1
|
Nian S, Zhao Y, Li C, Zhu K, Li N, Li W, Chen J. Development and validation of a radiomics-based model for predicting osteoporosis in patients with lumbar compression fractures. Spine J 2024:S1529-9430(24)00187-6. [PMID: 38679078 DOI: 10.1016/j.spinee.2024.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 04/11/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Osteoporosis, a metabolic bone disorder, markedly elevates fracture risks, with vertebral compression fractures being predominant. Antiosteoporotic treatments for patients with osteoporotic vertebral compression fractures (OVCF) lessen both the occurrence of subsequent fractures and associated pain. Thus, diagnosing osteoporosis in OVCF patients is vital. PURPOSE The aim of this study was to develop a predictive radiographic model using T1 sequence MRI images to accurately determine whether patients with lumbar spine compression fractures also have osteoporosis. STUDY DESIGN Retrospective cohort study. PATIENT SAMPLE Patients over 45 years of age diagnosed with a fresh lumbar compression fracture. OUTCOME MEASURES Diagnostic accuracy of the model (area under the ROC curve). METHODS The study retrospectively collected clinical and imaging data (MRI and DEXA) from hospitalized lumbar compression fracture patients (L1-L4) aged 45 years or older between January 2021 and June 2023. Using the pyradiomics package in Python, features from the lumbar compression fracture vertebral region of interest (ROI) were extracted. Downscaling of the extracted features was performed using the Mann-Whitney U test and the least absolute shrinkage selection operator (LASSO) algorithm. Subsequently, six machine learning models (Naive Bayes, Support Vector Machine [SVM], Decision Tree, Random Forest, Extreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LightGBM]) were employed to train and validate these features in predicting osteoporosis comorbidity in OVCF patients. RESULTS A total of 128 participants, 79 in the osteoporotic group and 49 in the non-osteoporotic group, met the study's inclusion and exclusion criteria. From the T1 sequence MRI images, 1906 imaging features were extracted in both groups. Utilizing the Mann-Whitney U test, 365 radiologic features were selected out of the initial 1,906. Ultimately, the lasso algorithm identified 14 significant radiological features. These features, incorporated into six conventional machine learning algorithms, demonstrated successful prediction of osteoporosis in the validation set. The NaiveBayes model yielded an area under the receiver operating characteristic curve (AUC) of 0.84, sensitivity of 0.87, specificity of 0.70, and accuracy of 0.81. CONCLUSIONS A NaiveBayes machine learning algorithm can predict osteoporosis in OVCF patients using t1-sequence MRI images of lumbar compression fractures. This approach aims to obviate the necessity for further osteoporosis assessments, diminish patient exposure to radiation, and bolster the clinical care of patients with OVCF.
Collapse
Affiliation(s)
- Sunqi Nian
- The Affiliated Hospital of Kunming University of Science and Technology, Department of Orthopaedics, The First People's Hospital of Yunnan Province, 157 Jinbi Road, Kunming, Yunnan Province, China
| | - Yayu Zhao
- The Affiliated Hospital of Kunming University of Science and Technology, Department of Orthopaedics, The First People's Hospital of Yunnan Province, 157 Jinbi Road, Kunming, Yunnan Province, China
| | - Chengjin Li
- The Affiliated Hospital of Kunming University of Science and Technology, Department of Orthopaedics, The First People's Hospital of Yunnan Province, 157 Jinbi Road, Kunming, Yunnan Province, China
| | - Kang Zhu
- Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, 104 Guanghua Street, Kunming, Yunnan Province, China
| | - Na Li
- Department of Anesthesiology, 920th Hospital of the Joint Logistics Support Force, 212 Daguan Road, Kunming, Yunnan Province, China
| | - Weichao Li
- The Affiliated Hospital of Kunming University of Science and Technology, Department of Orthopaedics, The First People's Hospital of Yunnan Province, 157 Jinbi Road, Kunming, Yunnan Province, China; Department of Orthopedics, Clinical Medical Centre for Yunnan Provincial Spinal Cord Disease, Yunnan Key Laboratory of Digital Orthopedics, 157 Jinbi Road, Kunming, Yunnan Province, China
| | - Jiayu Chen
- The Affiliated Hospital of Kunming University of Science and Technology, Department of Orthopaedics, The First People's Hospital of Yunnan Province, 157 Jinbi Road, Kunming, Yunnan Province, China; Department of Orthopedics, Clinical Medical Centre for Yunnan Provincial Spinal Cord Disease, Yunnan Key Laboratory of Digital Orthopedics, 157 Jinbi Road, Kunming, Yunnan Province, China.
