0.0 (0–1) respectively. 32% of patients had persistent CXR abnormality at 12-weeks. In multivariate analysis length of stay (LOS), smoking-status and obesity were identified as independent risk factors for persistent CXR abnormality. Serum LDH was significantly higher at baseline and at follow-up in patients with CXR abnormalities compared to those with resolution. A 5-point composite risk score (1-point each; LOS ≥ 15 days, Level 2/3 admission, LDH > 750 U/L, obesity and smoking-status) strongly predicted risk of persistent radiograph abnormality (0.81). Conclusion Persistent CXR abnormality 12-weeks post COVID-19 was common in this cohort. LOS, obesity, increased serum LDH, and smoking-status were risk factors for radiograph abnormality. These findings require further prospective validation. Supplementary Information The online version contains supplementary material available at 10.1186/s12931-021-01750-8.
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Affiliation(s)
- T J M Wallis
- Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK. .,NIHR Southampton Biomedical Research Centre Research Fellow, University of Southampton, MP218 D-Level South Academic Block University Hospital Southampton, Southampton, SO16 6YD, UK.
| | - E Heiden
- Department of Respiratory Medicine, University Hospital Southampton, Southampton, UK
| | - J Horno
- Department of Respiratory Medicine, University Hospital Southampton, Southampton, UK
| | - B Welham
- Department of Respiratory Medicine, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - H Burke
- Department of Respiratory Medicine, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - A Freeman
- Department of Respiratory Medicine, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - L Dexter
- Department of Respiratory Medicine, University Hospital Southampton, Southampton, UK
| | - A Fazleen
- Department of Respiratory Medicine, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - A Kong
- Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - C McQuitty
- Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - M Watson
- Department of Respiratory Medicine, University Hospital Southampton, Southampton, UK
| | - S Poole
- Department of Infection and Southampton NIHR Biomedical Research Centre, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - N J Brendish
- Department of Infection, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - T W Clark
- Department of Infection and Southampton NIHR Biomedical Research Centre, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - T M A Wilkinson
- Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - M G Jones
- Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - B G Marshall
- Department of Respiratory Medicine and Southampton NIHR Biomedical Research Centre, University Hospital Southampton and School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
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Moroni C, Cozzi D, Albanesi M, Cavigli E, Bindi A, Luvarà S, Busoni S, Mazzoni LN, Grifoni S, Nazerian P, Miele V. Chest X-ray in the emergency department during COVID-19 pandemic descending phase in Italy: correlation with patients' outcome. Radiol Med 2021; 126:661-668. [PMID: 33394364 PMCID: PMC7780606 DOI: 10.1007/s11547-020-01327-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 12/14/2020] [Indexed: 01/16/2023]
Abstract
PURPOSE The aims of our study are: (1) to estimate admission chest X-ray (CXR) accuracy during the descending phase of pandemic; (2) to identify specific CXR findings strictly associated with COVID-19 infection; and (3) to correlate lung involvement of admission CXR with patients' outcome. MATERIALS AND METHODS We prospectively evaluated the admission CXR of 327 patients accessed to our institute during the Italian pandemic descending phase (April 2020). For each CXR were searched ground glass opacification (GGO), consolidation (CO), reticular-nodular opacities (RNO), nodules, excavations, pneumothorax, pleural effusion, vascular congestion and cardiac enlargement. For lung alterations was defined the predominance (upper or basal, focal or diffuse, central or peripheric, etc.). Then radiologists assessed whether CXRs were suggestive or not for COVID-19 infection. For COVID-19 patients, a prognostic score was applied and correlated with the patients' outcome. RESULTS CXR showed 83% of specificity and 60% of sensitivity. GGO, CO, RNO and a peripheric, diffuse and basal prevalence showed good correlation with COVID-19 diagnosis. A logistic regression analysis pointed out GGO and a basal or diffuse distribution as independent predictors of COVID-19 diagnosis. The prognostic score showed good correlation with the patients' outcome. CONCLUSION In our study, admission CXR showed a fair specificity and a good correlation with patients' outcome. GGO and others CXR findings showed a good correlation with COVID-19 diagnosis; besides GGO a diffuse or bibasal distribution resulted in independent variables highly suggestive for COVID-19 infection thus enabling radiologists to signal to clinicians radiologically suspect patients during the pandemic descending phase.
