Literature DB >> 30534677

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

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.

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Year:  2018        PMID: 30534677      PMCID: PMC6321627          DOI: 10.1039/c8lc00792f

Source DB:  PubMed          Journal:  Lab Chip        ISSN: 1473-0189            Impact factor:   6.799


  26 in total

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Review 2.  An overview on the effectiveness of natural family planning.

Authors:  M Guida; G A Tommaselli; M Pellicano; S Palomba; C Nappi
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Journal:  JAMA       Date:  2016-12-13       Impact factor: 56.272

4.  Global, regional, and subregional trends in unintended pregnancy and its outcomes from 1990 to 2014: estimates from a Bayesian hierarchical model.

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5.  Women's interest in natural family planning.

Authors:  J B Stanford; J C Lemaire; P B Thurman
Journal:  J Fam Pract       Date:  1998-01       Impact factor: 0.493

6.  Dermatologist-level classification of skin cancer with deep neural networks.

Authors:  Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun
Journal:  Nature       Date:  2017-01-25       Impact factor: 49.962

7.  A smartphone dongle for diagnosis of infectious diseases at the point of care.

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Journal:  Sci Transl Med       Date:  2015-02-04       Impact factor: 17.956

8.  On the use of physical activity monitoring for estrus detection in dairy cows.

Authors:  P Løvendahl; M G G Chagunda
Journal:  J Dairy Sci       Date:  2010-01       Impact factor: 4.034

Review 9.  Effect of heat stress on reproductive performances of dairy cattle and buffaloes: A review.

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Journal:  Vet World       Date:  2016-03-05

10.  A smartphone-based diagnostic platform for rapid detection of Zika, chikungunya, and dengue viruses.

Authors:  Aashish Priye; Sara W Bird; Yooli K Light; Cameron S Ball; Oscar A Negrete; Robert J Meagher
Journal:  Sci Rep       Date:  2017-03-20       Impact factor: 4.379

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Journal:  Lab Chip       Date:  2019-11-22       Impact factor: 6.799

Review 2.  3D-printed microneedles in biomedical applications.

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Journal:  iScience       Date:  2020-12-31

Review 3.  Deep Learning-Enabled Technologies for Bioimage Analysis.

Authors:  Fazle Rabbi; Sajjad Rahmani Dabbagh; Pelin Angin; Ali Kemal Yetisen; Savas Tasoglu
Journal:  Micromachines (Basel)       Date:  2022-02-06       Impact factor: 2.891

Review 4.  Microscopic Imaging Methods for Organ-on-a-Chip Platforms.

Authors:  Bailey C Buchanan; Jeong-Yeol Yoon
Journal:  Micromachines (Basel)       Date:  2022-02-19       Impact factor: 2.891

  4 in total

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