Infertility

Enhancing Male Fertility Through AI-Based Management of Varicoceles.

REVIEW PURPOSE: The clinical management of subclinical and symptomatic varicoceles in male infertility remains challenging. Current guidelines focus on treating men with abnormal semen analyses, but a more precise approach to identify, stratify, and prognosticate men with varicoceles and fertility issues is essential.

Artificial Intelligence for Clinical Management of Male Infertility, a Scoping Review.

PURPOSE OF REVIEW: Infertility impacts one in six couples worldwide, with male infertility contributing to approximately half of these cases. However, the causes of infertility remain incompletely understood, and current methods of clinical management are cost-restrictive, time-intensive, and have limited success. Artificial intelligence (AI) may help address some of these challenges. In this revi...

Large language models to facilitate pregnancy prediction after in vitro fertilization.

We evaluated the efficacy of large language models (LLMs), specifically, generative pre-trained transformer-4 (GPT-4), in predicting pregnancy followi...

External validation of a fully automated evaluation tool: a retrospective analysis of 68,471 scored embryos.

OBJECTIVE: To externally validate a fully automated embryo classification system for in vitro fertilization (IVF) treatments.

A review of artificial intelligence applications in in vitro fertilization.

The field of reproductive medicine has witnessed rapid advancements in artificial intelligence (AI) methods, which have significantly enhanced the eff...

Identification of key biomarkers for predicting atherosclerosis progression in polycystic ovary syndrome via bioinformatics analysis and machine learning.

OBJECTIVE: Polycystic ovary syndrome (PCOS) is one of the most significant cardiovascular risk factors, playing vital roles in various cardiovascular ...

Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction.

INTRODUCTION: IVF is a widely-used assisted reproductive technology with a consistent success rate of around 30%, and improving this rate is crucial d...

On the role of artificial intelligence in analysing oocytes during in vitro fertilisation procedures.

Nowadays, the most adopted technique to address infertility problems is in vitro fertilisation (IVF). However, its success rate is limited, and the as...

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist.

Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF...

Shared diagnostic genes and potential mechanisms between polycystic ovary syndrome and recurrent miscarriage revealed by integrated transcriptomics analysis and machine learning.

OBJECTIVE: More and more studies have found that polycystic ovary syndrome (PCOS) is significantly associated with recurrent spontaneous abortion (RSA...

Intelligence computational analysis of letrozole solubility in supercritical solvent via machine learning models.

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical...

Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.

BACKGROUND: Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incuba...

Application of machine learning techniques in the diagnosis of endometriosis.

OBJECTIVE: The aim of this study is to assess the use of machine learning methodologies in the diagnosis of endometriosis (EM).

Knowledge-based machine learning for predicting and understanding the androgen receptor (AR)-mediated reproductive toxicity in zebrafish.

Traditional methods for identifying endocrine-disrupting chemicals (EDCs) that activate androgen receptors (AR) are costly, time-consuming, and low-th...

Optimizing oocyte yield utilizing a machine learning model for dose and trigger decisions, a multi-center, prospective study.

The objective of this study was to evaluate clinical outcomes for patients undergoing IVF treatment where an artificial intelligence (AI) platform was...

Deep learning based uterine fibroid detection in ultrasound images.

Uterine fibroids are common benign tumors originating from the uterus's smooth muscle layer, often leading to symptoms such as pelvic pain, and reprod...

F-Net: Follicles Net an efficient tool for the diagnosis of polycystic ovarian syndrome using deep learning techniques.

The study's primary objectives encompass the following: (i) To implement the object detection of ovarian follicles using you only look once (YOLO)v8 a...

Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model.

Ovarian cysts pose significant health risks including torsion, infertility, and cancer, necessitating rapid and accurate diagnosis. Ultrasonography is...

Use of federated learning to develop an artificial intelligence model predicting usable blastocyst formation from pre-ICSI oocyte images.

RESEARCH QUESTION: Can federated learning be used to develop an artificial intelligence (AI) model for evaluating oocyte competence using two-dimensio...

Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms.

: Polycystic ovary syndrome (PCOS) is a complex disorder that can negatively impact the obstetrical outcomes. The aim of this study was to determine t...