| |
Collapse
|
2
|
Hussain A, Marlowe S, Ali M, Uy E, Bhopalwala H, Gullapalli D, Vangara A, Haroon M, Akbar A, Piercy J. A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders. Cureus 2024; 16:e51581. [PMID: 38313926 PMCID: PMC10836179 DOI: 10.7759/cureus.51581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/02/2024] [Indexed: 02/06/2024] Open
Abstract
This systematic review examines the transformative impact of artificial intelligence (AI) in managing lung disorders through a comprehensive analysis of articles spanning 2014 to 2023. Evaluating AI's multifaceted roles in radiological imaging, disease burden prediction, detection, diagnosis, and molecular mechanisms, this review presents a critical synthesis of key insights from select articles. The findings underscore AI's significant strides in bolstering diagnostic accuracy, interpreting radiological imaging, predicting disease burdens, and deepening the understanding of tuberculosis (TB), chronic obstructive pulmonary disease (COPD), silicosis, pneumoconiosis, and lung fibrosis. The synthesis positions AI as a revolutionary tool within the healthcare system, offering vital implications for healthcare workers, policymakers, and researchers in comprehending and leveraging AI's pivotal role in lung disease management.
Collapse
Affiliation(s)
- Akbar Hussain
- Internal Medicine, Appalachian Regional Healthcare, Harlan, USA
| | - Stanley Marlowe
- Internal Medicine, Appalachian Regional Healthcare, Harlan, USA
| | - Muhammad Ali
- Pulmonary and Critical Care, Appalachian Regional Healthcare, Hazard, USA
| | - Edilfavia Uy
- Diabetes and Endocrinology, Appalachian Regional Healthcare, Whitesburg, USA
| | - Huzefa Bhopalwala
- Internal Medicine, Appalachian Regional Healthcare, Whitesburg, USA
- Cardiovascular, Mayo Clinic, Rochester, USA
| | | | - Avinash Vangara
- Internal Medicine, Appalachian Regional Healthcare, Harlan, USA
| | - Moeez Haroon
- Internal Medicine, Appalachian Regional Healthcare, Harlan, USA
| | - Aelia Akbar
- Public Health, Appalachian Regional Healthcare, Harlan, USA
| | - Jonathan Piercy
- Internal Medicine, Appalachian Regional Healthcare, Whitesburg, USA
| |
Collapse
|
3
|
Wang Y, Cui F, Ding X, Yao Y, Li G, Gui G, Shen F, Li B. Automated identification of the preclinical stage of coal workers' pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
4
|
Suganuma N, Yoshida S, Takeuchi Y, Nomura YK, Suzuki K. Artificial Intelligence in Quantitative Chest Imaging Analysis for Occupational Lung Disease. Semin Respir Crit Care Med 2023; 44:362-369. [PMID: 37072023 DOI: 10.1055/s-0043-1767760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Occupational lung disease manifests complex radiologic findings which have long been a challenge for computer-assisted diagnosis (CAD). This journey started in the 1970s when texture analysis was developed and applied to diffuse lung disease. Pneumoconiosis appears on radiography as a combination of small opacities, large opacities, and pleural shadows. The International Labor Organization International Classification of Radiograph of Pneumoconioses has been the main tool used to describe pneumoconioses and is an ideal system that can be adapted for CAD using artificial intelligence (AI). AI includes machine learning which utilizes deep learning or an artificial neural network. This in turn includes a convolutional neural network. The tasks of CAD are systematically described as classification, detection, and segmentation of the target lesions. Alex-net, VGG16, and U-Net are among the most common algorithms used in the development of systems for the diagnosis of diffuse lung disease, including occupational lung disease. We describe the long journey in the pursuit of CAD of pneumoconioses including our recent proposal of a new expert system.
