ArticleLab on a chip2018
An inexpensive smartphone-based device for point-of-care ovulation testing.
Article in Lab on a chip, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07248046 (Comparing Cervical Mucus, PDG, LH, and Basal Body Temperature Combinations for Ovulation and Luteal Phase Identification Using the Premom Smartphone App Versus User-Read Test Results), which is not on this map. Cited by 15 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.
Comparing Cervical Mucus, PDG, LH, and Basal Body Temperature Combinations for Ovulation and Luteal Phase Identification Using the Premom Smartphone App Versus User-Read Test Results: A Prospective Observational Study
Who cites it
15 citing papers in PubMed, 49 citations in OpenAlex.
- Development of colorimetric and machine learning based accurate glucose detection platform for point of care applications.Scientific reports · 2026Article
- Digitally Enabled AI-Interpreted Salivary Ferning-Based Ovulation Prediction: Feasibility Study.Journal of medical Internet research · 2025Article
- Diagnosis of Indian Big Four and monocled Cobra snakebites in envenomed plasma using smartphone-based digital imaging colourimetry method.PLoS neglected tropical diseases · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device.Scientific reports · 2024Article
- Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs).Scientific reports · 2024Article
- Measurement of luteinizing hormone surge in vaginal discharge: a potential biomarker that enables simple, non-invasive prediction of the periovulatory period.BMC women's health · 2024Article
- Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics.Communications medicine · 2023Article
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- Application of Microfluidic Chips in the Detection of Airborne Microorganisms.Micromachines · 2022Review
- Microscopic Imaging Methods for Organ-on-a-Chip Platforms.Micromachines · 2022Review
- Deep Learning-Enabled Technologies for Bioimage Analysis.Micromachines · 2022Review
- 3D-printed microneedles in biomedical applications.iScience · 2021Review
- Consistency and objectivity of automated embryo assessments using deep neural networks.Fertility and sterility · 2020Article
- Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.Lab on a chip · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
Abstract
The ability to accurately predict ovulation at-home using low-cost point-of-care diagnostics can be of significant help for couples who prefer natural family planning. Detecting ovulation-specific hormones in urine samples and monitoring basal body temperature are the current commonly home-based methods used for ovulation detection; however, these methods, relatively, are expensive for prolonged use and the results are difficult to comprehend. Here, we report a smartphone-based point-of-care device for automated ovulation testing using artificial intelligence (AI) by detecting fern patterns in a small volume (<100 μL) of saliva that is air-dried on a microfluidic device. We evaluated the performance of the device using artificial saliva and human saliva samples and observed that the device showed >99% accuracy in effectively predicting ovulation.
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.