Automatic Diagnose of Drug-Resistance Tuberculosis from CT Images Based on Deep Neural Networks

Du, Qiuyu; Liang, Shufan; Guo, Jixiang; Zhang, Yi; Li, Weiming; Wang, Chengdi; Xu, Xiang

doi:10.1007/978-3-031-20500-2_21

Cited by 2 publications

(6 citation statements)

References 19 publications

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“…We have made progress for DR-TB diagnosis and subtype triage before. 58 And in this study, based on a dataset of CT volumes from more than 1000 patients, we further involved four deep learning architectures and compared their performance via various parameters. For model performance improvement, three strategies were adopted.…”

Section: Discussionmentioning

confidence: 99%

“…To a certain extent, the strategy applied in our study surmounts the abovementioned conundrums. We have made progress for DR‐TB diagnosis and subtype triage before 58 . And in this study, based on a dataset of CT volumes from more than 1000 patients, we further involved four deep learning architectures and compared their performance via various parameters.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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

Self Cite

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“…The usefulness of ML in TB diagnosis has been demonstrated by several studies. [29][30][31]45,46 Early diagnosis of TB is crucial in preventing advanced symptoms such as cough and shortness of breath, particularly in the elderly. 45 It is revealed in Reference 45 that some people can carry the TB bacteria but show no symptoms, and these people might infect others.…”

Section: Applications Of Machine Learning In Tuberculosismentioning

confidence: 99%

“…The usefulness of ML in TB diagnosis has been demonstrated by several studies 29–31,45,46 . Early diagnosis of TB is crucial in preventing advanced symptoms such as cough and shortness of breath, particularly in the elderly 45 .…”

Section: Related Workmentioning

confidence: 99%

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Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

Yilmaz

2024

Concurrency and Computation

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SummaryThe promising results of ML (machine learning) methods in various disciplines have led to the frequent use of these methods in health fields such as disease diagnosis, personalized medicine, medical image‐based diagnosis, and predicting the number of deaths and cases in a pandemic. However, a neglected area in the field of healthcare is the lack of study with ML to predict treatment outcomes for tuberculosis (TB) patients, particularly children experiencing failed treatment. This need has become more apparent as the coronavirus pandemic has reversed the gains of health institutions with TB disease, especially in children. Therefore, this article conducted a study using the stacked ensemble ML method to early predict the risk for children experiencing a failed treatment outcome of TB. To fulfill this need and determine the most appropriate technique, a two‐stage methodology was followed in this work. First, predictions were obtained by combining the information gain feature selection (IGFS) approach with a variety of single‐based ML algorithms, including logistic regression (LR), deep belief neural networks (DBN), random forest (RF), and decision tree (DT). Second, the proposed method, which includes a stacked ensemble ML technique, was used. The latter model uses LR as a meta‐learner and the aforementioned single‐based ML algorithms (DBN, LR, RF, and DT). The performance results of ML models used in the two stages were compared, and the proposed model which is the combination of the stack‐based ensemble learning model and the IGFS technique provided better ROC curves, accuracy, precision, and recall results.

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Automatic Diagnose of Drug-Resistance Tuberculosis from CT Images Based on Deep Neural Networks

Cited by 2 publications

References 19 publications

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

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

Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

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