Collapse
Affiliation(s)
- Narufumi Suganuma
- Department of Environmental Medicine, Kochi Medical School, Nankoku, Kochi, Japan
| | - Shinichi Yoshida
- School of Information, Kochi University of Technology, Nankoku, Kochi, Japan
| | - Yuma Takeuchi
- Department of Environmental Medicine, Kochi Medical School, Nankoku, Kochi, Japan
- Department of Radiology, Kochi Medical School Hospital, Nankoku, Kochi, Japan
| | - Yoshua K Nomura
- Department of Environmental Medicine, Kochi Medical School, Nankoku, Kochi, Japan
| | - Kazuhiro Suzuki
- Department of Radiology, School of Medicine, Juntendo University, Bunkyo City, Tokyo, Japan
| |
Collapse
|
5
|
Huang Y, Si Y, Hu B, Zhang Y, Wu S, Wu D, Wang Q. Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images. Comput Biol Med 2022; 150:106137. [PMID: 36191395 DOI: 10.1016/j.compbiomed.2022.106137] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/13/2022] [Accepted: 09/18/2022] [Indexed: 11/22/2022]
Abstract
In the past decade, deep learning methods have been implemented in the medical image fields and have achieved good performance. Recently, deep learning algorithms have been successful in the evaluation of diagnosis on lung images. Although chest radiography (CR) is the standard data modality for diagnosing pneumoconiosis, computed tomography (CT) typically provides more details of the lesions in the lung. Thus, a transformer-based factorized encoder (TBFE) was proposed and first applied for the classification of pneumoconiosis depicted on 3D CT images. Specifically, a factorized encoder consists of two transformer encoders. The first transformer encoder enables the interaction of intra-slice by encoding feature maps from the same slice of CT. The second transformer encoder explores the inter-slice interaction by encoding feature maps from different slices. In addition, the lack of grading standards on CT for labeling the pneumoconiosis lesions. Thus, an acknowledged CR-based grading system was applied to mark the corresponding pneumoconiosis CT stage. Then, we pre-trained the 3D convolutional autoencoder on the public LIDC-IDRI dataset and fixed the parameters of the last convolutional layer of the encoder to extract CT feature maps with underlying spatial structural information from our 3D CT dataset. Experimental results demonstrated the superiority of the TBFE over other 3D-CNN networks, achieving an accuracy of 97.06%, a recall of 89.33%, precision of 90%, and an F1-score of 93.33%, using 10-fold cross-validation.
Collapse
Affiliation(s)
- Yingying Huang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shanxi, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key laboratory of Biomedical Spectroscopy, Xi'an 710119, Shanxi, China.
| | - Yang Si
- Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, Department of Neurology, Chengdu, Sichuan, China; University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
| | - Bingliang Hu
- Key laboratory of Biomedical Spectroscopy, Xi'an 710119, Shanxi, China.
| | - Yan Zhang
- Department of Radiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Shuang Wu
- Department of Radiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Dongsheng Wu
- Department of Radiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Research Center of Artificial Intelligence in Medicine, West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, Sichuan, China.
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shanxi, China; Key laboratory of Biomedical Spectroscopy, Xi'an 710119, Shanxi, China.
