A cost-effective system for automated early antimicrobial susceptibility testing using deep learning

Calvin Brown; Derek Tseng; Paige M. K. Larkin; Susan Realegeno; Leanne Mortimer; Arjun Subramonian; Dino Di Carlo; Omai Garner; Aydogan Ozcan

doi:10.1117/12.2579422

5 March 2021 A cost-effective system for automated early antimicrobial susceptibility testing using deep learning

Calvin Brown, Derek Tseng, Paige M. K. Larkin, Susan Realegeno, Leanne Mortimer, Arjun Subramonian, Dino Di Carlo, Omai Garner, Aydogan Ozcan

Author Affiliations +

Proceedings Volume 11626, Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2021; 116260J (2021) https://doi.org/10.1117/12.2579422
Event: SPIE BiOS, 2021, Online Only

Abstract

We demonstrate an automated, cost-effective system that delivers early antimicrobial-susceptibility-testing results, minimizing incubation time and eliminating human errors, while remaining compatible with standard clinical workflow. A neural network processes the time-lapse intensity information from a fiber-optic array to detect growth in each well of a 96-wellplate. Our blind testing on clinical Staphylococcus aureus infections reveals that 95.03% of all the wells containing growth were correctly identified, with an average incubation time of 5.72-h. This deep learning-based optical system met the FDA-defined essential and categorical agreement criteria for all 14 antibiotics tested, after an average of <7-h of incubation time.

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Calvin Brown, Derek Tseng, Paige M. K. Larkin, Susan Realegeno, Leanne Mortimer, Arjun Subramonian, Dino Di Carlo, Omai Garner, and Aydogan Ozcan "A cost-effective system for automated early antimicrobial susceptibility testing using deep learning", Proc. SPIE 11626, Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2021, 116260J (5 March 2021); https://doi.org/10.1117/12.2579422

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