Macie Massey, Director, Obstetrics and Gynecology, Cambridge Health Alliance
The reproductive sector is using artificial intelligence to improve the accuracy of the operations.
Lack of access, high cost, difficulty of care, and low success rates are the key obstacles that healthcare professionals deal with while using Assisted Reproductive Technology (ART). Despite decreasing fertility rates in Western countries, very few resources have been allocated for reproductive research.
AI and ML are transforming the practice of medicine significantly across multiple fields. Significant inroads have even been made in areas where dermatology, radiology, and pathology are fundamental parts of pattern recognition and classification. The field of reproductive science has been slow to explore opportunities available with AI. In order to solve the barriers of expense, access, and low success rates, AI can be extremely beneficial.
Consider the extraordinarily manual and labor-intensive ART procedures as they are today. Success rates relies on multiple factors. Some variables include patient-specific and (likely) uncontrollable, but others are rooted in the system like sperm, oocyte, and embryo selection for fertilization to implantation. The shortage of automation leads to a high variability of inter-users. After years of training and practice, talented embryologists can indeed be quite successful, however, the learning curve and inaccuracy among providers are rate-limiting.
These concerns are also a source of substantial expenses for the practice. The automation and streamlining of the whole process should decrease overhead expenses for fertility practices, improve access, and lowers patient costs. Innovation should not reduce clinicians' income. On the contrary, improved access, more effective processing, and better results can enhance patient volume and revenue while decreasing manual workload.
The AI can improve its accuracy and predictive abilities as the dataset increases and additional ML continues. The algorithm will get credited for selecting features that are eventually correlated with better performance. Furthermore, the algorithm mathematically determines the characteristics that result in better performance. In addition, the algorithm is even penalized for defining factors correlated with weaker results. Unprotected deep learning AI can identify patterns and features in time that were not considered by the original programmers or that may not be used by embryologists to subjectively assign consistency.
It can be expected to use AI in a similar manner to describe spermatocyte performance. Computer-aided sperm analysis (CASA) systems are utilized in science and have been implemented in some clinics. CASA analyses motile percentage and kinematic parameters at the population-stage. Lateral head displacement amplitude, average path velocity, beat cross frequency, curvilinear velocity, straight-line velocity, straightness, and linearity are the normal parameters.