AI-Based Analysis of Ziehl–Neelsen-Stained Sputum Smears for Mycobacterium tuberculosis as a Screening Method for Active Tuberculosis

Tuberculosis is the primary cause of death due to infection in the world. Identification of <i>Mycobacterium tuberculosis</i> in sputum is a diagnostic test, which can be used in screening programs-especially in countries with a high incidence of tuberculosis-to identify and treat those persons with the highest risk of disseminating the infection. We previously developed an algorithm which is able to automatically detect mycobacteria on tissue; in particular, our algorithm identified acid-fast bacilli on tissue with 100% specificity, 95.65% sensitivity, and 98.33% accuracy. We tested this algorithm on 1059 Ziehl-Neelsen-stained sputum smears to evaluate its results as a possible tool for screening. The results were displayed as a heat map of 32 × 32 pixel patches. Analysis of the positive patches revealed a good specificity (86.84%) and 100% sensitivity for patches with a level of confidence over 90; furthermore, the accuracy remained over 95% for all levels of confidence over 80, except the class (95-100]. The modest specificity is caused by the peculiarities of smears (uneven thickness, dust contamination, lack of coverslip). We will train the algorithm on sputum smears to increase the specificity to over 95%. However, as our algorithm showed no false negatives, it is suitable for screening.

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Publication Details

Journal
Life
Published
2024-11-03
DOI
https://doi.org/10.3390/life14111418
Citations
5
Primary Topic
Image Processing Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.60
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article

AI-Based Analysis of Ziehl–Neelsen-Stained Sputum Smears for Mycobacterium tuberculosis as a Screening Method for Active Tuberculosis

Liana Sticlaru, Gyula Fekete, Flora Eka Sari, Petronel Mustăţea et al.
5 citations
Life
Image Processing Techniques and Applications
1.60
article

AI-Based Analysis of Ziehl–Neelsen-Stained Sputum Smears for Mycobacterium tuberculosis as a Screening Method for Active Tuberculosis

Liana Sticlaru, Gyula Fekete, Flora Eka Sari, Petronel Mustăţea, Nirawan Putranto, Arief Budi Witarto, Carmen Dumitru, I. Daha, Cristiana Popp, Irina Tudor, Mihai Busca, Gheorghiță Jugulete, Luciana Nichita, Bogdan Ceachi, Cristian Mogodici, Sabina Zurac, Alexandra Vilaia, Mirela Cioplea, Julian Gerald Dcruz, Oana Stefan, Alexandra Bastian, Satria Pratama, Benyamin P. Octavianus
article en
5 citations

Abstract

Tuberculosis is the primary cause of death due to infection in the world. Identification of <i>Mycobacterium tuberculosis</i> in sputum is a diagnostic test, which can be used in screening programs-especially in countries with a high incidence of tuberculosis-to identify and treat those persons with the highest risk of disseminating the infection. We previously developed an algorithm which is able to automatically detect mycobacteria on tissue; in particular, our algorithm identified acid-fast bacilli on tissue with 100% specificity, 95.65% sensitivity, and 98.33% accuracy. We tested this algorithm on 1059 Ziehl-Neelsen-stained sputum smears to evaluate its results as a possible tool for screening. The results were displayed as a heat map of 32 × 32 pixel patches. Analysis of the positive patches revealed a good specificity (86.84%) and 100% sensitivity for patches with a level of confidence over 90; furthermore, the accuracy remained over 95% for all levels of confidence over 80, except the class (95-100]. The modest specificity is caused by the peculiarities of smears (uneven thickness, dust contamination, lack of coverslip). We will train the algorithm on sputum smears to increase the specificity to over 95%. However, as our algorithm showed no false negatives, it is suitable for screening.

LifeVol. 14(11)
Carol Davila University of Medicine and Pharmacy (RO), Universitatea de Medicină, Farmacie, Științe și Tehnologie „George Emil Palade” din Târgu Mureș (RO), Institutul Cantacuzino (RO), Indonesia Defense University (ID), National Institute of Research and Development for Biological Sciences (RO), Spitalul Clinic Colentina (RO), Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
Good health and well-being
Openalex Percentile: Top 14%
Image Processing Techniques and Applications
1.60
DOIOpen Access PDFOpenAlex
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