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Duarte CK, de Abreu Silva L, Castro CF, Ribeiro MV, Saldanha MF, Machado AM, Jansen AK. Prediction equations to estimate muscle mass using anthropometric data: a systematic review. Nutr Rev 2023; 81:1414-1440. [PMID: 37815928 DOI: 10.1093/nutrit/nuad022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023] Open
Abstract
CONTEXT Reduced muscle mass is linked to poor outcomes in both inpatients and outpatients, highlighting the importance of muscle mass assessment in clinical practice. However, laboratory methods to assess muscle mass are not yet feasible for routine use in clinical practice because of limited availability and high costs. OBJECTIVE This work aims to review the literature on muscle mass prediction by anthropometric equations in adults or older people. DATA SOURCES The following databases were searched for observational studies published until June 2022: MEDLINE, Embase, Scopus, SPORTDiscus, and Web of Science. DATA EXTRACTION Of 6437 articles initially identified, 63 met the inclusion criteria for this review. Four independent reviewers, working in pairs, selected and extracted data from those articles. DATA ANALYSIS Two studies reported new equations for prediction of skeletal muscle mass: 10 equations for free-fat mass and lean soft tissue, 22 for appendicular lean mass, 7 for upper-body muscle mass, and 7 for lower-body muscle mass. Twenty-one studies validated previously proposed equations. This systematic review shows there are numerous equations in the literature for muscle mass prediction, and most are validated for healthy adults. However, many equations were not always accurate and validated in all groups, especially people with obesity, undernourished people, and older people. Moreover, in some studies, it was unclear if fat-free mass or lean soft tissue had been assessed because of an imprecise description of muscle mass terminology. CONCLUSION This systematic review identified several feasible, practical, and low-cost equations for muscle mass prediction, some of which have excellent accuracy in healthy adults, older people, women, and athletes. Malnourished individuals and people with obesity were understudied in the literature, as were older people, for whom there are only equations for appendicular lean mass. SYSTEMATIC REVIEW REGISTRATION PROSPERO registration number CRD42021257200.
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Affiliation(s)
- Camila Kümmel Duarte
- are with the Postgraduate Program in Nutrition and Health, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Luciana de Abreu Silva
- are with the Postgraduate Program in Nutrition and Health, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Carolina Fernandes Castro
- are with the Department of Nutrition, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Mariana Vassallo Ribeiro
- are with the Department of Nutrition, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Marcelle Ferreira Saldanha
- are with the Postgraduate Program in Nutrition and Health, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Adriane Moreira Machado
- is with the Department of Nutrition, Faculdade Dinâmica do Vale do Piranga, Ponte Nova, Minas Gerais, Brazil
| | - Ann Kristine Jansen
- are with the Postgraduate Program in Nutrition and Health, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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Yoshida K, Takamatsu A, Matsubara T, Kitagawa T, Toshima F, Tanaka R, Gabata T. Deep learning-based cardiothoracic ratio measurement on chest radiograph: accuracy improvement without self-annotation. Quant Imaging Med Surg 2023; 13:6546-6554. [PMID: 37869343 PMCID: PMC10585545 DOI: 10.21037/qims-23-187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/21/2023] [Indexed: 10/24/2023]
Abstract
Background A reproducible and accurate automated approach to measuring cardiothoracic ratio on chest radiographs is warranted. This study aimed to develop a deep learning-based model for estimating the cardiothoracic ratio on chest radiographs without requiring self-annotation and to compare its results with those of manual measurements. Methods The U-net architecture was designed to segment the right and left lungs and the cardiac shadow, from chest radiographs. The cardiothoracic ratio was then calculated using these labels by a mathematical algorithm. The initial model of deep learning-based cardiothoracic ratio measurement was developed using open-source 247 chest radiographs that had already been annotated. The advanced model was developed using a training dataset of 729 original chest radiographs, the labels of which were generated by the initial model and then screened. The cardiothoracic ratio of the two models was estimated in an independent test set of 120 original cases, and the results were compared to those obtained through manual measurement by four radiologists and the image-reading reports. Results The means and standard deviations of the cardiothoracic ratio were 52.4% and 9.8% for the initial model, 51.0% and 9.3% for the advanced model, and 49.8% and 9.4% for the total of four manual measurements, respectively. The intraclass correlation coefficients (ICCs) of the cardiothoracic ratio ranged from 0.91 to 0.93 between the advanced model and the manual measurements, whereas those for the initial model and the manual measurements ranged from 0.77 to 0.82. Conclusions Deep learning-based cardiothoracic ratio estimation on chest radiographs correlated favorably with the results obtained through manual measurements by radiologists. When the model was trained on additional local images generated by the initial model, the correlation with manual measurement improved even more than the initial model alone.
