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Liu T, Zhang ZH, Zhou QH, Cheng QZ, Yang Y, Li JS, Zhang XM, Zhang JQ. MI-DenseCFNet: deep learning-based multimodal diagnosis models for Aureus and Aspergillus pneumonia. Eur Radiol 2024; 34:5066-5076. [PMID: 38231392 DOI: 10.1007/s00330-023-10578-3] [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] [Received: 11/08/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 01/18/2024]
Abstract
OBJECTIVE To build and merge a diagnostic model called multi-input DenseNet fused with clinical features (MI-DenseCFNet) for discriminating between Staphylococcus aureus pneumonia (SAP) and Aspergillus pneumonia (ASP) and to evaluate the significant correlation of each clinical feature in determining these two types of pneumonia using a random forest dichotomous diagnosis model. This will enhance diagnostic accuracy and efficiency in distinguishing between SAP and ASP. METHODS In this study, 60 patients with clinically confirmed SAP and ASP, who were admitted to four large tertiary hospitals in Kunming, China, were included. Thoracic high-resolution CT lung windows of all patients were extracted from the picture archiving and communication system, and the corresponding clinical data of each patient were collected. RESULTS The MI-DenseCFNet diagnosis model demonstrates an internal validation set with an area under the curve (AUC) of 0.92. Its external validation set demonstrates an AUC of 0.83. The model requires only 10.24s to generate a categorical diagnosis and produce results from 20 cases of data. Compared with high-, mid-, and low-ranking radiologists, the model achieves accuracies of 78% vs. 75% vs. 60% vs. 40%. Eleven significant clinical features were screened by the random forest dichotomous diagnosis model. CONCLUSION The MI-DenseCFNet multimodal diagnosis model can effectively diagnose SAP and ASP, and its diagnostic performance significantly exceeds that of junior radiologists. The 11 important clinical features were screened in the constructed random forest dichotomous diagnostic model, providing a reference for clinicians. CLINICAL RELEVANCE STATEMENT MI-DenseCFNet could provide diagnostic assistance for primary hospitals that do not have advanced radiologists, enabling patients with suspected infections like Staphylococcus aureus pneumonia or Aspergillus pneumonia to receive a quicker diagnosis and cut down on the abuse of antibiotics. KEY POINTS • MI-DenseCFNet combines deep learning neural networks with crucial clinical features to discern between Staphylococcus aureus pneumonia and Aspergillus pneumonia. • The comprehensive group had an area under the curve of 0.92, surpassing the proficiency of junior radiologists. • This model can enhance a primary radiologist's diagnostic capacity.
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Affiliation(s)
- Tong Liu
- The Second Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Wuhua District, Kunming, Yunnan, 650032, People's Republic of China
| | - Zheng-Hua Zhang
- Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, 650032, People's Republic of China
| | - Qi-Hao Zhou
- School of Information, Yunnan University, Kunming, Yunnan, 650032, People's Republic of China
| | - Qing-Zhao Cheng
- The Second Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Wuhua District, Kunming, Yunnan, 650032, People's Republic of China
| | - Yue Yang
- The Second Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Wuhua District, Kunming, Yunnan, 650032, People's Republic of China
| | - Jia-Shu Li
- The Second Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Wuhua District, Kunming, Yunnan, 650032, People's Republic of China
| | - Xue-Mei Zhang
- The Second Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Wuhua District, Kunming, Yunnan, 650032, People's Republic of China
| | - Jian-Qing Zhang
- The Second Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Wuhua District, Kunming, Yunnan, 650032, People's Republic of China.
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Lancaster AC, Cardin ME, Nguyen JA, Mehta TI, Oncel D, Bai HX, Cohen KA, Lin CT. Utilizing Deep Learning and Computed Tomography to Determine Pulmonary Nodule Activity in Patients With Nontuberculous Mycobacterial-Lung Disease. J Thorac Imaging 2024; 39:194-199. [PMID: 38640144 PMCID: PMC11031630 DOI: 10.1097/rti.0000000000000745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
PURPOSE To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT). MATERIALS AND METHODS We collected a data set of 650 nodules (316 acute and 334 chronic) from the CT scans of 110 patients with NTM-LD. The data set was divided into training, validation, and test sets in a ratio of 4:1:1. Bounding boxes were used to crop the 2D CT images down to the area of interest. A DCNN model was built using 11 convolutional layers and trained on these images. The performance of the model was evaluated on the hold-out test set and compared with that of 3 radiologists who independently reviewed the images. RESULTS The DCNN model achieved an area under the receiver operating characteristic curve of 0.806 for differentiating acute and chronic NTM-LD nodules, corresponding to sensitivity, specificity, and accuracy of 76%, 68%, and 72%, respectively. The performance of the model was comparable to that of the 3 radiologists, who had area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of 0.693 to 0.771, 61% to 82%, 59% to 73%, and 60% to 73%, respectively. CONCLUSIONS This study demonstrated the feasibility of using a DCNN model for the classification of the activity of NTM-LD nodules on chest CT. The model performance was comparable to that of radiologists. This approach can potentially and efficiently improve the diagnosis and management of NTM-LD.