| |
Collapse
|
6
|
Devnath L, Fan Z, Luo S, Summons P, Wang D. Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11193. [PMID: 36141457 PMCID: PMC9517617 DOI: 10.3390/ijerph191811193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 08/25/2022] [Accepted: 08/27/2022] [Indexed: 06/16/2023]
Abstract
Pneumoconiosis is a group of occupational lung diseases induced by mineral dust inhalation and subsequent lung tissue reactions. It can eventually cause irreparable lung damage, as well as gradual and permanent physical impairments. It has affected millions of workers in hazardous industries throughout the world, and it is a leading cause of occupational death. It is difficult to diagnose early pneumoconiosis because of the low sensitivity of chest radiographs, the wide variation in interpretation between and among readers, and the scarcity of B-readers, which all add to the difficulty in diagnosing these occupational illnesses. In recent years, deep machine learning algorithms have been extremely successful at classifying and localising abnormality of medical images. In this study, we proposed an ensemble learning approach to improve pneumoconiosis detection in chest X-rays (CXRs) using nine machine learning classifiers and multi-dimensional deep features extracted using CheXNet-121 architecture. There were eight evaluation metrics utilised for each high-level feature set of the associated cross-validation datasets in order to compare the ensemble performance and state-of-the-art techniques from the literature that used the same cross-validation datasets. It is observed that integrated ensemble learning exhibits promising results (92.68% accuracy, 85.66% Matthews correlation coefficient (MCC), and 0.9302 area under the precision-recall (PR) curve), compared to individual CheXNet-121 and other state-of-the-art techniques. Finally, Grad-CAM was used to visualise the learned behaviour of individual dense blocks within CheXNet-121 and their ensembles into three-color channels of CXRs. We compared the Grad-CAM-indicated ROI to the ground-truth ROI using the intersection of the union (IOU) and average-precision (AP) values for each classifier and their ensemble. Through the visualisation of the Grad-CAM within the blue channel, the average IOU passed more than 90% of the pneumoconiosis detection in chest radiographs.
Collapse
Affiliation(s)
- Liton Devnath
- School of Information and Physical Sciences, The University of Newcastle, Newcastle 2308, Australia
- British Columbia Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada
| | - Zongwen Fan
- College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
| | - Suhuai Luo
- School of Information and Physical Sciences, The University of Newcastle, Newcastle 2308, Australia
| | - Peter Summons
- School of Information and Physical Sciences, The University of Newcastle, Newcastle 2308, Australia
| | - Dadong Wang
- Quantitative Imaging, CSIRO Data61, Marsfield 2122, Australia
| |
Collapse
|
7
|
Computer-Aided Diagnosis of Coal Workers' Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19116439. [PMID: 35682023 PMCID: PMC9180284 DOI: 10.3390/ijerph19116439] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/20/2022] [Accepted: 05/22/2022] [Indexed: 02/01/2023]
Abstract
Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers' pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers' survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed.
Collapse
|
8
|
Hao C, Jin N, Qiu C, Ba K, Wang X, Zhang H, Zhao Q, Huang B. Balanced Convolutional Neural Networks for Pneumoconiosis Detection. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18179091. [PMID: 34501684 PMCID: PMC8431598 DOI: 10.3390/ijerph18179091] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 08/18/2021] [Accepted: 08/24/2021] [Indexed: 01/10/2023]
Abstract
Pneumoconiosis remains one of the most common and harmful occupational diseases in China, leading to huge economic losses to society with its high prevalence and costly treatment. Diagnosis of pneumoconiosis still strongly depends on the experience of radiologists, which affects rapid detection on large populations. Recent research focuses on computer-aided detection based on machine learning. These have achieved high accuracy, among which artificial neural network (ANN) shows excellent performance. However, due to imbalanced samples and lack of interpretability, wide utilization in clinical practice meets difficulty. To address these problems, we first establish a pneumoconiosis radiograph dataset, including both positive and negative samples. Second, deep convolutional diagnosis approaches are compared in pneumoconiosis detection, and a balanced training is adopted to promote recall. Comprehensive experiments conducted on this dataset demonstrate high accuracy (88.6%). Third, we explain diagnosis results by visualizing suspected opacities on pneumoconiosis radiographs, which could provide solid diagnostic reference for surgeons.
Collapse
Affiliation(s)
- Chaofan Hao
- Department of Automation, Tsinghua University, Beijing 100084, China; (C.H.); (K.B.)
| | - Nan Jin
- Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China; (N.J.); (C.Q.); (X.W.); (H.Z.)
| | - Cuijuan Qiu
- Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China; (N.J.); (C.Q.); (X.W.); (H.Z.)
| | - Kun Ba
- Department of Automation, Tsinghua University, Beijing 100084, China; (C.H.); (K.B.)
| | - Xiaoxi Wang
- Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China; (N.J.); (C.Q.); (X.W.); (H.Z.)
| | - Huadong Zhang
- Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China; (N.J.); (C.Q.); (X.W.); (H.Z.)
| | - Qi Zhao
- Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China; (N.J.); (C.Q.); (X.W.); (H.Z.)
