An inexpensive smartphone-based device for point-of-care ovulation testing. Issue 1 (11th December 2018)

• Record Type: Journal Article
• Authors: Potluri, Vaishnavi; Kathiresan, Preethi Sangeetha; Kandula, Hemanth; Thirumalaraju, Prudhvi; Kanakasabapathy, Manoj Kumar; Kota Sai Pavan, Sandeep; Yarravarapu, Divyank; Soundararajan, Anand; Baskar, Karthik; Gupta, Raghav; Gudipati, Neeraj; C. Petrozza, John; Shafiee, Hadi
• Is Part Of: Lab on a chip. Volume 19:Issue 1(2019)
• Page Start: 59
• Page End: 67
• Publication Date: 2018-12-11
• Subjects: Miniature electronic equipment -- Periodicals; Combinatorial chemistry -- Periodicals; Biotechnology -- Periodicals; 543.0813
• DOI: 10.1039/c8lc00792f
• Languages: English
• ISSNs: 1473-0197

Abstract

Abstract : Artificial intelligence-enabled ovulation testing on smartphones through the analysis of fern patterns in saliva. 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.