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Remote assessment of ovarian response and follicular status using visual analysis of ultrasound images
Authors:Pierson R A  Adams G P
Institution:Department of Obstetrics, Gynecology and Reproductive Sciences, College of Medicine, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada.
Abstract:Computer assisted evaluation of ultrasonographic image attributes indicative of viability and atresia of ovarian follicles has the potential to become an integral part of ovarian superstimulation protocols. However, in many cases, animal handling facilities, laboratories providing image analysis services and the individual making clinical decisions are geographically separated. The feasibility of remote assessment of follicular status and ovarian response to superstimulation is demonstrated using internet and video conferencing techniques. A cohort of heifers (n = 6) was subjected to ovarian superstimulation. Ultrasound images of the ovarian responses were digitally acquired and transmitted to a distant laboratory for quantitative assessment. Images from follicles which ovulated in response to luteolysis and GnRH treatments were visually and quantitatively different from follicles committed to atresia. Two types of atresia were observed; images of non-ovulatory follicles were characterized as being reflective of the potential to develop into either follicular or luteal cysts. It is probable that the response of individual follicles to the pharmacologic agents used for ovarian superstimulation and ovulation induction may be as important as the total number of follicles recruited. Thus, assessment of the progress and fates of individual follicles may be used to tailor ovarian stimulation to individual donor animals to increase the probability of successful ovarian stimulation and embryo production. The use of the internet for data transfer, image analysis and clinical evaluation places the prospects of providing useful information within the grasp of practitioners who wish to have access to the biological information, but do not wish to invest in the equipment required to make quantitative assessments of visual data.
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