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Affiliation(s)
- Kotaro Yoshida
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Atsushi Takamatsu
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Takashi Matsubara
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Taichi Kitagawa
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Fomihito Toshima
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Rie Tanaka
- College of Medical, Pharmaceutical & Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Toshifumi Gabata
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
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Kim D, Lee JH, Jang MJ, Park J, Hong W, Lee CS, Yang SY, Park CM. The Performance of a Deep Learning-Based Automatic Measurement Model for Measuring the Cardiothoracic Ratio on Chest Radiographs. Bioengineering (Basel) 2023; 10:1077. [PMID: 37760179 PMCID: PMC10525628 DOI: 10.3390/bioengineering10091077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/28/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
OBJECTIVE Prior studies on models based on deep learning (DL) and measuring the cardiothoracic ratio (CTR) on chest radiographs have lacked rigorous agreement analyses with radiologists or reader tests. We validated the performance of a commercially available DL-based CTR measurement model with various thoracic pathologies, and performed agreement analyses with thoracic radiologists and reader tests using a probabilistic-based reference. MATERIALS AND METHODS This study included 160 posteroanterior view chest radiographs (no lung or pleural abnormalities, pneumothorax, pleural effusion, consolidation, and n = 40 in each category) to externally test a DL-based CTR measurement model. To assess the agreement between the model and experts, intraclass or interclass correlation coefficients (ICCs) were compared between the model and two thoracic radiologists. In the reader tests with a probabilistic-based reference standard (Dawid-Skene consensus), we compared diagnostic measures-including sensitivity and negative predictive value (NPV)-for cardiomegaly between the model and five other radiologists using the non-inferiority test. RESULTS For the 160 chest radiographs, the model measured a median CTR of 0.521 (interquartile range, 0.446-0.59) and a mean CTR of 0.522 ± 0.095. The ICC between the two thoracic radiologists and between the model and two thoracic radiologists was not significantly different (0.972 versus 0.959, p = 0.192), even across various pathologies (all p-values > 0.05). The model showed non-inferior diagnostic performance, including sensitivity (96.3% versus 97.8%) and NPV (95.6% versus 97.4%) (p < 0.001 in both), compared with the radiologists for all 160 chest radiographs. However, it showed inferior sensitivity in chest radiographs with consolidation (95.5% versus 99.9%; p = 0.082) and NPV in chest radiographs with pleural effusion (92.9% versus 94.6%; p = 0.079) and consolidation (94.1% versus 98.7%; p = 0.173). CONCLUSION While the sensitivity and NPV of this model for diagnosing cardiomegaly in chest radiographs with consolidation or pleural effusion were not as high as those of the radiologists, it demonstrated good agreement with the thoracic radiologists in measuring the CTR across various pathologies.
