A Study of Mycobacterium tuberculosis Detection Using Different Neural Networks in Autopsy Specimens

Lee, Joong; Lee, Junghye

doi:10.3390/diagnostics13132230

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…Molecular tests include transcription-mediated amplification, strand-displacement amplification, conventional PCR, and Xpert MTB/RIF. Other techniques include mycobacterial culture, drug susceptibility tests, histopathologic examinations, and immunologic tests like the tuberculin skin test (TST) and interferon-gamma releasing assay (IGRA) [ 4 ]. The ZN stained histopathological examination, recognized as the standard approach, is commonly adopted for diagnosing pulmonary TB because of its cost-effectiveness [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network

Acharya,

Choi,

Yener

et al. 2024

IEEE Access

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Tuberculosis (TB), primarily affecting the lungs, is caused by the bacterium Mycobacterium tuberculosis and poses a significant health risk. Detecting acid-fast bacilli (AFB) in stained samples is critical for TB diagnosis. Whole Slide (WS) Imaging allows for digitally examining these stained samples. However, current deep-learning approaches to analyzing large-sized whole slide images (WSIs) often employ patch-wise analysis, potentially missing the complex spatial patterns observed in the granuloma essential for accurate TB classification. To address this limitation, we propose an approach that models cell characteristics and interactions as a graph, capturing both cell-level information and the overall tissue micro-architecture. This method differs from the strategies in related cell graph-based works that rely on edge thresholds based on sparsity/density in cell graph construction, emphasizing a biologically informed threshold determination instead. We introduce a cell graph-based jumping knowledge neural network (CG-JKNN) that operates on the cell graphs where the edge thresholds are selected based on the length of the mycobacteria’s cords and the activated macrophage nucleus’s size to reflect the actual biological interactions observed in the tissue. The primary process involves training a Convolutional Neural Network (CNN) to segment AFBs and macrophage nuclei, followed by converting large (42831*41159 pixels) lung histology images into cell graphs where an activated macrophage nucleus/AFB represents each node within the graph and their interactions are denoted as edges. To enhance the interpretability of our model, we employ Integrated Gradients and Shapely Additive Explanations (SHAP). Our analysis incorporated a combination of 33 graph metrics and 20 cell morphology features. In terms of traditional machine learning models, Extreme Gradient Boosting (XGBoost) was the best performer, achieving an F1 score of 0.9813 and an Area under the Precision-Recall Curve (AUPRC) of 0.9848 on the test set. Among graph-based models, our CG-JKNN was the top performer, attaining an F1 score of 0.9549 and an AUPRC of 0.9846 on the held-out test set. The integration of graph-based and morphological features proved highly effective, with CG-JKNN and XGBoost showing promising results in classifying instances into AFB and activated macrophage nucleus. The features identified as significant by our models closely align with the criteria used by pathologists in practice, highlighting the clinical applicability of our approach. Future work will explore knowledge distillation techniques and graph-level classification into distinct TB progression categories.

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Section: Introductionmentioning

confidence: 99%