Focal Spot, Winter 2008/2009

https://digitalcommons.wustl.edu/focal_spot_archives/1110/thumbnail.jp

Cutaneous manifestations in Moyamoya angiopathy: A review.

AbstractBackground and purpose: Moyamoya angiopathy (MA) is a progressive cerebrovascular disease with a poorly understood pathophysiology. It is mainly characterized by progressive bilateral stenosis of the terminal intracranial part of the supraclinoid internal carotid arteries and the proximal parts of the middle and anterior cerebral arteries. This results in early‐onset ischemic or hemorrhagic strokes. The disease may be idiopathic (known as Moyamoya disease) or associated with other heritable or acquired conditions, including type 1 neurofibromatosis or other RASopathies, sickle cell disease, Down syndrome, or autoimmune disorders (known as Moyamoya syndrome). Apart from the brain, other organ manifestations including cutaneous ones have also been described in MA patients.Materials and methods: A literature research on PubMed was performed for articles mentioning the cutaneous association in MA and published between 1994 and October 2020.Conclusion: The present review summarizes the cutaneous associations as well as the coincidental dermatological findings seen in MA patients. Those include changes in the epidermis, dermis, or skin appendages for example café‐au‐lait spots, hypomelanosis of Ito, livedo racemosa, hemangiomas, premature graying of hair, chilblains etc

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study

Introduction Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: Favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. Methods and analytics Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns' and mothers' clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. Ethics and dissemination This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study

Velo-Cardio-Facial Syndrome

Purpose of review: Velo-cardio-facial syndrome has emerged from obscurity to become one of the most researched disorders this past decade. It is one of the most common genetic syndromes in humans, the most common contiguous gene syndrome in humans, the most common syndrome of cleft palate, and the most common syndrome of conotruncal heart malformations. Velo-cardio-facial syndrome has an expansive phenotype, a factor reflected in the wide range of studies that cover both clinical features and molecular genetics. In this review, we cover multiple areas of research during the past year, including psychiatric disorders, neuroimaging, and the delineation of clinical features. Recent findings: The identification of candidate genes for heart anomalies, mental illness, and other clinical phenotypes has been reported in the past year with a focus on TBX1 for cardiac and craniofacial phenotypes and COMT and PRODH for psychiatric disorders. The expansive phenotype of velo-cardio-facial syndrome continues to grow with new behavioral and structural anomalies reported. Treatment issues are beginning to draw attention, although most authors continue to focus on diagnostic issues. Summary: Its high population prevalence, estimated to be as common as 1:2000 has sparked a large amount of research, as has the model the syndrome serves for identifying the causes of mental illness and learning disabilities, but it is obvious that more information is needed. Intensive scrutiny of velo-cardio-facial syndrome will undoubtedly continue for many years to come with the hope that researchers will turn more of their attention to treatment and treatment outcomes

Focal Spot, Winter 2007/2008

https://digitalcommons.wustl.edu/focal_spot_archives/1107/thumbnail.jp

The horizon of pediatric cardiac critical care.

Pediatric Cardiac Critical Care (PCCC) is a challenging discipline where decisions require a high degree of preparation and clinical expertise. In the modern era, outcomes of neonates and children with congenital heart defects have dramatically improved, largely by transformative technologies and an expanding collection of pharmacotherapies. Exponential advances in science and technology are occurring at a breathtaking rate, and applying these advances to the PCCC patient is essential to further advancing the science and practice of the field. In this article, we identified and elaborate on seven key elements within the PCCC that will pave the way for the future

Pediatric emergency medicine point-of-care ultrasound: summary of the evidence.

The utility of point-of-care ultrasound is well supported by the medical literature. Consequently, pediatric emergency medicine providers have embraced this technology in everyday practice. Recently, the American Academy of Pediatrics published a policy statement endorsing the use of point-of-care ultrasound by pediatric emergency medicine providers. To date, there is no standard guideline for the practice of point-of-care ultrasound for this specialty. This document serves as an initial step in the detailed how to and description of individual point-of-care ultrasound examinations. Pediatric emergency medicine providers should refer to this paper as reference for published research, objectives for learners, and standardized reporting guidelines

Advancements in Medical Imaging and Diagnostics with Deep Learning Technologies

Medical imaging has long been a cornerstone in diagnostic medicine, providing clinicians with a non-invasive method to visualize internal structures and processes. However, traditional imaging techniques have faced challenges in resolution, safety concerns related to radiation exposure, and the need for invasive procedures for clearer visualization. With the advent of deep learning technologies, significant advancements have been made in the field of medical imaging, addressing many of these challenges and introducing new capabilities. This research seeks into the integration of deep learning in enhancing image resolution, leading to clearer and more detailed visualizations. Furthermore, the ability to reconstruct three-dimensional images from traditional two-dimensional scans offers a more comprehensive view of the area under examination. Automated analysis powered by deep learning algorithms not only speeds up the diagnostic process but also detects anomalies that might be overlooked by the human eye. Predictive analysis, based on these enhanced images, can forecast the likelihood of diseases, and real-time analysis during surgeries ensures immediate feedback, enhancing the precision of medical procedures. Safety in medical imaging has also seen improvements. Techniques powered by deep learning require reduced radiation, minimizing risks to patients. Additionally, the enhanced clarity and detail in images reduce the need for invasive procedures, further ensuring patient safety. The integration of imaging data with Electronic Health Records (EHR) has paved the way for personalized care recommendations, tailoring treatments based on individual patient history and current diagnostics. Lastly, the role of deep learning extends to medical education, where it aids in creating realistic simulations and models, equipping medical professionals with better training tools

Histopathological analysis of vascular malformations

OBJECTIVE: To propose and develop a histopathological criteria to help diagnose vascular malformations. METHODS: All patients who underwent surgical resection and had a confirmed histopathological diagnosis of vascular malformations from 01 March 2018-26 February 2020 were included. A criteria based on 10 parameters was developed to help diagnose vascular malformations. Discrepancies between clinical and histopathological diagnosis were evaluated. RESULTS: A total of 18 cases were identified. There was a discrepancy between the clinical diagnosis and the initially reported histopathological diagnosis in 16 cases (88.9%). This was reduced to 7 (38.9%) and 6 cases (33.3%) with first and second time revised histopathological analysis using proposed criteria. CONCLUSIONS: The discrepancy between clinical and histopathological diagnoses of vascular malformations has highlighted the requirement of an agreed criteria for histopathologists to help formulate their diagnosis. The proposed criteria may be used as a guide in addressing this and guide treatment and improve clinical practice