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Affiliation(s)
- Donguk Kim
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea;
| | - Jong Hyuk Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
| | - Myoung-jin Jang
- Medical Research Collaborating Center, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
| | - Jongsoo Park
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
- Department of Radiology, College of Medicine, Yeungnam University 170, Hyeonchung-ro, Nam-gu, Daegu 42415, Republic of Korea
| | - Wonju Hong
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
- Department of Radiology, Hallym University Sacred Heart Hospital, Anyang-si, Gyeonggi-do 14068, Republic of Korea
| | - Chan Su Lee
- Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co. Ltd., 623, Gangnam-daero, Seocho-gu, Seoul 06524, Republic of Korea
| | - Si Yeong Yang
- Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co. Ltd., 623, Gangnam-daero, Seocho-gu, Seoul 06524, Republic of Korea
| | - Chang Min Park
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea;
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
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Chou HH, Lin JY, Shen GT, Huang CY. Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients. Diagnostics (Basel) 2023; 13:diagnostics13081376. [PMID: 37189477 DOI: 10.3390/diagnostics13081376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/05/2023] [Accepted: 04/07/2023] [Indexed: 05/17/2023] Open
Abstract
Cardiomegaly is associated with poor clinical outcomes and is assessed by routine monitoring of the cardiothoracic ratio (CTR) from chest X-rays (CXRs). Judgment of the margins of the heart and lungs is subjective and may vary between different operators. METHODS Patients aged > 19 years in our hemodialysis unit from March 2021 to October 2021 were enrolled. The borders of the lungs and heart on CXRs were labeled by two nephrologists as the ground truth (nephrologist-defined mask). We implemented AlbuNet-34, a U-Net variant, to predict the heart and lung margins from CXR images and to automatically calculate the CTRs. RESULTS The coefficient of determination (R2) obtained using the neural network model was 0.96, compared with an R2 of 0.90 obtained by nurse practitioners. The mean difference between the CTRs calculated by the nurse practitioners and senior nephrologists was 1.52 ± 1.46%, and that between the neural network model and the nephrologists was 0.83 ± 0.87% (p < 0.001). The mean CTR calculation duration was 85 s using the manual method and less than 2 s using the automated method (p < 0.001). CONCLUSIONS Our study confirmed the validity of automated CTR calculations. By achieving high accuracy and saving time, our model can be implemented in clinical practice.
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Affiliation(s)
- Hsin-Hsu Chou
- Department of Pediatrics, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600566, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413305, Taiwan
| | - Jin-Yi Lin
- Innovation and Incubation Center, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600566, Taiwan
| | - Guan-Ting Shen
- Innovation and Incubation Center, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600566, Taiwan
| | - Chih-Yuan Huang
- Division of Nephrology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600566, Taiwan
- Department of Sport Management, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan 717301, Taiwan
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Ajmera P, Kharat A, Gupte T, Pant R, Kulkarni V, Duddalwar V, Lamghare P. Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs. Acta Radiol Open 2022; 11:20584601221107345. [PMID: 35899142 PMCID: PMC9309780 DOI: 10.1177/20584601221107345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background Cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR (>0.55) is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. Purpose We propose a deep learning (DL)-based model for automatic CTR calculation to assist radiologists with rapid diagnosis of cardiomegaly and thus optimise the radiology flow. Material and Methods The study population included 1012 posteroanterior CXRs from a single institution. The Attention U-Net DL architecture was used for the automatic calculation of CTR. An observer performance test was conducted to assess the radiologist’s performance in diagnosing cardiomegaly with and without artificial intelligence assistance. Results U-Net model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], specificity >99%, precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. Furthermore, the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Conclusion Our segmentation-based AI model demonstrated high specificity (>99%) and sensitivity (80%) for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with provision of AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows by reducing radiologists’ burden and alerting to an abnormal enlarged heart early on.
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Affiliation(s)
- Pranav Ajmera
- Department of Radiodiagnosis, Dr DY Patil Medical College, Hospital and Research Center, DY Patil Vidyapeeth, DPU, Pune, India
| | - Amit Kharat
- Department of Radiodiagnosis, Dr DY Patil Medical College, Hospital and Research Center, DY Patil Vidyapeeth, DPU, Pune, India
| | | | - Richa Pant
- DeepTek Medical Imaging Pvt. Ltd, Pune, India
| | | | - Vinay Duddalwar
- Department of Radiology and Biomedical Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Purnachandra Lamghare
- Department of Radiodiagnosis, Dr DY Patil Medical College, Hospital and Research Center, DY Patil Vidyapeeth, DPU, Pune, India