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Affiliation(s)
- Andrew C. Lancaster
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mitchell E. Cardin
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jan A. Nguyen
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tej I. Mehta
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dilek Oncel
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harrison X. Bai
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Keira A. Cohen
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Cheng Ting Lin
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Zhang S, He C, Wan Z, Shi N, Wang B, Liu X, Hou D. Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism. Med Biol Eng Comput 2024; 62:1589-1600. [PMID: 38319503 DOI: 10.1007/s11517-024-03022-1] [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] [Received: 08/18/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024]
Abstract
This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3D CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model's output, which can identify lesions in important parts of the lungs and make transparent decisions.
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Affiliation(s)
- Shidong Zhang
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
| | - Cong He
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China.
| | - Zhenzhen Wan
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
| | - Ning Shi
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
| | - Bing Wang
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
| | - Xiuling Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
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Yan Q, Zhao W, Kong H, Chi J, Dai Z, Yu D, Cui J. CT‑based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non‑tuberculous mycobacterium from pulmonary tuberculosis. Exp Ther Med 2024; 27:112. [PMID: 38361522 PMCID: PMC10867735 DOI: 10.3892/etm.2024.12400] [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: 06/28/2023] [Accepted: 11/02/2023] [Indexed: 02/17/2024] Open
Abstract
Global incidence rate of non-tuberculous mycobacteria (NTM) pulmonary disease has been increasing rapidly. In some countries and regions, its incidence rate is higher than that of tuberculosis. It is easily confused with tuberculosis. The topic of this study is to identify two diseases using CT radioomics. The aim in the present study was to investigate the value of CT-based radiomics to analyze consolidation features in differentiation of non-tuberculous mycobacteria (NTM) from pulmonary tuberculosis (TB). A total of 156 patients (75 with NTM pulmonary disease and 81 with TB) exhibiting consolidation characteristics in Shandong Public Health Clinical Center were retrospectively analyzed. Subsequently, 305 regions of interest of CT consolidation were outlined. Using a random number generated via a computer, 70 and 30% of consolidations were allocated to the training and the validation cohort, respectively. By means of variance threshold, when investigating the effective radiomics features, SelectKBest and the least absolute shrinkage and selection operator regression method were employed for feature selection and combined to calculate the radiomics score. K-nearest neighbor (KNN), support vector machine (SVM) and logistic regression (LR) were used to analyze effective radiomics features. A total of 18 patients with NTM pulmonary disease and 18 with TB possessing consolidation characteristics in Jinan Infectious Disease Hospital were collected for external validation of the model. A total of three methods was used in the selection of 52 optimal features. For KNN, the area under the curve (AUC; sensitivity, specificity) for the training and validation cohorts were 0.98 (0.93, 0.94) and 0.90 (0.88, 083), respectively; for SVM, AUC was 0.99 (0.96, 0.96) and 0.92 (0.86, 0.85) and for LR, AUC was 0.99 (0.97, 0.97) and 0.89 (0.88, 0.85). In the external validation cohort, AUC values of models were all >0.84 and LR classifier exhibited the most significant precision, recall and F1 score (0.87, 0.94 and 0.88, respectively). LR classifier possessed the best performance in differentiating diseases. Therefore, CT-based radiomics analysis of consolidation features may distinguish NTM pulmonary disease from TB.
