New preprint: a deep learning model that can read 96-well broth micro dilution plates Philip Fowler, 23rd February 202523rd February 2025 The CRyPTIC project used bespoke 96-well broth microdilution plates to measure the minimum inhibitory concentrations (MICs) of 13 different antibiotics; to reduce the error in the measurements, photographs of each plate were taken after two weeks incubation and stored. Hence we have available over 20,000 images of M. tuberculosis growing on these plates along with MICs measured using a variety of approaches. In this preprint, a group of enterprising students at RMIT University in Ho Chi Minh, Vietnam, retrained a deep learning image classification algorithm (YOLOv8) on a subset of this dataset and achieved very good performance on a hold-out validation dataset. The call this model the TB Microbial Analysis System, TMAS for short. This is another step towards developing machine learning models that can be used to support the reading of such plates in low resource settings where genetics is currently too complex and expensive for tuberculosis drug susceptibility testing (DST). Even then, we will never be able to stop phenotypic DST since the catalogues of mutations a genetics-based microbiology rests on will need continuous validation and improvement. Share this: Click to share on X (Opens in new window) X Click to share on Bluesky (Opens in new window) Bluesky Click to email a link to a friend (Opens in new window) Email Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Mastodon (Opens in new window) Mastodon Related antimicrobial resistance clinical microbiology tuberculosis
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New publication: WHO catalogue of Mycobacterium tuberculosis resistant mutations 28th March 202228th March 2022 The CRyPTIC project collecting over 20,000 clinical samples of TB and for each, sequencing its… Share this: Click to share on X (Opens in new window) X Click to share on Bluesky (Opens in new window) Bluesky Click to email a link to a friend (Opens in new window) Email Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Mastodon (Opens in new window) Mastodon Read More