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Xing W, He W, Li X, Chen J, Cao Y, Zhou W, Shen Q, Zhang X, Ta D. Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106869. [PMID: 35576685 DOI: 10.1016/j.cmpb.2022.106869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 04/23/2022] [Accepted: 05/08/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Bronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day after birth or discharge by analyzing the clinical indicators, which may cause the delay of the best treatment opportunity. Thus, we proposed a deep learning-based method using chest X-ray images of the 28th day of oxygen inhalation for the early severity prediction of bronchopulmonary dysplasia in clinic. METHODS We first adopted a two-step lung field extraction method by combining digital image processing and human-computer interaction to form the one-to-one corresponding image and label. The designed XSEG-Net model was then trained for segmenting the chest X-ray images, with the results being used for the analysis of heart development and clinical severity. Therein, Six-Point cardiothoracic ratio measurement algorithm based on corner detection was designed for the analysis of heart development; and the transfer learning of deep convolutional neural network models were used for the early prediction of clinical severities. RESULTS The dice and cross-entropy loss value of the training of XSEG-Net network reached 0.9794 and 0.0146. The dice, volumetric overlap error, relative volume difference, precision, and recall were used to evaluate the trained model in testing set with the result being 98.43 ± 0.39%, 0.49 ± 0.35%, 0.49 ± 0.35%, 98.67 ± 0.40%, and 98.20 ± 0.47%, respectively. The errors between the Six-Point cardiothoracic ratio measurement method and the gold standard were 0.0122 ± 0.0084. The deep convolutional neural network model based on VGGNet had the promising prediction performance, with the accuracy, precision, sensitivity, specificity, and F1 score reaching 95.58 ± 0.48%, 95.61 ± 0.55%, 95.67 ± 0.44%, 96.98 ± 0.42%, and 95.61±0.48%, respectively. CONCLUSIONS These experimental results of the proposed methods in lung field segmentation, cardiothoracic ratio measurement and clinic severity prediction were better than previous methods, which proved that this method had great potential for clinical application.
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Affiliation(s)
- Wenyu Xing
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China; Human Phenome Institute, Fudan University, Shanghai 200438, China
| | - Wen He
- Department of Respiratory, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Xiaoling Li
- Department of Respiratory, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Jiangang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200237, China
| | - Yun Cao
- Department of Neonatology, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Wenhao Zhou
- Department of Neonatology, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Quanli Shen
- Department of Radiology, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Xiaobo Zhang
- Department of Respiratory, Children's Hospital of Fudan University, Shanghai 201102, China.
| | - Dean Ta
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China; Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China.
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Posteroanterior Chest X-ray Image Classification with a Multilayer 1D Convolutional Neural Network-Based Classifier for Cardiomegaly Level Screening. ELECTRONICS 2022. [DOI: 10.3390/electronics11091364] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Palpitations, chest tightness, and shortness of breath are early indications of cardiomegaly, which is an asymptomatic disease. Their causes and treatment strategies are different due to differing indications. Hence, early screening of cardiomegaly levels can be used to make a strategy for administering drugs and surgical treatments. In this study, we will establish a multilayer one-dimensional (1D) convolutional neural network (CNN)-based classifier for automatic cardiomegaly level screening based on chest X-ray (CXR) image classification in frontal posteroanterior view. Using two-round 1D convolutional processes in the convolutional pooling layer, two-dimensional (2D) feature maps can be converted into feature signals, which can enhance their characteristics for identifying normal condition and cardiomegaly levels. In the classification layer, a classifier based on gray relational analysis, which has a straightforward mathematical operation, is used to screen the cardiomegaly levels. Based on the collected datasets from the National Institutes of Health CXR image database, the proposed multilayer 1D CNN-based classifier with K-fold cross-validation has promising results for the intended medical purpose, with precision of 97.80%, recall of 98.20%, accuracy of 98.00%, and F1 score of 0.9799.
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Yin XX, Sun L, Fu Y, Lu R, Zhang Y. U-Net-Based Medical Image Segmentation. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4189781. [PMID: 35463660 PMCID: PMC9033381 DOI: 10.1155/2022/4189781] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/02/2022] [Accepted: 03/23/2022] [Indexed: 11/17/2022]
Abstract
Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of small targets and its scalable network architecture. With the increasing requirements for the performance of segmentation in medical imaging in recent years, U-Net has been cited academically more than 2500 times. Many scholars have been constantly developing the U-Net architecture. This paper summarizes the medical image segmentation technologies based on the U-Net structure variants concerning their structure, innovation, efficiency, etc.; reviews and categorizes the related methodology; and introduces the loss functions, evaluation parameters, and modules commonly applied to segmentation in medical imaging, which will provide a good reference for the future research.