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Affiliation(s)
- Qinghu Yan
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Wenlong Zhao
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Haili Kong
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Jingyu Chi
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Zhengjun Dai
- Huiying Medical Technology (Beijing) Co., Ltd., Beijing 100192, P.R. China
| | - Dexin Yu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China
| | - Jia Cui
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [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] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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Acharya V, Choi D, Yener B, Beamer G. Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2024; 12:17164-17194. [PMID: 38515959 PMCID: PMC10956573 DOI: 10.1109/access.2024.3359989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Tuberculosis (TB), primarily affecting the lungs, is caused by the bacterium Mycobacterium tuberculosis and poses a significant health risk. Detecting acid-fast bacilli (AFB) in stained samples is critical for TB diagnosis. Whole Slide (WS) Imaging allows for digitally examining these stained samples. However, current deep-learning approaches to analyzing large-sized whole slide images (WSIs) often employ patch-wise analysis, potentially missing the complex spatial patterns observed in the granuloma essential for accurate TB classification. To address this limitation, we propose an approach that models cell characteristics and interactions as a graph, capturing both cell-level information and the overall tissue micro-architecture. This method differs from the strategies in related cell graph-based works that rely on edge thresholds based on sparsity/density in cell graph construction, emphasizing a biologically informed threshold determination instead. We introduce a cell graph-based jumping knowledge neural network (CG-JKNN) that operates on the cell graphs where the edge thresholds are selected based on the length of the mycobacteria's cords and the activated macrophage nucleus's size to reflect the actual biological interactions observed in the tissue. The primary process involves training a Convolutional Neural Network (CNN) to segment AFBs and macrophage nuclei, followed by converting large (42831*41159 pixels) lung histology images into cell graphs where an activated macrophage nucleus/AFB represents each node within the graph and their interactions are denoted as edges. To enhance the interpretability of our model, we employ Integrated Gradients and Shapely Additive Explanations (SHAP). Our analysis incorporated a combination of 33 graph metrics and 20 cell morphology features. In terms of traditional machine learning models, Extreme Gradient Boosting (XGBoost) was the best performer, achieving an F1 score of 0.9813 and an Area under the Precision-Recall Curve (AUPRC) of 0.9848 on the test set. Among graph-based models, our CG-JKNN was the top performer, attaining an F1 score of 0.9549 and an AUPRC of 0.9846 on the held-out test set. The integration of graph-based and morphological features proved highly effective, with CG-JKNN and XGBoost showing promising results in classifying instances into AFB and activated macrophage nucleus. The features identified as significant by our models closely align with the criteria used by pathologists in practice, highlighting the clinical applicability of our approach. Future work will explore knowledge distillation techniques and graph-level classification into distinct TB progression categories.
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Affiliation(s)
| | - Diana Choi
- Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA 02155, USA
| | - BüLENT Yener
- Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Gillian Beamer
- Research Pathology, Aiforia Technologies, Cambridge, MA 02142, USA
- Texas Biomedical Research Institute, San Antonio, TX 78227, USA
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Gao Y, Zhang Y, Hu C, He P, Fu J, Lin F, Liu K, Fu X, Liu R, Sun J, Chen F, Yang W, Zhou Y. Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning. Front Public Health 2023; 11:1247141. [PMID: 38089031 PMCID: PMC10711219 DOI: 10.3389/fpubh.2023.1247141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction This study aimed to develop and assess a deep-learning model based on CT images for distinguishing infectivity in patients with pulmonary tuberculosis (PTB). Methods We labeled all 925 patients from four centers with weak and strong infectivity based on multiple sputum smears within a month for our deep-learning model named TBINet's training. We compared TBINet's performance in identifying infectious patients to that of the conventional 3D ResNet model. For model explainability, we used gradient-weighted class activation mapping (Grad-CAM) technology to identify the site of lesion activation in the CT images. Results The TBINet model demonstrated superior performance with an area under the curve (AUC) of 0.819 and 0.753 on the validation and external test sets, respectively, compared to existing deep learning methods. Furthermore, using Grad-CAM, we observed that CT images with higher levels of consolidation, voids, upper lobe involvement, and enlarged lymph nodes were more likely to come from patients with highly infectious forms of PTB. Conclusion Our study proves the feasibility of using CT images to identify the infectivity of PTB patients based on the deep learning method.
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Affiliation(s)
- Yi Gao
- Department of Infectious Disease and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Infectious Disease, Hainan General Hospital, Hainan Medical University, Haikou, China
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yiwen Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Chengguang Hu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Pengyuan He
- Department of Infectious Disease, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Jian Fu
- Department of Infectious Disease, Hainan General Hospital, Hainan Medical University, Haikou, China
| | - Feng Lin
- Department of Infectious Disease, Hainan General Hospital, Hainan Medical University, Haikou, China
| | - Kehui Liu
- Department of Radiology, Haikou Municipal People's Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou, China
| | - Xianxian Fu
- Clinical Lab, Haikou Municipal People's Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou, China
| | - Rui Liu
- Department of Infectious Disease, The Second Affiliated Hospital, Hainan Medical University, Haikou, China
| | - Jiarun Sun
- Department of Infectious Disease and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital, Hainan Medical University, Haikou, China
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yuanping Zhou
- Department of Infectious Disease and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Alamgeer M, Alruwais N, Alshahrani HM, Mohamed A, Assiri M. Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification. Cancers (Basel) 2023; 15:3982. [PMID: 37568800 PMCID: PMC10417684 DOI: 10.3390/cancers15153982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/27/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.
