Quantification of follicles in human ovarian tissue using image processing software and trained artificial intelligence

Biol Reprod. 2024 Mar 27:ioae048. doi: 10.1093/biolre/ioae048. Online ahead of print.ABSTRACTCancer survival rates in prepubertal girls and young women have risen in recent decades due to increasingly efficient treatments. However, many such treatments are gonadotoxic, causing premature ovarian insufficiency (POI), loss of fertility and ovarian endocrine function. Implantation of donor ovarian tissue encapsulated in immune-isolating capsules is a promising method to restore physiological endocrine function without immunosuppression or risk of reintroducing cancer cells harbored by the tissue. The success of this approach is largely determined by follicle density (FD) in the implanted ovarian tissue, which is analyzed manually from histologic sections and necessitates specialized, time-consuming labor. To address this limitation, we developed a fully automated method to quantify FD that does not require additional coding. We first analyzed ovarian tissue from 12 human donors between 16 to 37 years old using semi-automated image processing with manual follicle annotation and then trained artificial intelligence program based on follicle identification and object classification. One operator manually analyzed 102 whole slide images (WSIs) from serial histologic sections. Of those, 77 images were assessed by a second manual operator, followed with an automated method utilizing artificial intelligence (AI). Of the 1181 follicles the control operator counted, the comparison operato...
Source: Biology of Reproduction - Category: Reproduction Medicine Authors: Source Type: research