Detecting drug-resistant tuberculosis in chest radiographs

Jaeger, Stefan; Juarez-Espinosa, Octavio; Candemir, Sema; Poostchi, Mahdieh; Yang, Feng; Lewis, Kim; Ding, Meng; Folio, Les; Gabrielian, Andrei; Hurt, Darrell E.; Rosenthal, Alex; Thoma, George R.

doi:10.1007/s11548-018-1857-9

Cited by 44 publications

(43 citation statements)

References 30 publications

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“…Several studies were conducted using the radiological finding as a high-dimensional input (image) and predicted with complex neural network models such as Convolutional Neural Network. Nevertheless, it showed a lower performance [ 48 – 50 ]. Some pre-processing techniques and device settings affect the element of radiology image [ 51 ] including intensity, shape, and texture of the lesion portrayed in radiology film, despite digital image processing has been endorsed to tackle this issue.…”

Section: Discussionmentioning

confidence: 99%

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

et al. 2021

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Background and objectives Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluate the CUHAS-ROBUST model performance, an artificial-intelligence-based RR-TB screening tool. Methods A cross-sectional study involved suspected all type of RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment. Results A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields the highest values of accuracy (88% (95% CI 85–91)), and sensitivity (84% (95% CI 76–89)) compare to other models that show sensitivity below 80% (Logistic Regression 32%, Decision Tree 44%, Random Forest 25%, Extreme Gradient Boost 25%). However, this ANN has lower specificity among other models (90% (95% CI 86–93)) where Logistic Regression demonstrates the highest (99% (95% CI 97–99)). This ANN model was selected for the CUHAS-ROBUST application, although still lower than the sensitivity of global GeneXpert results (87.5%). Conclusion The ANN-CUHAS ROBUST outperforms other AI classifiers model in detecting all type of RR-TB, and by deploying into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level where the GeneXpert examination is not available. Trial registration NCT04208789.

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Section: Discussionmentioning

confidence: 99%

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

et al. 2021

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“…Signal processing methods are often used together with machine learning to automate the diagnosis of communicable diseases. Signal processing interventions focused specifically on the use of radiological data for tuberculosis 18,23 and drug-resistant tuberculosis, 19 ultrasound data for pneumonia, 24 micro scopy data for malaria, [25][26][27] and other biological sources of data for tuberculosis. [28][29][30] Most diagnostic interventions using AI in LMICs reported either high sensitivity, specificity, or high accuracy (>85% for all), or non-inferiority to comparator diagnostic tools.…”

Section: Ai-driven Interventions For Healthmentioning

confidence: 99%

“…Researchers applied machine learning and signal processing methods to digital chest radiographs to identify tuberculosis cases 18 and drug-resistant tuberculosis cases 19 Mortality and morbidity risk assessment Data mining; machine learning; signal processing…”

Section: Diagnosismentioning

confidence: 99%

Artificial intelligence and the future of global health

Schwalbe¹,

2020

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Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.

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“…AI can also assist in performing feats that have not been accomplished by human visual perception alone. Jaeger et al (20) developed a deep learning model with the aim of classifying drug-resistant and drug-sensitive TB directly from the radiographic appearance. The authors used a relatively small image dataset of 135 images, of which 45% were TB sensitive cases and 54% were multidrug resistant cases.…”

Section: Deep Learningmentioning

confidence: 99%