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Affiliation(s)
- Chiara Moroni
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
| | - Marco Albanesi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- Department of Clinical and Experimental Medicine, Institute of Diagnostic Imaging 2, University of Sassari, Sassari, Italy
| | - Edoardo Cavigli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Alessandra Bindi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Luvarà
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Simone Busoni
- Medical Physics Department, Careggi University Hospital, Florence, Italy
| | - Lorenzo Nicola Mazzoni
- Medical Physics Department, Careggi University Hospital, Florence, Italy
- Medical Physics Unit, AUSL Toscana Centro, Pistoia-Prato, Italy
| | - Stefano Grifoni
- Department of Emergency Medicine, Careggi University Hospital, Florence, Italy
| | - Peiman Nazerian
- Department of Emergency Medicine, Careggi University Hospital, Florence, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
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Liu TY, Rai A, Ditkofsky N, Deva DP, Dowdell TR, Ackery AD, Mathur S. Cost benefit analysis of portable chest radiography through glass: Initial experience at a tertiary care centre during COVID-19 pandemic. J Med Imaging Radiat Sci 2021; 52:186-190. [PMID: 33875400 PMCID: PMC8026266 DOI: 10.1016/j.jmir.2021.03.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/12/2021] [Accepted: 03/29/2021] [Indexed: 01/08/2023]
Abstract
Introduction Portable chest radiography through glass (TG-CXR) is a novel technique, particularly useful during the COVID-19 (Coronavirus disease 2019) pandemic. The purpose of this study was to understand the cost and benefit of adopting TG-CXR in quantifiable terms. Methods Portable or bedside radiographs are typically performed by a team of two technologists. The TG-CXR method has the benefit of allowing one technologist to stay outside of the patient room while operating the portable radiography machine, reducing PPE use, decreasing the frequency of radiography machine sanitization and decreasing technologists’ exposures to potentially infectious patients. The cost of implementing this technique during the current COVID-19 pandemic was obtained from our department's operational database. The direct cost of routinely used PPE and sanitization materials and the cost of the time taken by the technologists to clean the machine was used to form a quantitative picture of the benefit associated with TG-CXR technique. Results Technologists were trained on the TG-CXR method during a 15 min shift change briefing. This translated to a one-time cost of $424.88 USD. There was an average reduction of portable radiography machine downtime of 4 min and 48 s per study. The benefit of adopting the TG-CXR technique was $9.87 USD per patient imaged. This will result in a projected net cost savings of $51,451.84 USD per annum. Conclusion Adoption of the TG-CXR technique during the COVID-19 pandemic involved minimal one-time cost, but is projected to result in a net-benefit of over $51,000 USD per annum in our emergency department.
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Affiliation(s)
- Tian Yang Liu
- Department of Radiology, St. Michael's Hospital, University of Toronto, 30 Bond Street, Toronto, ON, Canada. M5B 1W8.
| | - Archana Rai
- Department of Radiology, St. Michael's Hospital, University of Toronto, 30 Bond Street, Toronto, ON, Canada. M5B 1W8
| | - Noah Ditkofsky
- Department of Radiology, St. Michael's Hospital, University of Toronto, 30 Bond Street, Toronto, ON, Canada. M5B 1W8
| | - Djeven P Deva
- Department of Radiology, St. Michael's Hospital, University of Toronto, 30 Bond Street, Toronto, ON, Canada. M5B 1W8; Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, 209 Victoria Street, Toronto, ON, Canada. M5B 1T8
| | - Timothy R Dowdell
- Department of Radiology, St. Michael's Hospital, University of Toronto, 30 Bond Street, Toronto, ON, Canada. M5B 1W8
| | - Alun Duncan Ackery
- Department of Emergency Medicine, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Shobhit Mathur
- Department of Radiology, St. Michael's Hospital, University of Toronto, 30 Bond Street, Toronto, ON, Canada. M5B 1W8
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Nakao T, Hanaoka S, Nomura Y, Murata M, Takenaga T, Miki S, Watadani T, Yoshikawa T, Hayashi N, Abe O. Unsupervised Deep Anomaly Detection in Chest Radiographs. J Digit Imaging 2021; 34:418-427. [PMID: 33555397 PMCID: PMC8289984 DOI: 10.1007/s10278-020-00413-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 12/04/2020] [Accepted: 12/18/2020] [Indexed: 01/07/2023] Open
Abstract
The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial network (α-GAN) framework, which is a combination of a GAN and a variational autoencoder, as a DNN model. A total of 29,684 frontal chest radiographs from the Radiological Society of North America Pneumonia Detection Challenge dataset were used for this study (16,880 male and 12,804 female patients; average age, 47.0 years). All these images were labeled as "Normal," "No Opacity/Not Normal," or "Opacity" by board-certified radiologists. About 70% (6,853/9,790) of the Normal images were randomly sampled as the training dataset, and the rest were randomly split into the validation and test datasets in a ratio of 1:2 (7,610 and 15,221). Our anomaly detection system could correctly visualize various lesions including a lung mass, cardiomegaly, pleural effusion, bilateral hilar lymphadenopathy, and even dextrocardia. Our system detected the abnormal images with an area under the receiver operating characteristic curve (AUROC) of 0.752. The AUROCs for the abnormal labels Opacity and No Opacity/Not Normal were 0.838 and 0.704, respectively. Our DNN-based unsupervised anomaly detection method could successfully detect various diseases or anomalies in chest radiographs by training with only the normal images.
