In Vitro Fertilization is among the most widespread treatments for
infertility. One of its main challenges is the evaluation and selection of
embryo for implantation, a process with large inter- and intra-clinician
variability. Deep learning based methods are gaining attention, but their
opaque nature compromises their acceptance in the clinical context, where
transparency in the decision making is key. In this paper we analyze the
current work in the explainability of AI-assisted embryo analysis models,
identifying the limitations. We also discuss how these models could be
integrated in the clinical context as decision support systems, considering the
needs of clinicians and patients. Finally, we propose guidelines for the sake
of increasing interpretability and trustworthiness, pushing this technology
forward towards established clinical practice.

本文分析了 AI 辅助胚胎分析模型的可解释性工作，确定了其局限性，并讨论了如何将这些模型整合到临床环境中作为决策支持系统，考虑了临床医生和患者的需求。最后，我们提出了增加解释性和可信度、推动这项技术走向临床实践的准则。

AI 辅助胚胎筛选中可解释性的探索

Exploring the Role of Explainability in AI-Assisted Embryo Selection

In Vitro Fertilization (IVF) is the most widely used Assisted Reproductive
Technology (ART). IVF usually involves controlled ovarian stimulation, oocyte
retrieval, fertilization in the laboratory with subsequent embryo transfer. The
first two steps correspond with follicular phase of females and ovulation in
their menstrual cycle. Therefore, we refer to it as the treatment cycle in our
paper. The treatment cycle is crucial because the stimulation medications in
IVF treatment are applied directly on patients. In order to optimize the
stimulation effects and lower the side effects of the stimulation medications,
prompt treatment adjustments are in need. In addition, the quality and quantity
of the retrieved oocytes have a significant effect on the outcome of the
following procedures. To improve the IVF success rate, we propose a
knowledge-based decision support system that can provide medical advice on the
treatment protocol and medication adjustment for each patient visit during IVF
treatment cycle. Our system is efficient in data processing and light-weighted
which can be easily embedded into electronic medical record systems. Moreover,
an oocyte retrieval oriented evaluation demonstrates that our system performs
well in terms of accuracy of advice for the protocols and medications.

为了提高试管婴儿的成功率，我们提出了一种基于知识的决策支持系统，它可以在整个治疗周期中为每次患者访问提供治疗方案和药物调整的医疗建议，并且在卵子检索方面的评估表明，该系统在协议和药物建议的准确性方面表现良好。