Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest X-rays? An evaluation of five AI products for TB screening and triaging in a high TB burden setting
Artificial intelligence (AI) products can be trained to recognize tuberculosis (TB)-related abnormalities on chest radiographs. Various AI products are available commercially, yet there is lack of evidence on how their performance compared with each other and with radiologists. We evaluated five AI software products for screening and triaging TB using a large dataset that had not been used to train any commercial AI products. Individuals (>=15 years old) presenting to three TB screening centers in Dhaka, Bangladesh, were recruited consecutively. All CXR were read independently by a group of three Bangladeshi registered radiologists and five commercial AI products: CAD4TB (v7), InferReadDR (v2), Lunit INSIGHT CXR (v4.9.0), JF CXR-1 (v2), and qXR (v3). All five AI products significantly outperformed the Bangladeshi radiologists. The areas under the receiver operating characteristic curve are qXR: 90.81% (95% CI:90.33-91.29%), CAD4TB: 90.34% (95% CI:89.81-90.87), Lunit INSIGHT CXR: 88.61% (95% CI:88.03%-89.20%), InferReadDR: 84.90% (95% CI: 84.27-85.54%) and JF CXR-1: 84.89% (95% CI:84.26-85.53%). Only qXR met the TPP with 74.3% specificity at 90% sensitivity. Five AI algorithms can reduce the number of Xpert tests required by 50%, while maintaining a sensitivity above 90%. All AI algorithms performed worse among the older age and people with prior TB history. AI products can be highly accurate and useful screening and triage tools for TB detection in high burden regions and outperform human readers.
Code (0)
등록된 구현이 없습니다.
Tasks
SensitivitySpecificitySimilar Papers 제목 키워드 기반
Classification of Pneumonia and Tuberculosis from Chest X-rays
Artificial intelligence (AI) and specifically machine learning is making inroads into number of fields. Machine learning is replacing and/or complementing humans in a certain type of domain to make systems perform tasks …
BIG-bench Machine LearningClassificationGeneral ClassificationFew-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images
Tuberculosis (TB) is caused by the bacterium Mycobacterium tuberculosis, primarily affecting the lungs. Early detection is crucial for improving treatment effectiveness and reducing transmission risk. Artificial intellig…
ClassificationFew-Shot Learningimage-classificationImage ClassificationDesign of an Efficient, Ease-of-use and Affordable Artificial Intelligence based Nucleic Acid Amplification Diagnosis Technology for Tuberculosis and Multi-drug Resistant Tuberculosis
Current technologies that facilitate diagnosis for simultaneous detection of Mycobacterium tuberculosis and its resistance to first-line anti-tuberculosis drugs (Isoniazid and Rifampicim) are designed for lab-based setti…
An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection
Tuberculosis remains a critical global health issue, particularly in resource-limited and remote areas. Early detection is vital for treatment, yet the lack of skilled radiologists underscores the need for artificial int…
Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning
Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This si…
Decision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)