Pregnancy success and outcomes after uterine fibroid embolization: updated review of published literature.

Females with symptomatic leiomyomas (fibroids) wishing to maintain fertility are faced with difficult treatment choices. These include uterine fibroid embolization (UFE), myomectomy, hormonal therapy, MRI high intensity focused ultrasound, and myolysis. This review focuses on UFE, one of the most commonly accepted minimally invasive procedural choices among patients with symptomatic fibroids wishing to retain the option of becoming pregnant in the future, and makes comparisons to myomectomy which has historically been the surgical choice for fertility-preserving fibroid treatment. Pubmed and Google Scholar searches using keywords such as: uterine artery embolization, uterine fibroid embolization, pregnancy, complications, infertility were performed between Jan 1, 2019 and May 10, 2019. Publications were chosen based on their inclusion of information pertaining to fertility or pregnancy after UFE without being limited to single case reports.Randomized controlled trials comparing myomectomy and UFE are limited due to study size and confounding variables, but through registry data and familiarity with referring clinicians, UFE has gained wide acceptance. Healthy pregnancies following UFE have been sporadically reported but the actual fertility rate after UFE remains uncertain. Conversely, low birth weight, miscarriage and prematurity have been associated with UFE. Despite inherent risks of possible fertility issues after UFE, the procedure remains an option for females with clinically symptomatic fibroids who desire pregnancy. However, additional research regarding rates of conception and obstetrical risks of infertility following UFE is necessary.

Use of a three-dimensional printed anatomical model for tumor management in a pediatric patient.

The purpose of this study was to investigate the usage of an anatomical model to improve surgical planning of a complex schwannoma resection. As advancements in additive manufacturing continue to prosper, new applications of this valuable technology are being implemented in the medical field. One of the most recent applications has been in the development of patient-specific anatomical models for unique clinical education as well as for preoperative planning. In this case, a multidisciplinary team with expertise in research, three-dimensional printing, and medicine was formed to develop a three-dimensional printed model that could be used to help plan the reduction of a tumor from the cervical spine of a pediatric patient. Image segmentation and stereolithography creation were accomplished using Mimics and 3-matic, respectively. Models were developed on two different printer types to view different aspects of the region of interest. Reports from the operating surgeon indicated that the model was instrumental in the planning procedures of the operation and reducing operation time.

Pressure-Sensing Gastric Calibration Tube for Minimizing Sleeve Volume Variation in Sleeve Gastrectomy Compared with Current Standard Technique.

The sleeve gastrectomy technique is dependent on the size of the bougie and the surgeon's technical skills. Standardization of the sleeve gastrectomy technique may potentially minimize the volume inconsistency and improve outcomes. A volume and pressure-sensitive gastric calibration tube may create a standard sleeve size and minimize interoperator variation. The objective of preliminary testing was to establish the variability of sleeve gastrectomy size in gastric explants, and to compare that with the variability of sleeve gastrectomy size when performed with a volume and pressure-sensitive gastric calibration tube. Three operators performed six sleeve gastrectomies each on commercially processed porcine gastric explants, half with a 40 Fr bougie, and a half with a pressure-sensing and volume-controllable gastric calibration tube prototype. The resulting sleeves were evaluated using standard statistical methods. The pressure-sensitive gastric calibration tube demonstrated superior consistency to a standard 40 Fr bougie by common measures of variation. However, further investigation is warranted to characterize the significance of this difference.

Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume.

PURPOSE: Hydrocephalus is a clinically significant condition which can have devastating consequences if left untreated. Currently available methods for quantifying this condition using CT imaging are unreliable and prone to error. The purpose of this study is to investigate the clinical utility of using convolutional neural networks to calculate ventricular volume and explore limitations. METHODS: A two-dimensional convolutional neural network was designed to perform fully automated ventricular segmentation on CT images. A total of 300 head CTs were collected and used in this exploration. Two hundred were used to train the network, 50 were used for validation, and 50 were used for testing. RESULTS: Dice scores for the left lateral, right lateral, and third ventricle segmentations were 0.92, 0.92, and 0.79, respectively; the coefficients of determination were r2 = 0.991, r2 = 0.994, and r2 = 0.976; the average volume differences between manual and automated segmentation were 0.821 ml, 0.587 ml, and 0.099 ml. CONCLUSION: Two-dimensional convolutional neural network architectures can be used to accurately segment and quantify intracranial ventricle volume. While further refinements are necessary, it is likely these networks could be used as a clinical tool to quantify hydrocephalus accurately and efficiently.

The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning.

INTRODUCTION: 3D-printed anatomical models play an important role in medical and research settings. The recent successes of 3D anatomical models in healthcare have led many institutions to adopt the technology. However, there remain several issues that must be addressed before it can become more wide-spread. Of importance are the problems of cost and time of manufacturing. Machine learning (ML) could be utilized to solve these issues by streamlining the 3D modeling process through rapid medical image segmentation and improved patient selection and image acquisition. The current challenges, potential solutions, and future directions for ML and 3D anatomical modeling in healthcare are discussed. AREAS COVERED: This review covers research articles in the field of machine learning as related to 3D anatomical modeling. Topics discussed include automated image segmentation, cost reduction, and related time constraints. EXPERT COMMENTARY: ML-based segmentation of medical images could potentially improve the process of 3D anatomical modeling. However, until more research is done to validate these technologies in clinical practice, their impact on patient outcomes will remain unknown. We have the necessary computational tools to tackle the problems discussed. The difficulty now lies in our ability to collect sufficient data.

The potential role of bioengineering and three-dimensional printing in curing global corneal blindness.

An insufficiency of accessible allograft tissue for corneal transplantation leaves many impaired by untreated corneal disease. There is promise in the field of regenerative medicine for the development of autologous corneal tissue grafts or collagen-based scaffolds. Another approach is to create a suitable corneal implant that meets the refractive needs of the cornea and is integrated into the surrounding tissue but does not attempt to perfectly mimic the native cornea on a cellular level. Materials that have been investigated for use in the latter concept include natural polymers such as gelatin, semisynthetic polymers like gelatin methacrylate, and synthetic polymers. There are advantages and disadvantages inherent in natural and synthetic polymers: natural polymers are generally more biodegradable and biocompatible, while synthetic polymers typically provide greater control over the characteristics or property adjustment of the materials. Additive manufacturing could aid in the precision production of keratoprostheses and the personalization of implants.