An inexpensive smartphone-based device for point-of-care ovulation testing

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 (<100μL) 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.

Consistency and objectivity of automated embryo assessments using deep neural networks

“…These algorithms are easily adaptable to different imaging platforms and are not limited to expensive time-lapse imaging platforms. Previous studies on using deep-neural networks have shown their portability to inexpensive hardware such as portable computers (<$100) and smartphones (<$5) ( 12 , 22 ). The underlying costs of the technology associated with developing these networks promises easier and increased adoption of the technology from a scalability standpoint.…”

Understanding and Assisting Reproduction in Wildlife Species Using Microfluidics

“…In a more integrated platform equipped with a dedicated cartridge containing all necessary buffers and pre-loaded with all reagents, LH, follicle-stimulating hormone (FSH), β-hCG, and prostaglandin were simultaneously analyzed in serum using a sandwich immunoassay followed by quantitative fluorescence detection [15]. More recently, a completely different approach was proposed that used microliter-sized saliva samples dried in a microfluidic device and smartphone imaging [16]. Fern patterns resulting from sample drying were analyzed using artificial intelligence in saliva in the ovulation period, to reveal an increase in salivary electrolytes reflecting the higher estradiol level in blood.…”

3D-printed microneedles in biomedical applications

“…Each 3D printing method has a unique set of tradeoffs in resolution, cost-efficiency, biocompatibility, and output volume, enabling the use of 3D printing in a wide range of applications ( Bakhshinejad and D'souza, 2015 ; Park et al., 2015 ). The ability to use biomaterials in 3D printing processes ( Chia and Wu, 2015 ), along with microscale and nanoscale 3D printing ( You et al., 2018 ), can enable the fabrication of a wide range of laboratory instruments for clinical and point-of-care applications ( Aimar et al., 2019 ; Amin et al., 2016b ; Douroumis, 2019 ; Knowlton et al., 2015c ; Yenilmez et al., 2016a ), including organ-on-a-chip devices ( Jain et al., 2020 ; Knowlton and Tasoglu, 2016 ; Knowlton et al., 2016b , 2016c ), tissue engineering ( Knowlton et al., 2018 ; Sears et al., 2016 ; Zhang et al., 2019 ), wound healing ( Joseph et al., 2019 ; Tabriz et al., 2020 ), fertility and embryology research ( Kanakasabapathy et al., 2019 ; Knowlton et al., 2015d ; Potluri et al., 2018 ), cancer research ( Knowlton et al., 2015a , 2016a ), stem cell research ( Javaid and Haleem, 2020 ; Tasoglu and Demirci, 2013 ), and circulating tumor cell isolation ( Chen et al., 2020 ).…”