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Affiliation(s)
- Takahiro Nakao
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.
| | - Shouhei Hanaoka
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Yukihiro Nomura
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Masaki Murata
- Department of Management, Japan University of Economics, 3-11-25 Gojo, Dazaifu-shi, Fukuoka, Japan
| | - Tomomi Takenaga
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Soichiro Miki
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Takeyuki Watadani
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Takeharu Yoshikawa
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Naoto Hayashi
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
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Ghosh S, Deshwal H, Saeedan MB, Khanna VK, Raoof S, Mehta AC. Imaging algorithm for COVID-19: A practical approach. Clin Imaging 2021; 72:22-30. [PMID: 33197713 PMCID: PMC7655027 DOI: 10.1016/j.clinimag.2020.11.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/30/2020] [Accepted: 11/08/2020] [Indexed: 02/07/2023]
Abstract
The global pandemic of COVID-19 pneumonia caused by the novel coronavirus (SARS-CoV-2) has strained healthcare resources across the world with emerging challenges of mass testing, resource allocation and management. While reverse transcriptase-polymerase chain reaction (RT-PCR) test is the most commonly utilized test and considered the current gold standard for diagnosis, the role of chest imaging has been highlighted by several studies demonstrating high sensitivity of computed tomography (CT). Many have suggested using CT chest as a first-line screening tool for the diagnosis of COVID-19. However, with advancement of laboratory testing and challenges in obtaining a CT scan without significant risk to healthcare providers, the role of imaging in diagnosis has been questioned. Several imaging societies have released consensus statements and guidelines on utilizing imaging resources and optimal reporting. In this review, we highlight the current evidence on various modalities in thoracic imaging for the diagnosis of COVID-19 and describe an algorithm on how to use these resources in an optimal fashion in accordance with the guidelines and statements released by major imaging societies.
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Affiliation(s)
- Subha Ghosh
- Thoracic Imaging, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - Himanshu Deshwal
- Division of Pulmonary, Critical Care and Sleep Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Mnahi Bin Saeedan
- Thoracic Imaging, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Suhail Raoof
- Division of Pulmonary and Critical Care Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Lenox Hill Hospital, New York, NY, USA
| | - Atul C Mehta
- Respiratory Institute, Cleveland Clinic, Cleveland, OH, USA
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48
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Li MD, Little BP, Alkasab TK, Mendoza DP, Succi MD, Shepard JAO, Lev MH, Kalpathy-Cramer J. Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs. Acad Radiol 2021; 28:572-576. [PMID: 33485773 PMCID: PMC7813473 DOI: 10.1016/j.acra.2021.01.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 01/25/2023]
Abstract
RATIONALE AND OBJECTIVES Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intelligence (AI) system could help improve radiologist interrater agreement. MATERIALS AND METHODS We performed a retrospective multi-radiologist user study to evaluate the impact of an AI system, the PXS score model, on the grading of categorical COVID-19 lung disease severity on 154 chest radiographs into four ordinal grades (normal/minimal, mild, moderate, and severe). Four radiologists (two thoracic and two emergency radiologists) independently interpreted 154 CXRs from 154 unique patients with COVID-19 hospitalized at a large academic center, before and after using the AI system (median washout time interval was 16 days). Three different thoracic radiologists assessed the same 154 CXRs using an updated version of the AI system trained on more imaging data. Radiologist interrater agreement was evaluated using Cohen and Fleiss kappa where appropriate. The lung disease severity categories were associated with clinical outcomes using a previously published outcomes dataset using Fisher's exact test and Chi-square test for trend. RESULTS Use of the AI system improved radiologist interrater agreement (Fleiss κ = 0.40 to 0.66, before and after use of the system). The Fleiss κ for three radiologists using the updated AI system was 0.74. Severity categories were significantly associated with subsequent intubation or death within 3 days. CONCLUSION An AI system used at the time of CXR study interpretation can improve the interrater agreement of radiologists.
