ArticleScientific reports2016
High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 42 citations in OpenAlex.
- Portable Dynamic Laser Speckle Imaging for Rapid Antimicrobial Susceptibility Testing.ECS sensors plus · 2025Article
- Integrated Sample to Detection of Carbapenem-Resistant Bacteria Extracted from Water Samples Using a Portable Gold Nanoparticle-Based Biosensor.Sensors (Basel, Switzerland) · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Review
- Machine learning-based sensor array: full and reduced fluorescence data for versatile analyte detection based on gold nanocluster as a single probe.Analytical and bioanalytical chemistry · 2022Article
- Portable Colorimetric Device with Commercial Microplates for Quantitative Detection of Urine Biomarkers: Design, Development, and Clinical Evaluation.Biosensors · 2022Article
- LC/MS Profiling and Gold Nanoparticle Formulation of Major Metabolites fromPlants (Basel, Switzerland) · 2022Article
- Capillary-Driven Flow Microfluidics Combined with Smartphone Detection: An Emerging Tool for Point-of-Care Diagnostics.Diagnostics (Basel, Switzerland) · 2020Review
- Combinatorial Antimicrobial Susceptibility Testing Enabled by Non-Contact Printing.Micromachines · 2020Article
- Automated screening of sickle cells using a smartphone-based microscope and deep learning.NPJ digital medicine · 2020Article
- Modern Tools for Rapid Diagnostics of Antimicrobial Resistance.Frontiers in cellular and infection microbiology · 2020Review
- Smartphone-based turbidity reader.Scientific reports · 2019Article
- Smartphone-based pathogen diagnosis in urinary sepsis patients.EBioMedicine · 2018Article
- An ultra-low-cost smartphone octochannel spectrometer for mobile health diagnostics.Journal of biophotonics · 2018Article
- Sub-nanoliter, real-time flow monitoring in microfluidic chips using a portable device and smartphone.Scientific reports · 2018Article
- Emerging Microtechnologies and Automated Systems for Rapid Bacterial Identification and Antibiotic Susceptibility Testing.SLAS technology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Routine antimicrobial susceptibility testing (AST) can prevent deaths due to bacteria and reduce the spread of multi-drug-resistance, but cannot be regularly performed in resource-limited-settings due to technological challenges, high-costs, and lack of trained professionals. We demonstrate an automated and cost-effective cellphone-based 96-well microtiter-plate (MTP) reader, capable of performing AST without the need for trained diagnosticians. Our system includes a 3D-printed smartphone attachment that holds and illuminates the MTP using a light-emitting-diode array. 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 ~1 minute. We tested this mobile-reader using MTPs prepared with 17 antibiotics targeting Gram-negative bacteria on clinical isolates of Klebsiella pneumoniae, containing highly-resistant antimicrobial profiles. Using 78 patient isolate test-plates, we demonstrated that our mobile-reader meets the FDA-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 mobile-reader could eliminate the need for trained diagnosticians to perform AST, reduce the cost-barrier for routine testing, and assist in spatio-temporal tracking of bacterial resistance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.