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Affiliation(s)
- Mohammad Alamgeer
- Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha 61421, Saudi Arabia
| | - Nuha Alruwais
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, Riyadh 11495, Saudi Arabia;
| | - Haya Mesfer Alshahrani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Abdullah Mohamed
- Research Centre, Future University in Egypt, New Cairo 11845, Egypt;
| | - Mohammed Assiri
- Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Aflaj 16273, Saudi Arabia;
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Naidoo J, Shelmerdine SC, -Charcape CFU, Sodhi AS. Artificial Intelligence in Paediatric Tuberculosis. Pediatr Radiol 2023; 53:1733-1745. [PMID: 36707428 PMCID: PMC9883137 DOI: 10.1007/s00247-023-05606-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/07/2022] [Accepted: 01/13/2023] [Indexed: 01/29/2023]
Abstract
Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus pandemic, which has disrupted the "End TB Strategy" and framework set out by the World Health Organization (WHO). Since the inception of artificial intelligence (AI) more than 60 years ago, the interest in AI has risen and more recently we have seen the emergence of multiple real-world applications, many of which relate to medical imaging. Nonetheless, real-world AI applications and clinical studies are limited in the niche area of paediatric imaging. This review article will focus on how AI, or more specifically deep learning, can be applied to TB diagnosis and management in children. We describe how deep learning can be utilised in chest imaging to provide computer-assisted diagnosis to augment workflow and screening efforts. We also review examples of recent AI applications for TB screening in resource constrained environments and we explore some of the challenges and the future directions of AI in paediatric TB.
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Affiliation(s)
- Jaishree Naidoo
- Envisionit Deep AI LTD, Coveham House, Downside Bridge Road, Cobham, KT11 3 EP, UK.
| | - Susan Cheng Shelmerdine
- Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
- Great Ormond Street Hospital for Children, UCL Great Ormond Street Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
| | - Carlos F Ugas -Charcape
- Department of Diagnostic Imaging, Instituto Nacional de Salud del Niño San Borja, Lima, Peru
| | - Arhanjit Singh Sodhi
- Department of Computer Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
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Liu CJ, Tsai CC, Kuo LC, Kuo PC, Lee MR, Wang JY, Ko JC, Shih JY, Wang HC, Yu CJ. A deep learning model using chest X-ray for identifying TB and NTM-LD patients: a cross-sectional study. Insights Imaging 2023; 14:67. [PMID: 37060419 PMCID: PMC10105818 DOI: 10.1186/s13244-023-01395-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 02/19/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Timely differentiating between pulmonary tuberculosis (TB) and nontuberculous mycobacterial lung disease (NTM-LD), which are radiographically similar, is important because infectiousness and treatment differ. This study aimed to evaluate whether artificial intelligence could distinguish between TB or NTM-LD patients by chest X-rays (CXRs) from suspects of mycobacterial lung disease. METHODS A total of 1500 CXRs, including 500 each from patients with pulmonary TB, NTM-LD, and patients with clinical suspicion but negative mycobacterial culture (Imitator) from two hospitals, were retrospectively collected and evaluated in this study. We developed a deep neural network (DNN) and evaluated model performance using the area under the receiver operating characteristic curves (AUC) in both internal and external test sets. Furthermore, we conducted a reader study and tested our model under three scenarios of different mycobacteria prevalence. RESULTS Among the internal and external test sets, the AUCs of our DNN model were 0.83 ± 0.005 and 0.76 ± 0.006 for pulmonary TB, 0.86 ± 0.006 and 0.64 ± 0.017 for NTM-LD, and 0.77 ± 0.007 and 0.74 ± 0.005 for Imitator. The DNN model showed higher performance on the internal test set in classification accuracy (66.5 ± 2.5%) than senior (50.8 ± 3.0%, p < 0.001) and junior pulmonologists (47.5 ± 2.8%, p < 0.001). Among different prevalence scenarios, the DNN model has stable performance in terms of AUC to detect TB and mycobacterial lung disease. CONCLUSION DNN model had satisfactory performance and a higher accuracy than pulmonologists on classifying patients with presumptive mycobacterial lung diseases. DNN model could be a complementary first-line screening tool.
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Affiliation(s)
- Chia-Jung Liu
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Cheng Che Tsai
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lu-Cheng Kuo
- Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan
| | - Po-Chih Kuo
- Department of Computer Science, National Tsing Hua University, No. 101, Kuang Fu Rd, Sec.2, Hsinchu, 300044, Taiwan.
| | - Meng-Rui Lee
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu, Taiwan.
- Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan.
| | - Jann-Yuan Wang
- Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan
| | - Jen-Chung Ko
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu, Taiwan
| | - Jin-Yuan Shih
- Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan
| | - Hao-Chien Wang
- Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan
- Department of Medicine, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Chong-Jen Yu
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan
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Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study. EClinicalMedicine 2023; 58:101913. [PMID: 36969336 PMCID: PMC10034267 DOI: 10.1016/j.eclinm.2023.101913] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 03/19/2023] Open
Abstract
Background Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow. Methods A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. A fully automated pipeline system (FAPS) using RefineNet and the Xception + Pyramid pooling module (PPM) was developed to perform the segmentation and classification of breast lesions. The performances of six radiologists and adjustments in Breast Imaging Reporting and Data System (BI-RADS) category 4 under the FAPS-assisted strategy were explored in pooled external and prospective testing sets. The segmentation performance was assessed using the Dice similarity coefficient (DSC), and the classification was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The radiologists' reading time was recorded for comparison with the FAPS. This trial is registered with China Clinical Trial Registration Centre (ChiCTR2200063444). Findings The FAPS-based segmentation task achieved DSCs of 0.888 ± 0.101, 0.820 ± 0.148 and 0.837 ± 0.132 in the internal, pooled external and prospective testing sets, respectively. For the classification task, the FAPS achieved AUCs of 0.947 (95% confidence interval [CI]: 0.916-0.978), 0.940 (95% [CI]: 0.894-0.987) and 0.891 (95% [CI]: 0.816-0.945). It outperformed radiologists in terms of classification efficiency based on single lesions (6 s vs 3 min). Moreover, the FAPS-assisted strategy improved the performance of radiologists. BI-RADS category 4 in 12.4% and 13.3% of patients was adjusted in two testing sets with the assistance of FAPS, which may play an important guiding role in the selection of clinical management strategies. Interpretation The FAPS based on CEM demonstrated the potential for the segmentation and classification of breast lesions, and had good generalisation ability and clinical applicability. Funding This study was supported by the Taishan Scholar Foundation of Shandong Province of China (tsqn202211378), National Natural Science Foundation of China (82001775), Natural Science Foundation of Shandong Province of China (ZR2021MH120), and Special Fund for Breast Disease Research of Shandong Medical Association (YXH2021ZX055).
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Yoon I, Hong JH, Witanto JN, Yim JJ, Kwak N, Goo JM, Yoon SH. Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantification. Quant Imaging Med Surg 2023; 13:747-762. [PMID: 36819253 PMCID: PMC9929398 DOI: 10.21037/qims-22-620] [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: 06/27/2022] [Accepted: 11/21/2022] [Indexed: 01/05/2023]
Abstract
Background This study aimed (I) to investigate the clinical implication of computed tomography (CT) cavity volume in tuberculosis (TB) and non-tuberculous mycobacterial pulmonary disease (NTM-PD), and (II) to develop a three-dimensional (3D) nnU-Net model to automatically detect and quantify cavity volume on CT images. Methods We retrospectively included conveniently sampled 206 TB and 186 NTM-PD patients in a tertiary referral hospital, who underwent thin-section chest CT scans from 2012 through 2019. TB was microbiologically confirmed, and NTM-PD was diagnosed by 2007 Infectious Diseases Society of America/American Thoracic Society guideline. The reference cavities were semi-automatically segmented on CT images and a 3D nnU-Net model was built with 298 cases (240 cases for training, 20 for tuning, and 38 for internal validation). Receiver operating characteristic curves were used to evaluate the accuracy of the CT cavity volume for two clinically relevant parameters: sputum smear positivity in TB and treatment in NTM-PD. The sensitivity and false-positive rate were calculated to assess the cavity detection of nnU-Net using radiologist-detected cavities as references, and the intraclass correlation coefficient (ICC) between the reference and the U-Net-derived cavity volumes was analyzed. Results The mean CT cavity volumes in TB and NTM-PD patients were 11.3 and 16.4 cm3, respectively, and were significantly greater in smear-positive TB (P<0.001) and NTM-PD necessitating treatment (P=0.020). The CT cavity volume provided areas under the curve of 0.701 [95% confidence interval (CI): 0.620-0.782] for TB sputum positivity and 0.834 (95% CI: 0.773-0.894) for necessity of NTM-PD treatment. The nnU-Net provided per-patient sensitivity of 100% (19/19) and per-lesion sensitivity of 83.7% (41/49) in the validation dataset, with an average of 0.47 false-positive small cavities per patient (median volume, 0.26 cm3). The mean Dice similarity coefficient between the manually segmented cavities and the U-Net-derived cavities was 78.9. The ICCs between the reference and U-Net-derived volumes were 0.991 (95% CI: 0.983-0.995) and 0.933 (95% CI: 0.897-0.957) on a per-patient and per-lesion basis, respectively. Conclusions CT cavity volume was associated with sputum positivity in TB and necessity of treatment in NTM-PD. The 3D nnU-Net model could automatically detect and quantify mycobacterial cavities on chest CT, helping assess TB infectivity and initiate NTM-TB treatment.
