1. Computer pattern recognition of
Mycobacterium tuberculosis in MODS culture
Alicia Katherine Alva Mantari1, Mirko Zimic1, David A. J. Moore2, Robert H. Gilman3, Mark F. Brady4
1 Universidad Peruana Cayetano Heredia, Lima, Peru; 2 Wellcome Centre for Clinical Tropical Medicine, Imperial College London, London, United Kingdom;
3 Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA; 4 Warren Alpert Medical School of Brown University, Providence, USA
Background
Results
Conclusions
Image Processing
The Microscopic-Observation Drug-Susceptibility assay (MODS) is a non-proprietary, low-technology, low-cost liquid broth TB culture tool
MODS is a direct observation culture method that simultaneously yields drug susceptibility and is an improvement on current diagnostics in terms of accuracy, speed, and cost1
Scaling-up this test internationally is difficult because well-trained laboratory technicians are necessary to read the test results
Access to MODS might be significantly improved if a computer could be trained to accurately identify Mycobacterium tuberculosis (MTB) in MODS cultures using only free computer programs2, 3
This first attempt at a pattern recognition algorithm to identify MTB in MODS culture yielded both a high sensitivity and specificity
The algorithm was able to differentiate well between MTB and atypical mycobacteria, even without additional adjusting
The performance of this algorithm lends quantitative support to laboratory technician claims that MTB in MODS has a unique growth pattern
Border
Original
Grayscale
Threshold
Filter
Median
Exclude
Border
Dilation
Background & Border
Erosion
Filter
Fill holes
Filter 2
Median
Filter 2
Fill holes
Filter
Area
Color the background
Skeleton
Extraction of image variables
Future directions
While 10 days incubation was chosen because characteristic cords have formed by this time but conglomerations have not yet formed, it is possible that the evolution of image variables over time could allow for higher precision or faster identification of MTB
This algorithm may benefit from adjustment with machine learning optimization
A clinically applicable experiment will use the entire image as the measure of success rather than the image, leading to another degree of complexity
Larger image libraries will allow for further evolution of this algorithm
7 image variables were selected out of >40 according to significance in a logistic regression based on the learning set of hand-picked typical MTB forms:
Length, width, width variability, birefringence, the ratio of the length to the perimeter, and another composite variable
Methods
MODS cultures at 10 days of incubation were digitally photographed and put through a series of image processing procedures to extract image variables
Logistic regression was used on a set of typical MTB cording forms to make an algorithm to identify specific morphological characteristics and geometrical relationships typical to MTB
This algorithm was then challenged to identify a series of 2875 MTB and non-MTB forms
Because of the concern that atypical mycobacteria might resemble MTB, other mycobacteria were included to ensure that the algorithm could differentiate between mycobacteria: M. kansasiis, M. avium, and M. chelonae
Algorithm cross-reactivity
Typical cords vs. detritus
Bacteria
Specificity
M. avium
97.06
M. chelonae
99.14
M. kansasii
93.75
Detritus
98.25
Irregularly shaped MTB
97.89
ROC
References
Sensitivity= 98.91
Specificity= 98.38
Moore DA, Evans CA, Gilman RH, et al. Microscopic-observation drug-susceptibility assay for the diagnosis of TB. N Engl J Med Oct 12;355(15):
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