Background and Objectives: The use of Artificial Intelligence (AI) in the medical field is rapidly expanding. This review aims to explore and summarize all published research on the development and validation of deep learning (DL) models in gynecologic laparoscopic surgeries. Materials and Methods: MEDLINE, IEEE Xplore, and Google scholar were searched for eligible studies published between January 2000 and May 2025. Selected studies developed a DL model using datasets derived from gynecologic laparoscopic procedures. The exclusion criteria included non-gynecologic datasets, non-laparoscopic datasets, non-Convolutional Neural Network (CNN) models, and non-English publications. Results: A total of 16 out of 621 studies met our inclusion criteria. The findings were categorized into four main application areas: (i) anatomy classification (n = 6), (ii) anatomy segmentation (n = 5), (iii) surgical instrument classification and segmentation (n = 5), and (iv) surgical action recognition (n = 5). Conclusions: This review emphasizes the growing role of AI in gynecologic laparoscopy, improving anatomy recognition, instrument tracking, and surgical action analysis. As datasets grow and computational capabilities advance, these technologies are poised to improve intraoperative guidance and standardize surgical training.

Background/Objectives : To construct and compare multivariable prediction models for the early prediction of large-for-gestational-age (LGA) neonates, using ultrasound biometry and maternal characteristics. Methods: This retrospective cohort study analyzed data from singleton pregnancies that underwent routine ultrasound examinations at 30⁺⁰–34⁺⁰ weeks of gestation. Ultrasound parameters included fetal abdominal circumference (AC), head circumference (HC), femur length (FL), HC-to-AC ratio, mean uterine artery pulsatility index (mUtA-PI), and presence of polyhydramnios. LGA neonates were defined as those having a birthweight > 90th percentile. Logistic regression was used to evaluate associations between ultrasound markers and LGA after adjusting for the following maternal and pregnancy-related covariates: maternal age, body mass index, parity, gestational diabetes mellitus (GDM), pre-existing diabetes, previous cesarean section (PCS), assisted reproductive technology (ART) use, smoking, hypothyroidism, and chronic hypertension. Associations were expressed as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Three prognostic models were developed utilizing the following predictors: (i) biometric ultrasound measurements including AC, HC-to-AC ratio, FL, UtA-PI, and polyhydramnios (Model 1), (ii) a combination of biometric ultrasound measurements and clinical–maternal data (Model 2), and (iii) only the estimated fetal weight (EFW) (Model 3). Results: In total, 3808 singleton pregnancies were included in the analyses. The multivariable analysis revealed that AC (aOR 1.07, 95% CI [1.06, 1.08]), HC to AC (aOR 1.01, 95% CI [1.006, 1.01]), FL (aOR 1.01, 95% CI [1.009, 1.01]), and the presence of polyhydramnios (aOR 4.97, 95% CI [0.7, 58.8]) were associated with an increased risk of LGA, while a higher mUtA-PI was associated with a reduced risk (aOR 0.98, 95% CI [0.98, 0.99]). Maternal parameters, such as GDM, pre-existing diabetes, elevated pre-pregnancy BMI, absence of uterine artery notching, mUtA-PI, and multiparity, were significantly higher in the LGA group. Both models 1 and 2 showed similar performance (AUCs: 84.7% and 85.3%, respectively) and outperformed model 3 (AUC: 77.5%). Bootstrap and temporal validation indicated minimal overfitting and stable model performance, while decision curve analysis supported potential clinical utility. Conclusions: Models using biometric and Doppler ultrasound at 30–34 weeks demonstrated good discriminative ability for predicting LGA neonates, with an AUC up to 84.7%. Adding maternal characteristics did not significantly improve performance, while the biometric model performed better than EFW alone. Sensitivity at conventional thresholds was low but increased substantially when lower probability cut-offs were applied, illustrating the model’s threshold-dependent flexibility for early risk stratification in different clinical screening needs. Although decision curve analysis was performed to explore potential clinical utility, external validation and prospective assessment in clinical settings are still needed to confirm generalizability and to determine optimal decision thresholds for clinical application.

Embryo’s Implantation is a question that we are not in the position to answer in full details. We know that it depends on embryo’s quality and uterine receptivity. It is possible to access uterine cavity before in vitro fertilisation by a variety of tools, however hysteroscopy is the gold standard in evaluation of uterine cavity and potential pathology. The frequencies of unrecognised uterine pathology revealed by hysteroscopy are 18-50% and 40-43% in patients undergoing in vitro fertilisation or after recurrent implantation failure respectively. Data is inconclusive regarding the role of hysteroscopy on pregnancy outcome after in vitro fertilisation. This review aims to evaluate the validity of office hysteroscopy in subfertile population before or after IVF treatment

Hysteroscopy provides direct visualization of the uterine cavity; however, diagnostic outcomes remain highly operator-dependent and subjective. This study aimed to develop an automated deep learning framework based on the ConvNeXt-XLarge architecture, in comparison with an EfficientNetV2 baseline, to objectively classify common uterine pathologies and thereby reduce diagnostic variability. A prospective dataset of 1262 histopathologically confirmed hysteroscopic images was analyzed using an 80:20 training to validation split and a two-stage transfer learning strategy. In addition to the primary multiclass framework, three binary classification models were developed to evaluate pairwise differentiation between the pathologies. The multiclass model achieved an accuracy of 80.5%, a precision of 82.3%, a weighted Area Under the Receiver Operating Characteristic Curve (AUC) of 94.8%, and a weighted average precision (AP) of 91.6%. In the binary classification tasks, model 1 (fibroids vs. functional polyps) achieved an accuracy of 96.7% and an AUC of 99.8%; model 2 (fibroids vs. hyperplastic polyps) achieved 87.2% accuracy and an AUC of 87.4%; and model 3 (functional vs. hyperplastic polyps) achieved 92.2% accuracy and an AUC of 99.0%. ConvNeXt-XLarge statistically significantly outperformed EfficientNetV2 in the multiclass task in terms of accuracy and AUC, and in model 2 in terms of accuracy, underscoring the added value of the deeper backbone for this hysteroscopic image classification. Overall, these findings demonstrate that deep learning frameworks can accurately automate the classification of hysteroscopic findings. By providing high-precision analysis, these models offer a robust tool to mitigate diagnostic subjectivity and optimize clinical decision-making.