Deep Learning to Determine the Activity of Pulmonary Tuberculosis on                     Chest Radiographs

Lee, Seowoo; Yim, Jae-Joon; Kwak, Nakwon; Lee, Yeon Joo; Lee, Jung Kyu; Lee, Ji Yeon; Kim, Ju Sang; Kang, Young Ae; Jeon, Doo Soo; Jang, Mijung; Goo, Jin Mo; Yoon, Soon Ho

doi:10.1148/radiol.2021210063

Cited by 30 publications

(20 citation statements)

References 17 publications

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“…Basic clinical data and clinical manifestations of all the enrolled patients were collected for further investigation. Patients of active pulmonary tuberculosis were previously reported to be older ( Perez-Guzman et al, 1999 ; Li et al, 2017 ) than those with non-active pulmonary tuberculosis and more likely to have symptoms such as cough ( Alavi et al, 2014 ) and chest pain ( Kwon et al, 2013 ; Lee et al, 2021 ), which was consistent with the findings of our study. However, there was no difference in gender between active and non-active pulmonary tuberculosis patients in this study, which agreed with the findings of Kim et al (2014) and Wang et al (2021) .…”

Section: Discussionsupporting

confidence: 92%

Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis

Nijiati¹,

Zhou²,

Damaola³

et al. 2022

Front. Mol. Biosci.

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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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Section: Discussionsupporting

confidence: 92%

Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis

Nijiati¹,

Zhou²,

Damaola³

et al. 2022

Front. Mol. Biosci.

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“…Deep learning, a major branch of AI, 36 is experiencing an era of explosive growth, constituting a breakthrough in medical image classification tasks by mining the association between raw input visual data and desired output, generating decisions ranging from macroscopic disease diagnosis and outcome prediction to microscopic gene mutation status 37–49 . Along with the vigorous evolution of AI techniques, medical image‐based computational approaches have been proposed for TB, showing comparable performance to radiologists in disease assessment 15–20 . And efficiency of software for automated TB diagnosis has been validated, offering novel insights for TB management 50,51 .…”

Section: Discussionmentioning

confidence: 99%

“…14 Currently, image-based artificial intelligence (AI) systems have been proposed for the detection and activity assessment of TB, achieving, or even surpassing the performance of human physicians. [15][16][17][18][19][20] For automated diagnosis of DR-TB, chest X-ray (CXR), a two-dimensional projection consisting of overlapped anatomical structures, was commonly utilized, whereas CT imaging, a three-dimensional (3D) reconstruction presenting higher diagnostic reliability, has been seldom reported. 21 Moreover, moderate performance and small-scale datasets hinder the practicality and generality of those models.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for precise diagnosis and subtype triage of drug‐resistant tuberculosis on chest computed tomography

Liang,

Xu,

Yang

et al. 2024

MedComm

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Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug‐resistant tuberculosis (DR‐TB) and drug‐sensitive tuberculosis (DS‐TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning‐based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR‐TB and classify crucial subtypes, including rifampicin‐resistant tuberculosis, multidrug‐resistant tuberculosis, and extensively drug‐resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR‐TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR‐TB diagnosis. Notably, the system demonstrated instructive value in DR‐TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human‐understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision‐making for DR‐TB.

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“…This includes two publicly available data sets maintained by the National Institutes of Health, which are from Montgomery County, Maryland, and Shenzhen, China. The other two data sets are from Thomas Jefferson University Hospital, Philadelphia, and the Belarus Tuberculosis Portal Two different DCNNs, Alex Net and Google Net, were used to classify the images as having manifestations of pulmonary TB or as healthy Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99 6 Lee JH et al [ 28 ] European Radiology (2021) 20,135 radiographs in 19,686 individuals Armed forces hospital, Seoul, South Korea Deep learning–based automated detection algorithms For the radiologically identifiable relevant abnormality, deep learning -based automated detection algorithms showed an AUC value of 0.967 (95% CI 0.938–0.996) with sensitivities of 0.821 and 0.679, specificities of 0.960 and 0.997, PPVs of 0.028 and 0.257, and NPVs of both 0.999 at high sensitivity and high specificity thresholds, respectively 7 Lee S et al [ 29 ] Radiology (2021) 6,654 pre- and post-treatment radiographs from 3,327 patients with pulmonary tuberculosis and 3,182 normal radiographs from as many patients Six Korean hospitals (hospitals A–F) Efficient Net, which was adopted as a base feature extractor. The network was built and trained by using open-source software (TensorFlow, version 1.11.0; Keras, version 2.2.4) In two test sets that included radiographs depicting active and healed tuberculosis (test set 1, n = 148; test set 2 subset, n = 200), a deep learning model ROCs, 0.83 and 0.84, respectively) differentiated active from healed tuberculosis on radiographs, with comparable performance to that of expert readers (AUCs, 0.69–0.80 [ P = 0.001 to P = 0.23] and 0.71–0.80 [ P = 0.001 to P = 0.08]) 8 Ma L et al [ 30 ] Journal of Xray Science and Technology (2020) A CT image data set of 846 patients was retrospectively collected Hospital of Hebei University of China A U-Net deep learning algorithm was applied for automatic detection and segmentation of Active TB lesions For an independent test, the AI tool yields an AUC value of 0.980.…”

Section: Artificial Intelligence For the Diagnosis Of Tuberculosis Fr...mentioning

confidence: 99%

Artificial Intelligence in Paediatric Tuberculosis

et al. 2023

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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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Deep Learning to Determine the Activity of Pulmonary Tuberculosis on Chest Radiographs

Cited by 30 publications

References 17 publications

Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis

Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis

Deep learning for precise diagnosis and subtype triage of drug‐resistant tuberculosis on chest computed tomography

Artificial Intelligence in Paediatric Tuberculosis

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