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Affiliation(s)
- Ieun Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Jung Hee Hong
- Department of Radiology, Keimyung University Dongsan Medical Center, Daegu, Korea
| | | | - Jae-Joon Yim
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Nakwon Kwak
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
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Distinguishing nontuberculous mycobacterial lung disease and Mycobacterium tuberculosis lung disease on X-ray images using deep transfer learning. BMC Infect Dis 2023; 23:32. [PMID: 36658559 PMCID: PMC9854086 DOI: 10.1186/s12879-023-07996-5] [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] [Received: 09/23/2022] [Accepted: 01/09/2023] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Nontuberculous mycobacterial lung disease (NTM-LD) and Mycobacterium tuberculosis lung disease (MTB-LD) have similar clinical characteristics. Therefore, NTM-LD is sometimes incorrectly diagnosed with MTB-LD and treated incorrectly. To solve these difficulties, we aimed to distinguish the two diseases in chest X-ray images using deep learning technology, which has been used in various fields recently. METHODS We retrospectively collected chest X-ray images from 3314 patients infected with Mycobacterium tuberculosis (MTB) or nontuberculosis mycobacterium (NTM). After selecting the data according to the diagnostic criteria, various experiments were conducted to create the optimal deep learning model. A performance comparison was performed with the radiologist. Additionally, the model performance was verified using newly collected MTB-LD and NTM-LD patient data. RESULTS Among the implemented deep learning models, the ensemble model combining EfficientNet B4 and ResNet 50 performed the best in the test data. Also, the ensemble model outperformed the radiologist on all evaluation metrics. In addition, the accuracy of the ensemble model was 0.85 for MTB-LD and 0.78 for NTM-LD on an additional validation dataset consisting of newly collected patients. CONCLUSIONS In previous studies, it was known that it was difficult to distinguish between MTB-LD and NTM-LD in chest X-ray images, but we have successfully distinguished the two diseases using deep learning methods. This study has the potential to aid clinical decisions if the two diseases need to be differentiated.
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Han D, Chen Y, Li X, Li W, Zhang X, He T, Yu Y, Dou Y, Duan H, Yu N. Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia. LA RADIOLOGIA MEDICA 2023; 128:68-80. [PMID: 36574111 PMCID: PMC9793822 DOI: 10.1007/s11547-022-01580-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 12/13/2022] [Indexed: 12/29/2022]
Abstract
PURPOSE To develop and validate a 3D-convolutional neural network (3D-CNN) model based on chest CT for differentiating active pulmonary tuberculosis (APTB) from community-acquired pneumonia (CAP). MATERIALS AND METHODS Chest CT images of APTB and CAP patients diagnosed in two imaging centers (n = 432 in center A and n = 61 in center B) were collected retrospectively. The data in center A were divided into training, validation and internal test sets, and the data in center B were used as an external test set. A 3D-CNN was built using Keras deep learning framework. After the training, the 3D-CNN selected the model with the highest accuracy in the validation set as the optimal model, which was applied to the two test sets in centers A and B. In addition, the two test sets were independently diagnosed by two radiologists. The 3D-CNN optimal model was compared with the discrimination, calibration and net benefit of the two radiologists in differentiating APTB from CAP using chest CT images. RESULTS The accuracy of the 3D-CNN optimal model was 0.989 and 0.934 with the internal and external test set, respectively. The area-under-the-curve values with the 3D-CNN model in the two test sets were statistically higher than that of the two radiologists (all P < 0.05), and there was a high calibration degree. The decision curve analysis showed that the 3D-CNN optimal model had significantly higher net benefit for patients than the two radiologists. CONCLUSIONS 3D-CNN has high classification performance in differentiating APTB from CAP using chest CT images. The application of 3D-CNN provides a new automatic and rapid diagnosis method for identifying patients with APTB from CAP using chest CT images.
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Affiliation(s)
- Dong Han
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China
| | - Yibing Chen
- School of Information Science & Technology, Northwest University, Xi’an, 710127 Shaanxi China
| | - Xuechao Li
- Clinical Research Center, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Wen Li
- Department of Radiology, Baoji Central Hospital, Baoji, 721008 China
| | - Xirong Zhang
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China ,College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Taiping He
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China ,College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Yong Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China ,College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Yuequn Dou
- Respiratory Department, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Haifeng Duan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China
| | - Nan Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000, China.
