Artificial Intelligence in Image-Based Screening and Diagnostics of Pulmonary Tuberculosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 9641

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National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Interests: machine learning; artificial intelligence; medical image analysis; image informatics; multimodal data analysis; data science
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Interests: machine learning; artificial intelligence; computer vision; medical image analysis; data science; biomaterial-associated infections; music therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In 2020, the World Health Organization (WHO) estimated that 10 million people were infected with tuberculosis (TB) worldwide. A total of 1.4 million people died from TB in 2019 (including 208,000 people with HIV). Worldwide, TB is one of the top 10 causes of death and the leading cause from a single infectious agent (above HIV/AIDS). Further, child and adolescent TB is often overlooked by health providers and can be difficult to diagnose and treat. Additionally, multidrug-resistant TB (MDR-TB) remains a public health crisis and a health security threat. A global total of 206,030 people with multidrug- or rifampicin-resistant TB (MDR/RR-TB) were detected and notified in 2019—a 10% increase from 186,883 in 2018.

Early screening and diagnosis play a crucial role in increasing the survival rate. There are several diagnostic methods, including the slow sputum culture, tissue biopsy analysis, as well as the WHO-recommended Xpert MTB/RIF, Xpert Ultra, and TrueNAT assays. Radiographic imaging methods such as computed tomography (CT) and chest-X-rays (CXRs) are also widely used for screening and diagnosis. Research on using artificial intelligence (AI) and machine learning (ML) methods for image-based screening and diagnostics of pulmonary TB has gained significance because they offer the promise of alleviating the human burden in screening in countries that lack adequate resources.

Through this Special Issue, “Artificial Intelligence in Image-Based Screening and Diagnostics of Pulmonary Tuberculosis”, we aim to include primary research studies and literature reviews focusing on the novel AI/ML methods and their application in the screening and diagnosis of pulmonary MDR- and drug-sensitive TB. It will help convey the state-of-the-art in AI that has made or exhibits the potential to make a significant contribution to an important global health challenge.

Dr. Sameer Antani
Dr. Sivaramakrishnan Rajaraman
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • image-based screening and diagnostics
  • computer-aided diagnosis
  • machine learning
  • deep learning
  • global health

Published Papers (1 paper)

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21 pages, 5400 KiB  
Article
Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings
by Sivaramakrishnan Rajaraman, Ghada Zamzmi, Les Folio, Philip Alderson and Sameer Antani
Diagnostics 2021, 11(5), 840; https://doi.org/10.3390/diagnostics11050840 - 7 May 2021
Cited by 21 | Viewed by 8332
Abstract
Chest X-rays (CXRs) are the most commonly performed diagnostic examination to detect cardiopulmonary abnormalities. However, the presence of bony structures such as ribs and clavicles can obscure subtle abnormalities, resulting in diagnostic errors. This study aims to build a deep learning (DL)-based bone [...] Read more.
Chest X-rays (CXRs) are the most commonly performed diagnostic examination to detect cardiopulmonary abnormalities. However, the presence of bony structures such as ribs and clavicles can obscure subtle abnormalities, resulting in diagnostic errors. This study aims to build a deep learning (DL)-based bone suppression model that identifies and removes these occluding bony structures in frontal CXRs to assist in reducing errors in radiological interpretation, including DL workflows, related to detecting manifestations consistent with tuberculosis (TB). Several bone suppression models with various deep architectures are trained and optimized using the proposed combined loss function and their performances are evaluated in a cross-institutional test setting using several metrics such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and multiscale structural similarity measure (MS–SSIM). The best-performing model (ResNet–BS) (PSNR = 34.0678; MS–SSIM = 0.9828) is used to suppress bones in the publicly available Shenzhen and Montgomery TB CXR collections. A VGG-16 model is pretrained on a large collection of publicly available CXRs. The CXR-pretrained model is then fine-tuned individually on the non-bone-suppressed and bone-suppressed CXRs of Shenzhen and Montgomery TB CXR collections to classify them as showing normal lungs or TB manifestations. The performances of these models are compared using several performance metrics such as accuracy, the area under the curve (AUC), sensitivity, specificity, precision, F-score, and Matthews correlation coefficient (MCC), analyzed for statistical significance, and their predictions are qualitatively interpreted through class-selective relevance maps (CRMs). It is observed that the models trained on bone-suppressed CXRs (Shenzhen: AUC = 0.9535 ± 0.0186; Montgomery: AUC = 0.9635 ± 0.0106) significantly outperformed (p < 0.05) the models trained on the non-bone-suppressed CXRs (Shenzhen: AUC = 0.8991 ± 0.0268; Montgomery: AUC = 0.8567 ± 0.0870).. Models trained on bone-suppressed CXRs improved detection of TB-consistent findings and resulted in compact clustering of the data points in the feature space signifying that bone suppression improved the model sensitivity toward TB classification. Full article
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