Identification of the optimal puncture position in PIEZO-ICSI using image analysis: a pilot study

Maekawa, Taira; Mizuta, Shimpei; Kinishi, Yuya; Takahashi, Chie; Matsubayashi, Hidehiko; Kitaya, Kotaro; Takeuchi, Takumi; Hata, Yoshinobu; Ishikawa, Tomomoto

doi:10.1016/j.fertnstert.2019.07.825

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is expected that a medical simulator for ICSI could be developed in the future. On the other hand, we have the first report that the feature characteristics of the membrane could be a predictor to avoid the rupture [ 16 , 17 , 18 , 19 ]. This technique visualized the optimal puncture point with image processing of LBP in the clinical study.…”

Section: Discussionmentioning

confidence: 99%

“…Although currently AI applications in reproductive medicine [ 15 ] are being reviewed, the number of them is still rather small. We developed a support system to identify the optimal puncture position of the oocyte cytoplasmic membrane by applying LBP and investigated the mechanism of the endometrium in our previous works [ 16 , 17 , 18 ]. In addition, the computer intelligence technique was implemented for the first time and proved its efficacy in the field of reproductive medicine [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rupture Prediction for Microscopic Oocyte Images of Piezo Intracytoplasmic Sperm Injection by Principal Component Analysis

Yagi

Tsuji

Morimoto³

et al. 2022

JCM

Self Cite

View full text Add to dashboard Cite

Assisted reproductive technology (ART) has progressed rapidly, resulting in a great improvement in the clinical pregnancy ratio. When applying the protocol of piezo intracytoplasmic sperm injection (Piezo-ICSI), it is very important to puncture the zona pellucida and the oocyte cytoplasmic membrane without rupturing the oocyte cytoplasmic membrane. Previous studies have shown that the poor extensibility of the oocyte cytoplasmic membrane might be closely related to rupture. However, no consensus has been reached regarding how the quality of the oocyte for extensible ability or rupture possibility affects the surfaces of the oocyte on the microscopic frames. We conducted this study to provide evidence that artificial intelligence (AI) techniques are superior for predicting the tendency of oocyte rupture before puncturing on Piezo-ICSI. To inspect it, we provided a retrospective trial of 38 rupture oocytes and 55 nonruptured oocytes. This study marked the highest accuracy of 91.4% for predicting oocytes rupture using the support-vector machine method of machine learning. We conclude that AI technologies might serve an important role and provide a significant benefit to ART.

show abstract