Potential Use of Smartphone as a Tool to Capture Embryo Digital Images from Stereomicroscope and to Evaluate Them by an Artificial Neural Network

An online graphical user interface connected to a server was developed aiming to facilitate access to professionals worldwide that face problems with bovine blastocysts classification. The blastocysts assessment is carried on using images taken from an inverted microscope, which usually requires more expensive devices such as digital camera and computer software. Smartphone camera quality and tasks processing are getting better with technology advances. Therefore, a smartphone can be attached to the eyepiece lens to provide Real-Time evaluation, and thus reducing costs when comparing to computers, cameras, and software that are commonly used for this purpose.

[1]  Wei Lv,et al.  A method based on artificial neural network to estimate the health of wind turbine , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[2]  Yun Tian,et al.  Automatic Blastomere Recognition from a Single Embryo Image , 2014, Comput. Math. Methods Medicine.

[3]  Bruce Campbell,et al.  A clinically useful simplified blastocyst grading system. , 2015, Reproductive biomedicine online.

[4]  István Komlósi,et al.  The effect of internal and external factors on bovine embryo transfer results in a tropical environment. , 2006, Animal reproduction science.

[5]  Liron Pantanowitz,et al.  Smartphone adapters for digital photomicrography , 2014, Journal of pathology informatics.

[6]  R. W. Wright,et al.  Bovine embryo morphology and evaluation. , 1983, Theriogenology.

[7]  Parvaneh Saeedi,et al.  Automatic Segmentation of Trophectoderm in Microscopic Images of Human Blastocysts , 2015, IEEE Transactions on Biomedical Engineering.

[8]  B D Slenning,et al.  Agreement among evaluators of bovine embryos produced in vivo or in vitro. , 1995, Theriogenology.

[9]  José C. Rocha,et al.  Methods for assessing the quality of mammalian embryos: How far we are from the gold standard? , 2016, JBRA assisted reproduction.

[10]  José Celso Rocha,et al.  A method using artificial neural networks to morphologically assess mouse blastocyst quality , 2014, Journal of animal science and technology.

[11]  R. J. Mapletoft,et al.  Evaluation and classification of bovine embryos , 2013 .

[12]  Lawrence E Gibson,et al.  Smart teledermatopathology: a feasibility study of novel, high‐value, portable, widely accessible and intuitive telepathology methods using handheld electronic devices , 2013, Journal of cutaneous pathology.

[13]  Leonid Oliker,et al.  Algorithms for Automatic Alignment of Arrays , 1996, J. Parallel Distributed Comput..

[14]  Parvaneh Saeedi,et al.  Automatic blastomere detection in day 1 to day 2 human embryo images using partitioned graphs and ellipsoids , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[15]  Poul Hyttel,et al.  Essentials of Domestic Animal Embryology , 2009 .

[16]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.