Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA.
Biol Reprod. 2024 Jun 12;110(6):1086-1099. doi: 10.1093/biolre/ioae048.
Cancer 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, 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 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 follicle density that does not require additional coding. We first analyzed ovarian tissue from 12 human donors between 16 and 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 from serial histologic sections. Of those, 77 images were assessed by a second manual operator, followed with an automated method utilizing artificial intelligence. Of the 1181 follicles the control operator counted, the comparison operator counted 1178, and the artificial intelligence counted 927 follicles with 80% of those being correctly identified as follicles. The three-stage artificial intelligence pipeline finished 33% faster than manual annotation. Collectively, this report supports the use of artificial intelligence and automation to select tissue donors and grafts with the greatest follicle density to ensure graft longevity for premature ovarian insufficiency treatment.
由于治疗方法越来越有效，最近几十年来，青春期前女孩和年轻女性的癌症存活率有所上升。然而，许多此类治疗方法具有性腺毒性，会导致卵巢早衰、丧失生育能力和卵巢内分泌功能。将包裹在免疫隔离胶囊中的供体卵巢组织植入是一种有前途的方法，可以在不进行免疫抑制或引入组织中携带的癌细胞的风险的情况下恢复生理内分泌功能。这种方法的成功在很大程度上取决于植入卵巢组织中的卵泡密度，这需要从组织学切片中手动分析，并且需要专门的、耗时的劳动力。为了解决这个限制，我们开发了一种完全自动化的方法来定量卵泡密度，而不需要额外的编码。我们首先使用半自动图像处理方法对 12 名年龄在 16 至 37 岁的人类供体的卵巢组织进行了分析，手动标记了卵泡，并随后使用基于卵泡识别和对象分类的人工智能程序进行了训练。一名操作人员手动分析了 102 张来自连续组织学切片的全幻灯片图像。其中，77 张图像由第二名手动操作人员评估，然后使用人工智能的自动方法进行评估。在对照操作员计数的 1181 个卵泡中，比较操作员计数了 1178 个，人工智能计数了 927 个卵泡，其中 80%被正确识别为卵泡。三阶段人工智能管道比手动注释快 33%。总的来说，本报告支持使用人工智能和自动化来选择具有最大卵泡密度的组织供体和移植物，以确保用于治疗卵巢早衰的移植物的长期存活。