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Affiliation(s)
- Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
- College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
| | - Le Sun
- Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yuhan Fu
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
| | - Ruiliang Lu
- Department of Radiology, The First People's Hospital of Foshan, Foshan 528000, China
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
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Saiviroonporn P, Wonglaksanapimon S, Chaisangmongkon W, Chamveha I, Yodprom P, Butnian K, Siriapisith T, Tongdee T. A clinical evaluation study of cardiothoracic ratio measurement using artificial intelligence. BMC Med Imaging 2022; 22:46. [PMID: 35296262 PMCID: PMC8925133 DOI: 10.1186/s12880-022-00767-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/28/2022] [Indexed: 11/25/2022] Open
Abstract
Background Artificial intelligence, particularly the deep learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on chest X-ray (CXR) images. In everyday clinical use, however, this technology is usually implemented in a non-automated (AI-assisted) capacity because it still requires approval from radiologists. We investigated the performance and efficiency of our recently proposed models for the AI-assisted method intended for clinical practice. Methods We validated four proposed DL models (AlbuNet, SegNet, VGG-11, and VGG-16) to find the best model for clinical implementation using a dataset of 7517 CXR images from manual operations. These models were investigated in single-model and combined-model modes to find the model with the highest percentage of results where the user could accept the results without further interaction (excellent grade), and with measurement variation within ± 1.8% of the human-operating range. The best model from the validation study was then tested on an evaluation dataset of 9386 CXR images using the AI-assisted method with two radiologists to measure the yield of excellent grade results, observer variation, and operating time. A Bland–Altman plot with coefficient of variation (CV) was employed to evaluate agreement between measurements. Results The VGG-16 gave the highest excellent grade result (68.9%) of any single-model mode with a CV comparable to manual operation (2.12% vs 2.13%). No DL model produced a failure-grade result. The combined-model mode of AlbuNet + VGG-11 model yielded excellent grades in 82.7% of images and a CV of 1.36%. Using the evaluation dataset, the AlbuNet + VGG-11 model produced excellent grade results in 77.8% of images, a CV of 1.55%, and reduced CTR measurement time by almost ten-fold (1.07 ± 2.62 s vs 10.6 ± 1.5 s) compared with manual operation. Conclusion Due to its excellent accuracy and speed, the AlbuNet + VGG-11 model could be clinically implemented to assist radiologists with CTR measurement.
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Affiliation(s)
- Pairash Saiviroonporn
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand
| | - Suwimon Wonglaksanapimon
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand.
| | | | | | - Pakorn Yodprom
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand
| | - Krittachat Butnian
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand
| | - Thanogchai Siriapisith
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand
| | - Trongtum Tongdee
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand
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Lee MS, Kim YS, Kim M, Usman M, Byon SS, Kim SH, Lee BI, Lee BD. Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning. Sci Rep 2021; 11:16885. [PMID: 34413405 PMCID: PMC8376868 DOI: 10.1038/s41598-021-96433-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 08/09/2021] [Indexed: 11/21/2022] Open
Abstract
We examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior-anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.
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Affiliation(s)
- Mu Sook Lee
- Department of Radiology, Keimyung University Dongsan Hospital, 1035, Dalgubeol-daero, Sindang-dong, Daegu, 42601, Republic of Korea
| | - Yong Soo Kim
- Division of ICT Convergence, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon, Gyeonggi-do, 16227, Republic of Korea
| | - Minki Kim
- Division of AI Computer Science and Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon, Gyeonggi-do, 16227, Republic of Korea
| | - Muhammad Usman
- Center for Artificial Intelligence in Medicine and Imaging, HealthHub, Co. Ltd., 623, Gangnam-daero, Seocho-gu, Seoul, 06524, Republic of Korea
| | - Shi Sub Byon
- Center for Artificial Intelligence in Medicine and Imaging, HealthHub, Co. Ltd., 623, Gangnam-daero, Seocho-gu, Seoul, 06524, Republic of Korea
| | - Sung Hyun Kim
- Human Medical Imaging and Intervention Center, 621, Gangnam-daero, Seocho-gu, Seoul, 06524, Republic of Korea
| | - Byoung Il Lee
- Center for Artificial Intelligence in Medicine and Imaging, HealthHub, Co. Ltd., 623, Gangnam-daero, Seocho-gu, Seoul, 06524, Republic of Korea
| | - Byoung-Dai Lee
- Division of AI Computer Science and Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon, Gyeonggi-do, 16227, Republic of Korea.
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