RT Journal Article SR Electronic(1) A1 Fowler, Philip W. A1 Gibertoni Cruz, Ana LuĂ­za A1 Hoosdally, Sarah J. A1 Jarrett, Lisa A1 Borroni, Emanuele A1 Chiacchiaretta, Matteo A1 Rathod, Priti A1 Lehmann, Sarah A1 Molodtsov, Nikolay A1 Walker, Timothy M. A1 Robinson, Esther A1 Hoffmann, Harald A1 Peto, Timothy E. A. A1 Cirillo, Daniela Maria A1 Smith, Grace E. A1 Crook, Derrick W.YR 2018 T1 Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis JF Microbiology, VO 164 IS 12 SP 1522 OP 1530 DO https://doi.org/10.1099/mic.0.000733 PB Microbiology Society, SN 1465-2080, AB M. tuberculosis grows slowly and is challenging to work with experimentally compared with many other bacteria. Although microtitre plates have the potential to enable high-throughput phenotypic testing of M. tuberculosis, they can be difficult to read and interpret. Here we present a software package, the Automated Mycobacterial Growth Detection Algorithm (AMyGDA), that measures how much M. tuberculosis is growing in each well of a 96-well microtitre plate. The plate used here has serial dilutions of 14 anti-tuberculosis drugs, thereby permitting the MICs to be elucidated. The three participating laboratories each inoculated 38 96-well plates with 15 known M. tuberculosis strains (including the standard H37Rv reference strain) and, after 2 weeks' incubation, measured the MICs for all 14 drugs on each plate and took a photograph. By analysing the images, we demonstrate that AMyGDA is reproducible, and that the MICs measured are comparable to those measured by a laboratory scientist. The AMyGDA software will be used by the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) to measure the drug susceptibility profile of a large number (>30000) of samples of M. tuberculosis from patients over the next few years., UL https://www.microbiologyresearch.org/content/journal/micro/10.1099/mic.0.000733