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15
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Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis. J Clin Med 2022; 12:jcm12010303. [PMID: 36615102 PMCID: PMC9820940 DOI: 10.3390/jcm12010303] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/21/2022] [Accepted: 12/24/2022] [Indexed: 01/03/2023] Open
Abstract
Tuberculosis (TB) remains one of the leading causes of death among infectious diseases worldwide. Early screening and diagnosis of pulmonary tuberculosis (PTB) is crucial in TB control, and tend to benefit from artificial intelligence. Here, we aimed to evaluate the diagnostic efficacy of a variety of artificial intelligence methods in medical imaging for PTB. We searched MEDLINE and Embase with the OVID platform to identify trials published update to November 2022 that evaluated the effectiveness of artificial-intelligence-based software in medical imaging of patients with PTB. After data extraction, the quality of studies was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using a bivariate random-effects model. In total, 3987 references were initially identified and 61 studies were finally included, covering a wide range of 124,959 individuals. The pooled sensitivity and the specificity were 91% (95% confidence interval (CI), 89-93%) and 65% (54-75%), respectively, in clinical trials, and 94% (89-96%) and 95% (91-97%), respectively, in model-development studies. These findings have demonstrated that artificial-intelligence-based software could serve as an accurate tool to diagnose PTB in medical imaging. However, standardized reporting guidance regarding AI-specific trials and multicenter clinical trials is urgently needed to truly transform this cutting-edge technology into clinical practice.
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Nijiati M, Zhou R, Damaola M, Hu C, Li L, Qian B, Abulizi A, Kaisaier A, Cai C, Li H, Zou X. Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis. Front Mol Biosci 2022; 9:1086047. [PMID: 36545511 PMCID: PMC9760807 DOI: 10.3389/fmolb.2022.1086047] [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] [Received: 11/01/2022] [Accepted: 11/24/2022] [Indexed: 12/08/2022] Open
Abstract
Active pulmonary tuberculosis (ATB), which is more infectious and has a higher mortality rate compared with non-active pulmonary tuberculosis (non-ATB), needs to be diagnosed accurately and timely to prevent the tuberculosis from spreading and causing deaths. However, traditional differential diagnosis methods of active pulmonary tuberculosis involve bacteriological testing, sputum culturing and radiological images reading, which is time consuming and labour intensive. Therefore, an artificial intelligence model for ATB differential diagnosis would offer great assistance in clinical practice. In this study, computer tomography (CT) scans images and corresponding clinical information of 1160 ATB patients and 1131 patients with non-ATB were collected and divided into training, validation, and testing sets. A 3-dimension (3D) Nested UNet model was utilized to delineate lung field regions in the CT images, and three different pre-trained deep learning models including 3D VGG-16, 3D EfficientNet and 3D ResNet-50 were used for classification and differential diagnosis task. We also collected an external testing set with 100 ATB cases and 100 Non-ATB cases for further validation of the model. In the internal and external testing set, the 3D ResNet-50 model outperformed other models, reaching an AUC of 0.961 and 0.946, respectively. The 3D ResNet-50 model reached even higher levels of diagnostic accuracy than experienced radiologists, while the CT images reading and diagnosing speed was 10 times faster than human experts. The model was also capable of visualizing clinician interpretable lung lesion regions important for differential diagnosis, making it a powerful tool assisting ATB diagnosis. In conclusion, we developed an auxiliary tool to differentiate active and non-active pulmonary tuberculosis, which would have broad prospects in the bedside.
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Affiliation(s)
- Mayidili Nijiati
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, China
| | - Renbing Zhou
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, China
| | - Miriguli Damaola
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, China
| | - Chuling Hu
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Li Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | | | | | | | - Chao Cai
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Hongjun Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China,*Correspondence: Hongjun Li, ; Xiaoguang Zou,
| | - Xiaoguang Zou
- Clinical Medical Research Center, The First People’s Hospital of Kashi Prefecture, Kashi, China,*Correspondence: Hongjun Li, ; Xiaoguang Zou,
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Ying C, Li X, Lv S, Du P, Chen Y, Fu H, Du W, Xu K, Zhang Y, Wu W. T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis. Int J Infect Dis 2022; 125:42-50. [PMID: 36180035 DOI: 10.1016/j.ijid.2022.09.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/21/2022] [Accepted: 09/21/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) for early differential diagnosis of nontuberculous mycobacteria pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB). METHODS A total of 1049 cases were enrolled, including 467 NTM-PD and 582 PTB cases. A total of 320 cases (160 NTM-PD and 160 PTB) were randomized as the testing set and were analyzed using T-SPOT combined with the DL model. The testing cases were first divided into T-SPOT-positive and -negative groups, and the DL model was then used to separate the cases into four subgroups further. RESULTS The precision was found to be 91.7% for the subgroup of T-SPOT-negative and DL classified as NTM-PD, and 89.8% for T-SPOT-positive and DL classified as PTB, which covered 66.9% of the total cases, compared with the accuracy rate of 80.3% of T-SPOT alone. In the other two remaining groups, where the T-SPOT prediction was inconsistent with the DL model, the accuracy was 73.0% and 52.2%, separately. CONCLUSION Our study shows that the new diagnostic system combining T-SPOT with DL based computed tomography image analysis can greatly improve the classification precision of NTM-PD and PTB when the two methods of prediction are consistent.
