An inexpensive smartphone-based device for point-of-care ovulation testing
| Autor: | Hadi Shafiee, Neeraj Gudipati, Vaishnavi Potluri, Sandeep Kota Sai Pavan, Hemanth Kandula, John C. Petrozza, Raghav Gupta, Prudhvi Thirumalaraju, Manoj Kumar Kanakasabapathy, Karthik Baskar, Preethi Sangeetha Kathiresan, Divyank Yarravarapu, Anand Soundararajan |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
media_common.quotation_subject Biomedical Engineering Physiology Bioengineering 02 engineering and technology 01 natural sciences Biochemistry Models Biological Article Young Adult Artificial Intelligence Basal body temperature Medicine Humans Saliva Natural family planning Ovulation Point of care media_common Ovulation Detection Small volume business.industry 010401 analytical chemistry General Chemistry Equipment Design 021001 nanoscience & nanotechnology 0104 chemical sciences Predicting ovulation Point-of-Care Testing Female Smartphone 0210 nano-technology business |
| Zdroj: | Lab on a chip. 19(1) |
| ISSN: | 1473-0189 |
| Popis: | 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 (99% accuracy in effectively predicting ovulation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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