The system can be used with any common inverted microscope used for ICSI, with or without heated glass stage, brand and /or model.
For any further information and support, you can check our instructions and technical manual. You can request any time a Demo or a Free trial. We will be happy to assist you.
Sperm movement analysis includes several parameters for the quantification of both flagellar and head movement of spermatozoa. We calculate different velocity patterns of the progression of the spermatozoa such as curvilinear velocity, straight-line velocity, average-path velocity, etc. Our algorithm calculation also includes velocity ratios, amplitude of lateral head displacement and other kinematic values for the best determination of motility. Based on all of the above data, the best spermatozoa, at the moment the user ranks, are immediately cycled and displayed on the screen with a certain color so you can decide which sperm is the best for selection and ICSI.
For the morphology ranking system, we use a horizontal gradient color bar, and the algorithms calculate the score by using a combination of multiple and different aspects of spermatozoa characteristics. Those are based on different sperm structures, shape/ratio/dimension, including features such as acrosome, nucleus, etc. All the above characteristics, are combined with experimental algorithms, for the time being, so that we’ll be able to include in the final result also the different sperm membrane properties which are related to membrane function, integrity, protamine levels, and sperm DNA maturity.
The Sperm Tracer uses a sophisticated deep neural network architecture with high complexity algorithms, which track, detect and generate motility and morphology ranking for the sperm analyzed, in real-time, when compared to the reference baseline from multiple samples database and statistical analysis of each given semen sample.
The motility analysis includes four different kinetics patterns. The best spermatozoa, at the moment the user ranks, are immediately cycled and displayed on the screen. Based on the 4 different colors you can decide which sperm is the best for selection and ICSI.
In cases with immotile or with finely twitching movement embryologists can use the morphological option analysis. The morphology analysis includes a ranking system with different confidence levels representing the best or worst spermatozoa to use. In the horizontal gradient color bar, the green color represents the most suitable sperm to use and the red color specifies what should be avoided.
Finally, there is the analysis option that includes both kinematics patterns and the morphology of the immobilized spermatozoa. The total score for the convenience of the embryologists is shown as a horizontal gradient color bar. Red identifies poor sperm while green is a suitable sperm for ICSI. This allows an embryologist to rapidly decide whether or not the analyzed sperm should be selected for ICSI.
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The ranking and the confidence levels obtained, and the algorithm designed and developed by ReproScientifics is the first (1) version of this system. Given the evolution of the artificial intelligence models and subsequent bioinformatics development, the predictive value of this analysis is considered experimental and may be affected, among others, by camera resolution, image or video quality, internet speed, and will be periodically adjusted by enhancements and updates, as the dataset increases, and the algorithm evolves.
The algorithm cannot guarantee higher rates of fertilization, embryo quality or increased pregnancy rates since there are many other factors that may affect the above such as the ICSI technique, the quality of the oocytes, the culture media, the suboptimal laboratory conditions and many other factors related to the patients. The system doesn’t make decisions of diagnostic or therapeutic nature. It doesn’t detect or diagnose diseases, nor does it make any decisions as a Clinical Support Software (CDS).
To avoid doubt, Sperm Tracer technology has not yet obtained any regulatory approvals, including FDA and/or CE approval and the Sperm Tracer has been created for educational and training purposes only. The system can store data and the software does not drive or influence the use of a medical device or user. The analysis services carried out by Sperm Tracer algorithms are exclusively intended to be interpreted by embryologists and/or qualified/certified health professionals and only for educational or training purposes.
The result obtained by this analysis and the information that could be derived from it, cannot be considered in any case as a substitute of medical counselling or treatment by a trained professional and it does not represent itself as a medical enquiry. We recommend you to consult your physician for medical testing & counselling upon reception of your results. Any result should be interpreted in the context of all available clinical findings, within the general context of a medical enquiry, which must be conducted by genetic diagnosis and/or clinical trained professionals.
Data security is the practice of protecting digital information from unauthorized access, corruption, or theft throughout its entire lifecycle. ReproScientifics recognizes the importance of protecting and ensuring the integrity of Users’ Confidential Information and Personal Data.
Users’ Confidential Information and Personal Data are gathered, used, stored, shared, secured, retained, and disposed of in accordance with the best privacy practices. The data is always encrypted meeting the highest standards of security and requirements.
Sperm Tracer is an additional tool that can be used in fertility clinics for improving their outcomes. As the analysis of sperm parameters is the primary approach for identifying and diagnosing male infertility, semen analysis and sperm selection during ICSI are particularly important since it defines fertility status and potential, as well as the course of assisted reproduction.
The World Health Organization (WHO) demographics estimates that the male factor contributes to 50% of infertility cases and according to reports of the European Society of Human Reproduction and Embryology (ESHRE) and the Centers for Disease Control and Prevention of the United States (CDC), the percentage of deliveries per ART cycle in 2014 and 2016 were 21 and 22%, respectively. Among the reasons for this relatively low efficiency, the quality of the spermatozoa has been pointed out as critical, and the presence of low quality and high percentages of DNA-damaged spermatozoa in the patient’s ejaculation is possibly one of the main factors reducing the ARTs outcomes. Thus, one of the main challenges in reproductive medicine is to ensure the highest quality of the spermatozoa used in ARTs.
The visual assessment of sperms for ICSI is performed manually and it is only based on the judgment of embryologists. This method is inaccurate, subjective, non-repeatable, and hard to teach. The inherent lack of objectivity in the evaluation of human sperm motility and morphology, the difficulty in standardizing, implementing, and controlling manual methods, and the high degree of variation within and between laboratories and technicians have fueled the introduction of new technologies based on Artificial Intelligence and deep convolutional neural networks.
In recent years, a number of tests were introduced for the evaluation of sperm chromatin structure, including TUNEL (terminal dUTP nick-end labeling), COMET (single cell gel electrophoresis), AO (acridine orange), CMA3 (chromomycin A3), SCSA (sperm chromatin structure assay), and SCD (sperm chromatin dispersion). Most of these sperm tests require too much time for analysis, expensive equipment, and high costs. But the main problem is that none of the aforementioned techniques can be used in daily routines because the process of fixing and/or the staining procedures compromise sperm viability, either by introducing dye into the cell nucleus or by fully lysing the cell.
With our deep learning-based method the sperm to be used for ICSI is not destroyed. We use complicated algorithms that combine motility kinematics patterns, morphological features, and experimental algorithms for sperm membrane properties, directly related to the DNA integrity. So, for the first time, embryologists have the opportunity to use their experience and skills together with the help of artificial intelligence, to select the best sperm for their patients in real time while the technology can improve the overall results.