Morphological Characterization of Mycobacterium tuberculosis in a MODS Culture for an Automatic Diagnostics through Pattern Recognition

Alicia Alva, Fredy Aquino, Robert H Gilman, Carlos Olivares, David Requena, Andrés H Gutiérrez, Luz Caviedes, Jorge Coronel, Sandra Larson, Patricia Sheen, David A J Moore and Mirko Zimic

PLOS ONE, 2013, vol. 8, issue 12, 1-11

Abstract: Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts.The Microscopic Observation Drug Susceptibility (MODS) assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB) to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7–10 days with a low cost.An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS.We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity) and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii). The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity.This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0082809

DOI: 10.1371/journal.pone.0082809

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