Artificial Intelligence Assisting the Early Detection of Active Pulmonary Tuberculosis From Chest X-Rays: A Population-Based Study

Article Medicine, General & Internal

Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China

Yanling Zheng et al.

Summary: The study focused on predicting tuberculosis incidence in Kashgar, China using a Box-Jenkins method and an AR-Elman hybrid method. The AR-Elman hybrid model showed better prediction accuracy than the single AR model in both fitting and forecasting datasets. This suggests the AR-Elman hybrid method could be more effective in predicting temporal trends of TB incidence and could have implications for TB prevention and control strategies in the region.

BMJ OPEN (2021)

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Article Computer Science, Software Engineering

CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization

Zijie J. Wang et al.

Summary: CNN Explainer is an interactive visualization tool designed for non-experts to learn and examine convolutional neural networks. It helps users understand the underlying components of CNNs through a model overview and dynamic visual explanation views. A user study shows that CNN Explainer helps users more easily understand the inner workings of CNNs, and is engaging and enjoyable to use.

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS (2021)

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Article Immunology

A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers

Zhi Zhen Qin et al.

Summary: The availability of AI-powered CAD products for detecting TB-related abnormalities in chest X-rays has increased. These products offer a solution to the shortage of radiologists in high TB-burden countries. Before adoption, TB programs need to consider factors such as diagnostic accuracy and implementation features. The AI4HLTH website serves as a valuable resource for information on CAD tools to help TB programs choose the most suitable product.

TUBERCULOSIS (2021)

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Article Biology

Sharp U-Net: Depthwise convolutional network for biomedical image segmentation

Hasib Zunair et al.

Summary: Sharp U-Net architecture addresses the issues of blurred feature maps and over-/under-segmented target regions by employing a depthwise convolution before merging encoder and decoder features. The model consistently outperforms recent baselines in both binary and multi-class biomedical image segmentation tasks, while adding no extra learnable parameters and surpassing baselines with more than three times the number of parameters.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Public, Environmental & Occupational Health

The Epidemiological Characteristics of Pulmonary Tuberculosis - Kashgar Prefecture, Xinjiang Uygur Autonomous Region, China, 2011-2020

Diermulati Tusun et al.

Summary: This study analyzed the epidemiological characteristics of pulmonary tuberculosis in Kashgar Prefecture, Xinjiang Uygur Autonomous Region, China from 2011 to 2020. The study found a significant declining trend in the notification rate of reported PTB cases in the past three years. Males, elderly population, winter and spring, and farmers as an occupation were identified as the main factors associated with high incidence of tuberculosis in Kashgar. Targeted prevention and treatment of TB should be strengthened in key groups in this region.

CHINA CDC WEEKLY (2021)

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Article Health Care Sciences & Services