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Affiliation(s)
- Matthew D Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Brent P Little
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Tarik K Alkasab
- Division of Emergency Radiology, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Dexter P Mendoza
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Marc D Succi
- Division of Emergency Radiology, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts
| | - Jo-Anne O Shepard
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Michael H Lev
- Division of Emergency Radiology, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts..
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Pan D, Pellicori P, Dobbs K, Bulemfu J, Sokoreli I, Urbinati A, Brown O, Sze S, Rigby AS, Kazmi S, Riistama JM, Cleland JGF, Clark AL. Prognostic value of the chest X-ray in patients hospitalised for heart failure. Clin Res Cardiol 2021; 110:1743-56. [PMID: 33754159 DOI: 10.1007/s00392-021-01836-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 03/03/2021] [Indexed: 11/05/2022]
Abstract
Background Patients admitted to hospital with heart failure will have had a chest X-ray (CXR), but little is known about their prognostic significance. We aimed to report the prevalence and prognostic value of the initial chest radiograph findings in patients admitted to hospital with heart failure (acute heart failure, AHF). Methods The erect CXRs of all patients admitted with AHF between October 2012 and November 2016 were reviewed for pulmonary venous congestion, Kerley B lines, pleural effusions and alveolar oedema. Film projection (whether anterior–posterior [AP] or posterior–anterior [PA]) and cardiothoracic ratio (CTR) were also recorded. Trial registration: ISRCTN96643197 Results Of 1145 patients enrolled, 975 [median (interquartile range) age 77 (68–83) years, 61% with moderate, or worse, left ventricular systolic dysfunction, and median NT-proBNP 5047 (2337–10,945) ng/l] had an adequate initial radiograph, of which 691 (71%) were AP. The median CTR was 0.57 (IQR 0.53–0.61) in PA films and 0.60 (0.55–0.64) in AP films. Pulmonary venous congestion was present in 756 (78%) of films, Kerley B lines in 688 (71%), pleural effusions in 649 (67%) and alveolar oedema in 622 (64%). A CXR score was constructed using the above features. Increasing score was associated with increasing age, urea, NT-proBNP, and decreasing systolic blood pressure, haemoglobin and albumin; and with all-cause mortality on multivariable analysis (hazard ratio 1.10, 95% confidence intervals 1.07–1.13, p < 0.001). Conclusions Radiographic evidence of congestion on a CXR is very common in patients with AHF and is associated with other clinical measures of worse prognosis. Graphic abstract Signs of heart failure are highly prevalent in patients presenting to hospital with acute heart failure and when combined into a chest x-ray score, relate to a worse long term risk of death ![]()
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50
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Jain SN, Modi T, Varma RU. Decoding the neonatal chest radiograph: An insight into neonatal respiratory distress. Indian J Radiol Imaging 2021; 30:482-492. [PMID: 33737778 PMCID: PMC7954172 DOI: 10.4103/ijri.ijri_281_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 05/07/2020] [Revised: 07/25/2020] [Accepted: 08/11/2020] [Indexed: 11/23/2022] Open
Abstract
Respiratory distress is one of the leading causes of neonatal morbidity and mortality. Factors such as gestational age at birth, pulmonary maturity, and congenital factors are peculiar to this demographic. Clinical evaluation accompanied by chest radiography is the standard protocol for evaluating the underlying causative factors. Knowledge of the radiographic appearances of various pathologies and associations with certain congenital factors is quintessential for radiologists and primary neonatal care providers to steer the management in the right direction.
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Affiliation(s)
- Sanjay N Jain
- Department of Radiology, Prince Aly Khan Hospital, Aga Hall, Nesbit Road, Mazagaon, India
| | - Tanvi Modi
- Department of Radiology, TNMC and BYL Nair Hospital, Mumbai Central, Mumbai, Maharashtra, India
| | - Ravi U Varma
- Department of Radiology, TNMC and BYL Nair Hospital, Mumbai Central, Mumbai, Maharashtra, India
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