Predicting multi-drug resistant tuberculosis using machine learning on genomic and clinical data [0.03%]
基于基因组和临床数据的机器学习预测多重耐药结核病
Komal Saxena,S Shyni Carmel Mary,Prolay Ghosh et al.
Komal Saxena et al.
Background: Tuberculosis (TB) is still the largest cause of death in the world, especially in countries with low and medium incomes. Multi-Drug Resistant Tuberculosis (MDR-TB), which is resistant to isoniazid and rifampic...
Multi-modal AI approach for Early Tuberculosis Detection by combining symptom, imaging, and clinical data [0.03%]
结合症状、影像和临床数据的多模态AI方法实现早期结核病检测
Rahul Bhagwat Mapari,Asif Ibrahim Tamboli,Prachi Tamhan et al.
Rahul Bhagwat Mapari et al.
Background: Tuberculosis (TB) is still a major health problem around the world, especially in low- and middle-income countries, where it kills about 1.5 million people every year. Early and accurate identification is very...
NLP-driven analysis of electronic health records for early Identification of tuberculosis cases [0.03%]
基于NLP的电子健康档案分析在肺结核病例早期识别中的应用
Araddhana Arvind Deshmukh,Abhijit Chitre,Vina M Lomte et al.
Araddhana Arvind Deshmukh et al.
Tuberculosis (TB) is still one of the biggest health problems in the world. Each year, millions of people get it, and because diagnosis are often delayed, the disease spreads and kills more people. Radiographic examination and sputum smear ...
Advancing automated tuberculosis detection in chest radiographs through stable policy optimization with clipped objectives [0.03%]
具有裁剪目标的稳定策略优化在胸部X光片中推进自动化结核病检测
Anup Gade,Amol Bhoite,Bharati P Vasgi et al.
Anup Gade et al.
Automated tuberculosis detection from chest radiographs is a fundamentally hard problem that arises from the imbalanced nature of data and weight of clinical misclassifications. In this work, we propose a novel optimization method named cli...
Addressing diagnostic resource imbalance in pulmonary tuberculosis detection from chest radiographs through cost-aware learning [0.03%]
基于成本感知学习解决胸部X光片中肺结核检测的诊断资源不平衡问题
Dr Nidhi Ranjan,Dr Balasaheb Balkhande,Lata Tembhare et al.
Dr Nidhi Ranjan et al.
Automated early detection of pulmonary tuberculosis using chest radiographs is hampered by extreme class imbalance, resulting in poor performance across real-world settings where the clinical and operational cost of missed TB cases outstrip...
Intrinsic motivation-based exploration for enhancing tuberculosis lesion discovery in sparse annotation chest X-ray datasets [0.03%]
基于内在动机的探索，利用稀疏标注的数据集增强胸部X光片中肺结核病灶的发现能力
Vaishali Niranjane,Lakhwinder Kaur,Anant More et al.
Vaishali Niranjane et al.
The detection of tuberculosis lesions in chest X-rays is increasingly critical but remains hindered by sparse annotations and significant class imbalance within medical imaging datasets. This study addresses these limitations by proposing a...
AI-powered risk prediction of tuberculosis reactivation in latently infected individuals [0.03%]
利用人工智能预测潜伏结核感染再激活风险
Mohammed Eltahir Abdelhag,Husham E Homeida,Ogail Yousif Dawod et al.
Mohammed Eltahir Abdelhag et al.
Background: A quarter of the world's population is latently infected with Mycobacterium tuberculosis, making it a huge health issue. In spite of not having signs, many of these humans are liable to reactivation and TB, es...
Hybrid deep learning system combining radiological and clinical data for improved tuberculosis diagnosis [0.03%]
结合放射学和临床数据的混合深度学习系统用于改进结核病诊断
Navnath B Pokale,Anjali Shrivastav,Kanchan K Doke et al.
Navnath B Pokale et al.
Tuberculosis (TB) remains among the leading causes of infectious mortality. Diagnosis is challenging because imaging and clinical data must be interpreted in tandem. Chest X-rays and computed tomography scans provide screening but require e...
Enhancing tuberculosis CT imaging analysis through synthetic data augmentation via deep adversarial models [0.03%]
基于深度对抗模型的合成数据增强在提升结核病CT影像分析中的应用研究
Vaishali Sandeep Baste,Padmavati Shrivastava,Rahul Patil et al.
Vaishali Sandeep Baste et al.
Even in resource-limited settings, such as central Africa or India, where the burden of tuberculosis remains great [23,39], there are very few datasets that have such annotations. Objective This study aims to address the challenge associate...
CNN-based image recognition of Acid-Fast Bacilli in sputum smears for enhanced tuberculosis diagnosis [0.03%]
基于CNN的痰涂片中酸性分枝菌体图像识别以增强结核病诊断
Chandrakant Kokane,Neeta A Deshpande,Nitin Dhawas et al.
Chandrakant Kokane et al.
Background: Tuberculosis (TB) is still one of the deadliest infectious illnesses in the world, especially in countries with low or middle income. An early and correct evaluation is very important for treating diseases eff...