- Correspondence: (Q.Z.); (B.H.)
| | - Biqing Huang
- Department of Automation, Tsinghua University, Beijing 100084, China; (C.H.); (K.B.)
- Correspondence: (Q.Z.); (B.H.)
| |
Collapse
|
9
|
Informatics Approaches for Recognition, Management, and Prevention of Occupational Respiratory Disease. Clin Chest Med 2021; 41:605-621. [PMID: 33153682 DOI: 10.1016/j.ccm.2020.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Computer and information systems can improve occupational respiratory disease prevention and surveillance by providing efficient resources for patients, workers, clinicians, and public health practitioners. Advances include interlinking electronic health records, autocoding surveillance data, clinical decision support systems, and social media applications for acquiring and disseminating information. Obstacles to advances include inflexible hierarchical coding schemes, inadequate occupational health electronic health record systems, and inadequate public focus on occupational respiratory disease. Potentially transformative approaches include machine learning, natural language processing, and improved ontologies.
Collapse
|
10
|
Wang X, Yu J, Zhu Q, Li S, Zhao Z, Yang B, Pu J. Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography. Occup Environ Med 2020; 77:597-602. [DOI: 10.1136/oemed-2019-106386] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 04/30/2020] [Accepted: 05/11/2020] [Indexed: 11/04/2022]
Abstract
ObjectivesTo investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.MethodsWe retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.ResultsThe Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, p<0.001).ConclusionOur experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.
Collapse
|
11
|
Haleem A, Javaid M, Khan IH. Current status and applications of Artificial Intelligence (AI) in medical field: An overview. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.cmrp.2019.11.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
12
|
Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages. J Digit Imaging 2018; 30:413-426. [PMID: 28108817 DOI: 10.1007/s10278-017-9942-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.
Collapse
|
13
|
Zhu B, Luo W, Li B, Chen B, Yang Q, Xu Y, Wu X, Chen H, Zhang K. The development and evaluation of a computerized diagnosis scheme for pneumoconiosis on digital chest radiographs. Biomed Eng Online 2014; 13:141. [PMID: 25277489 PMCID: PMC4271323 DOI: 10.1186/1475-925x-13-141] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Accepted: 09/24/2014] [Indexed: 11/25/2022] Open
Abstract
Purpose To diagnose pneumoconiosis using a computer-aided diagnosis system based on digital chest radiographs. Methods Lung fields were first extracted by combining the traditional Otsu-threshold method with a morphological reconstruction on digital radiographs (DRs), and then subdivided into six non-overlapping regions (region (a-f)). Twenty-two wavelet-based energy texture features were calculated exclusively from each region and selected using a decision tree algorithm. A support vector machine (SVM) with a linear kernel was trained using samples with texture features to classify an individual region of a healthy subject or a pneumoconiosis patient. The final classification results were obtained by integrating these individual classifiers with the weighted voting method. All models were developed on a dataset of 85 healthy controls and 40 stage I or II pneumoconiosis patients and validated by using the bootstrap resampling with replacement method. Results The areas under receiver operating characteristic curves (AUCs) of regions (c) and (f) were 0.688 and 0.563, which were worse than those of the other four regions. Region (c) and (f) were both excluded from the individual classifiers that were going to be assembled further. When built on the selected texture features, each individual SVM showed a higher diagnostic performance for the training set and the test set. The classification performance after an ensemble was 0.997 and 0.961 of the AUC value for the training and test sets, respectively. The final results were 0.974 ± 0.018 for AUC value and 0.929 ± 0.018 for accuracy. Conclusion The integrated SVM model built on the selected feature set showed the highest diagnostic performance among all individual SVM models. The model has good potential in diagnosing pneumoconiosis based on digital chest radiographs.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Hui Chen
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
| | | |
Collapse
|