Modified Smartphone-based System Automatically Determines Antibiotic Resistance

An inexpensive optical fiber-array enables the capture of the transmitted light of each well through the smartphone camera. A custom-designed application sends the captured image to a server to automatically determine well turbidity, with results returned to the smartphone in about one minute.

The investigators tested this mobile-reader on clinical isolates of Klebsiella pneumoniae containing highly resistant antimicrobial profiles using MTPs prepared with 17 antibiotics targeting Gram-negative bacteria. Using 78 patient isolate test-plates, they demonstrated that the mobile-reader met the [U.S.] Food and Drug Administration-defined AST criteria, with a well-turbidity detection accuracy of 98.21%, minimum-inhibitory-concentration accuracy of 95.12%, and a drug-susceptibility interpretation accuracy of 99.23%, with no very major errors.

"This work is extremely important and timely, given that drug-resistant bacteria are increasingly becoming a global threat rendering many of our first-line antibiotics ineffective," said senior author Dr. Aydogan Ozcan, professor of electrical engineering and bioengineering at the University of California, Los Angeles. "Our new smartphone-based technology can help put laboratory-quality testing into much wider adoption, especially in resource-limited regions."

The smartphone AST system was described in the December 15, 2016, online edition of the journal Scientific Reports.

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