ArticlePloS one2014
Quantitative imaging with a mobile phone microscope.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
What it found
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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
63 citing papers in PubMed, 197 citations in OpenAlex.
- Smartphone-based multispectral autofluorescence analysis of bacteria mixtures of staphylococci using convolutional neural network.Journal of biological engineering · 2026Article
- A cost-effective diffuse optical tomographic system for imaging absorbing and fluorescent targets in a scattering medium.Scientific reports · 2025Article
- Point-of-Care Diagnosis of Cryptosporidiosis in Neonatal Ruminant Faecal Samples by a Portable Field Microscope Integrated With a Smartphone.Veterinary medicine and science · 2025Article
- A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning.Scientific data · 2025Article
- Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications.Sensors (Basel, Switzerland) · 2024Review
- Portable, smartphone-linked, and miniaturized photonic resonator absorption microscope (PRAM Mini) for point-of-care diagnostics.Biomedical optics express · 2024Article
- A commentary on the development and use of smartphone imaging devices.Biophysical reviews · 2024Article
- The Effect of Pixel Design and Operation Conditions on Linear Output Range of 4T CMOS Image Sensors.Sensors (Basel, Switzerland) · 2024Article
- Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices.PloS one · 2024Article
- Large depth-of-field ultra-compact microscope by progressive optimization and deep learning.Nature communications · 2023Article
- Thyroid Cytopathology Cancer Diagnosis from Smartphone Images Using Machine Learning.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2023Article
- A review on low-cost microscopes for Open Science.Microscopy research and technique · 2022Review
- Examination of blood samples using deep learning and mobile microscopy.BMC bioinformatics · 2022Article
- Wound Size Imaging: Ready for Smart Assessment and Monitoring.Advances in wound care · 2021Article
- PhaseRMiC: phase real-time microscope camera for live cell imaging.Biomedical optics express · 2021Article
- Smartphone-based imaging systems for medical applications: a critical review.Journal of biomedical optics · 2021Review
- Optical Detection Methods for High-Throughput Fluorescent Droplet Microflow Cytometry.Micromachines · 2021Review
- Pocket MUSE: an affordable, versatile and high-performance fluorescence microscope using a smartphone.Communications biology · 2021Article
- Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2.ACS omega · 2021Article
- Detection of Bacterial and Viral Pathogens Using Photonic Point-of-Care Devices.Diagnostics (Basel, Switzerland) · 2020Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Use of optical imaging for medical and scientific applications requires accurate quantification of features such as object size, color, and brightness. High pixel density cameras available on modern mobile phones have made photography simple and convenient for consumer applications; however, the camera hardware and software that enables this simplicity can present a barrier to accurate quantification of image data. This issue is exacerbated by automated settings, proprietary image processing algorithms, rapid phone evolution, and the diversity of manufacturers. If mobile phone cameras are to live up to their potential to increase access to healthcare in low-resource settings, limitations of mobile phone-based imaging must be fully understood and addressed with procedures that minimize their effects on image quantification. Here we focus on microscopic optical imaging using a custom mobile phone microscope that is compatible with phones from multiple manufacturers. We demonstrate that quantitative microscopy with micron-scale spatial resolution can be carried out with multiple phones and that image linearity, distortion, and color can be corrected as needed. Using all versions of the iPhone and a selection of Android phones released between 2007 and 2012, we show that phones with greater than 5 MP are capable of nearly diffraction-limited resolution over a broad range of magnifications, including those relevant for single cell imaging. We find that automatic focus, exposure, and color gain standard on mobile phones can degrade image resolution and reduce accuracy of color capture if uncorrected, and we devise procedures to avoid these barriers to quantitative imaging. By accommodating the differences between mobile phone cameras and the scientific cameras, mobile phone microscopes can be reliably used to increase access to quantitative imaging for a variety of medical and scientific applications.
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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.