| Performance of a deep learning based neural network in the selection of human blastocysts for implantation CL Bormann, MK Kanakasabapathy, P Thirumalaraju, R Gupta, ... Elife 9, e55301, 2020 | 89 | 2020 |
| Consistency and objectivity of automated embryo assessments using deep neural networks CL Bormann, P Thirumalaraju, MK Kanakasabapathy, H Kandula, I Souter, ... Fertility and sterility 113 (4), 781-787. e1, 2020 | 72 | 2020 |
| Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology MK Kanakasabapathy, P Thirumalaraju, CL Bormann, H Kandula, ... Lab on a Chip 19 (24), 4139-4145, 2019 | 48 | 2019 |
| An inexpensive smartphone-based device for point-of-care ovulation testing V Potluri, PS Kathiresan, H Kandula, P Thirumalaraju, ... Lab on a Chip 19 (1), 59-67, 2019 | 37 | 2019 |
| Detection of non-technical losses using advanced metering infrastructure and deep recurrent neural networks S Chatterjee, V Archana, K Suresh, R Saha, R Gupta, F Doshi 2017 IEEE International Conference on Environment and Electrical Engineering …, 2017 | 33 | 2017 |
| Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images MK Kanakasabapathy, P Thirumalaraju, H Kandula, F Doshi, ... Nature Biomedical Engineering 5 (6), 571-585, 2021 | 24 | 2021 |
| Deep learning-enabled blastocyst prediction system for cleavage stage embryo selection P Thirumalaraju, JY Hsu, CL Bormann, M Kanakasabapathy, I Souter, ... Fertility and sterility 111 (4), e29, 2019 | 19 | 2019 |
| Artificial intelligence-enabled system for embryo classification and selection based on image analysis I Dimitriadis, CL Bormann, P Thirumalaraju, M Kanakasabapathy, ... Fertility and sterility 111 (4), e21, 2019 | 17 | 2019 |
| Deep convolutional neural networks (CNN) for assessment and selection of normally fertilized human embryos I Dimitriadis, CL Bormann, MK Kanakasabapathy, P Thirumalaraju, ... Fertility and Sterility 112 (3), e272, 2019 | 10 | 2019 |
| Automated quality assessment of individual embryologists performing ICSI using deep learning-enabled fertilization and embryo grading technology P Thirumalaraju, MK Kanakasabapathy, R Gupta, R Pooniwala, ... Fertility and Sterility 112 (3), e71, 2019 | 7 | 2019 |
| A deep learning framework outperforms embryologists in selecting day 5 euploid blastocysts with the highest implantation potential E Hariton, I Dimitriadis, MK Kanakasabapathy, P Thirumalaraju, R Gupta, ... Fertility and Sterility 112 (3), e77-e78, 2019 | 6 | 2019 |
| Predicting blastocyst formation of day 3 embryos using a convolutional neural network (CNN): a machine learning approach P Bortoletto, MK Kanakasabapathy, P Thirumalaraju, R Gupta, ... Fertility and Sterility 112 (3), e272-e273, 2019 | 5 | 2019 |
| Improved monitoring of human embryo culture conditions using a deep learning-derived key performance indicator (KPI) MK Kanakasabapathy, P Thirumalaraju, R Gupta, R Pooniwala, ... Fertility and Sterility 112 (3), e70-e71, 2019 | 5 | 2019 |
| Deep learning can improve day 5 embryo scoring and decision making in an embryology laboratory E Hariton, P Thirumalaraju, MK Kanakasabapathy, R Gupta, R Pooniwala, ... Fertility and Sterility 112 (3), e272, 2019 | 1 | 2019 |
| Consistency and objectivity of automated embryo assessments using deep neural networks CL Bormann, P Thirumalaraju, MK Kanakasabapathy, H Kandula, I Souter, ... Fertility and Sterility 113 (4), 781-787. e1, 2020 | | 2020 |
Manoj Kumar KanakasabapathyResearcher, Brigham and Women's Hospital, Harvard Medical SchoolVerified email at bwh.harvard.edu