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Affiliation(s)
- Chiqing Ying
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Xukun Li
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, China
| | - Shuangzhi Lv
- Radiology Department, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Peng Du
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, China
| | - Yunzhi Chen
- School of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou, China
| | - Hongxin Fu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Weibo Du
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Kaijin Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Ying Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Wei Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
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18
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Liang S, Ma J, Wang G, Shao J, Li J, Deng H, Wang C, Li W. The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis. Front Med (Lausanne) 2022; 9:935080. [PMID: 35966878 PMCID: PMC9366014 DOI: 10.3389/fmed.2022.935080] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/13/2022] [Indexed: 11/30/2022] Open
Abstract
With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.
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Affiliation(s)
- Shufan Liang
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
- Precision Medicine Key Laboratory of Sichuan Province, Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jiechao Ma
- AI Lab, Deepwise Healthcare, Beijing, China
| | - Gang Wang
- Precision Medicine Key Laboratory of Sichuan Province, Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jun Shao
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jingwei Li
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hui Deng
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
- Precision Medicine Key Laboratory of Sichuan Province, Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Hui Deng,
| | - Chengdi Wang
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
- Chengdi Wang,
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
- Weimin Li,
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Yan Q, Wang W, Zhao W, Zuo L, Wang D, Chai X, Cui J. Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics. BMC Pulm Med 2022; 22:4. [PMID: 34991543 PMCID: PMC8740493 DOI: 10.1186/s12890-021-01766-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 11/24/2021] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE To differentiate nontuberculous mycobacteria (NTM) pulmonary diseases from pulmonary tuberculosis (PTB) by analyzing the CT radiomics features of their cavity. METHODS 73 patients of NTM pulmonary diseases and 69 patients of PTB with the cavity in Shandong Province Chest Hospital and Qilu Hospital of Shandong University were retrospectively analyzed. 20 patients of NTM pulmonary diseases and 20 patients of PTB with the cavity in Jinan Infectious Disease Hospitall were collected for external validation of the model. 379 cavities as the region of interesting (ROI) from chest CT images were performed by 2 experienced radiologists. 80% of cavities were allocated to the training set and 20% to the validation set using a random number generated by a computer. 1409 radiomics features extracted from the Huiying Radcloud platform were used to analyze the two kinds of diseases' CT cavity characteristics. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) methods, and six supervised learning classifiers (KNN, SVM, XGBoost, RF, LR, and DT models) were used to analyze the features. RESULTS 29 optimal features were selected by the variance threshold method, K best method, and Lasso algorithm.and the ROC curve values are obtained. In the training set, the AUC values of the six models were all greater than 0.97, 95% CI were 0.95-1.00, the sensitivity was greater than 0.92, and the specificity was greater than 0.92. In the validation set, the AUC values of the six models were all greater than 0.84, 95% CI were 0.76-1.00, the sensitivity was greater than 0.79, and the specificity was greater than 0.79. In the external validation set, The AUC values of the six models were all greater than 0.84, LR classifier has the highest precision, recall and F1-score, which were 0.92, 0.94, 0.93. CONCLUSION The radiomics features extracted from cavity on CT images can provide effective proof in distinguishing the NTM pulmonary disease from PTB, and the radiomics analysis shows a more accurate diagnosis than the radiologists. Among the six classifiers, LR classifier has the best performance in identifying two diseases.
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Affiliation(s)
- Qinghu Yan
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250013, China
| | - Wuzhang Wang
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250013, China
| | - Wenlong Zhao
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250013, China
| | - Liping Zuo
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Dongdong Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Xiangfei Chai
- Huiying Medical Technology (Beijing) Co., Ltd, Beijing, 100192, China
| | - Jia Cui
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250013, China.
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