Common neuroimaging findings in mild traumatic brain injury (mTBI), including sport-related concussion (SRC), are reviewed based on computed tomography and magnetic resonance imaging (MRI). Common abnormalities radiologically identified on the day of injury, typically a computed tomographic scan, are in the form of contusions, small subarachnoid or intraparenchymal hemorrhages as well as subdural and epidural collections, edema, and skull fractures. Common follow-up neuroimaging findings with MRI include white matter hyperintensities, hypointense signal abnormalities that reflect prior hemorrhage, focal encephalomalacia, presence of atrophy and/or dilated Virchow-Robins perivascular space. The MRI findings from a large pediatric mTBI study show low frequency of positive MRI findings at 6 months postinjury. The review concludes with an examination of some of the advanced MRI-based image analysis methods that can be performed in the patient who has sustained an mTBI.

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.

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.

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.

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