| Literature DB >> 30534677 |
Vaishnavi Potluri1, Preethi Sangeetha Kathiresan, Hemanth Kandula, Prudhvi Thirumalaraju, Manoj Kumar Kanakasabapathy, Sandeep Kota Sai Pavan, Divyank Yarravarapu, Anand Soundararajan, Karthik Baskar, Raghav Gupta, Neeraj Gudipati, John C Petrozza, Hadi Shafiee.
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.Entities:
Mesh:
Year: 2018 PMID: 30534677 PMCID: PMC6321627 DOI: 10.1039/c8lc00792f
Source DB: PubMed Journal: Lab Chip ISSN: 1473-0189 Impact factor: 6.799