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. 2006 Oct 12;355(15):1539-50. Scilab Toolbox available at http://www.scilab.org/ ImageJ available at http://rsbweb.nih.gov/ij/ Youden index = 0.9594 Visit modsperu.org for more information