The effectiveness of hysteroscopy in improving pregnancy rates in subfertile women without other gynaecological symptoms: a systematic review

background: Although hysteroscopy is frequently used in the management of subfertile women, a systematic review of the evidence on this subject is lacking. methods: We summarized and appraised the evidence for the benefit yielded by this procedure. Our systematic search was limited to randomized and controlled studies. The QUOROM and MOOSE guidelines were followed. Language restrictions were not applied. results: We identified 30 relevant publications. Hysteroscopic removal of endometrial polyps with a mean diameter of 16 mm detected by ultrasound doubles the pregnancy rate when compared with diagnostic hysteroscopy and polyp biopsy in patients undergoing intrauterine insemination, starting 3 months after the surgical intervention [relative risk (RR) ¼ 2.3; 95% confidence interval (CI): 1.6–3.2]. In patients with one fibroid structure smaller than 4 cm, there was a marginally significant benefit from myomectomy when compared with expectant management (RR ¼ 1.9; 95% CI: 1.0–3.7). Hysteroscopic metroplasty for septate uterus resulted in fewer pregnancies in patients with subfertility when compared with those with recurrent pregnancy loss (RR ¼ 0.7; 95% CI: 0.5–0.9). Randomized controlled studies on hysteroscopic treatment of intrauterine adhesions are lacking. Hysteroscopy in the cycle preceding a subsequent IVF attempt nearly doubles the pregnancy rate in patients with at least two failed IVF attempts compared with starting IVF immediately (RR ¼ 1.7; 95% CI: 1.5–2.0). conclusions: Scarce evidence on the effectiveness of hysteroscopic surgery in subfertile women with polyps, fibroids, septate uterus or intrauterine adhesions indicates a potential benefit. More randomized controlled trials are needed before widespread use of hysteroscopic surgery in the general subfertile population can be justified

the impact of uterine immaturity on obstetrical syndromes during adolescence

Pregnant nulliparous adolescents are at increased risk, inversely proportional to their age, of major obstetric syndromes, including preeclampsia, fetal growth restriction, and preterm birth. Emerging evidence indicates that biological immaturity of the uterus accounts for the increased incidence of obstetrical disorders in very young mothers, possibly compounded by sociodemographic factors associated with teenage pregnancy. The endometrium in most newborns is intrinsically resistant to progesterone signaling, and the rate of transition to a fully responsive tissue likely determines pregnancy outcome during adolescence. In addition to ontogenetic progesterone resistance, other factors appear important for the transition of the immature uterus to a functional organ, including estrogen-dependent growth and tissue-specific conditioning of uterine natural killer cells, which plays a critical role in vascular adaptation during pregnancy. The perivascular space around the spiral arteries is rich in endometrial mesenchymal stem-like cells, and dynamic changes in this niche are essential to accommodate endovascular trophoblast invasion and deep placentation. Here we evaluate the intrinsic (uterine-specific) mechanisms that predispose adolescent mothers to the great obstetrical syndromes and discuss the convergence of extrinsic risk factors that may be amenable to intervention

Semantic Extraction of Permanent Structures for the Reconstruction of Building Interiors from Point Clouds

The extraction of permanent structures (such as walls, floors, and ceilings) is an important step in the reconstruction of building interiors from point clouds. These permanent structures are, in general, assumed to be planar. However, point clouds from building interiors often also contain clutter with planar surfaces such as furniture, cabinets, etc. Hence, not all planar surfaces that are extracted belong to permanent structures. This is undesirable as it can result in geometric errors in the reconstruction. Therefore, it is important that reconstruction methods can correctly detect and extract all permanent structures even in the presence of such clutter. We propose to perform semantic scene completion using deep learning, prior to the extraction of permanent structures to improve the reconstruction results. For this, we started from the ScanComplete network proposed by Dai et al. We adapted the network to use a different input representation to eliminate the need for scanning trajectory information as this is not always available. Furthermore, we optimized the architecture to make inference and training significantly faster. To further improve the results of the network, we created a more realistic dataset based on real-life scans from building interiors. The experimental results show that our approach significantly improves the extraction of the permanent structures from both synthetically generated as well as real-life point clouds, thereby improving the overall reconstruction results.status: publishe

Semantic Extraction of Permanent Structures for the Reconstruction of Building Interiors from Point Clouds

The extraction of permanent structures (such as walls, floors, and ceilings) is an important step in the reconstruction of building interiors from point clouds. These permanent structures are, in general, assumed to be planar. However, point clouds from building interiors often also contain clutter with planar surfaces such as furniture, cabinets, etc. Hence, not all planar surfaces that are extracted belong to permanent structures. This is undesirable as it can result in geometric errors in the reconstruction. Therefore, it is important that reconstruction methods can correctly detect and extract all permanent structures even in the presence of such clutter. We propose to perform semantic scene completion using deep learning, prior to the extraction of permanent structures to improve the reconstruction results. For this, we started from the ScanComplete network proposed by Dai et al. We adapted the network to use a different input representation to eliminate the need for scanning trajectory information as this is not always available. Furthermore, we optimized the architecture to make inference and training significantly faster. To further improve the results of the network, we created a more realistic dataset based on real-life scans from building interiors. The experimental results show that our approach significantly improves the extraction of the permanent structures from both synthetically generated as well as real-life point clouds, thereby improving the overall reconstruction results