Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma.

OBJECTIVE: To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, or pelvis) trauma, by using clinical and/or imaging data. MATERIALS AND METHODS: This was a retrospective study of 840 adult patients admitted to a level 1 trauma center after injury to the torso over the course of 1 year. Clinical parameters included age, sex, vital signs, clinical scores, and laboratory values. Imaging data consisted of any injury present on CT. The two outcomes of interest were ICU admission and extended LOS, defined as more than the median LOS in the dataset. We developed and tested artificial neural network (ANN) and support vector machine (SVM) models, and predictive performance was evaluated by area under the receiver operating characteristic (ROC) curve (AUC). RESULTS: The AUCs of SVM and ANN models to predict ICU admission were up to 0.87 ± 0.03 and 0.78 ± 0.02, respectively. The AUCs of SVM and ANN models to predict extended LOS were up to 0.80 ± 0.04 and 0.81 ± 0.05, respectively. Predictions based on imaging alone or imaging with clinical parameters were consistently more accurate than those based solely on clinical parameters. CONCLUSIONS: The best performing models incorporated imaging findings and outperformed those with clinical findings alone. ML models have the potential to help predict outcomes in trauma by integrating clinical and imaging findings, although further research may be needed to optimize their performance. KEY POINTS: • Artificial neural network and support vector machine-based models were used to predict the intensive care unit admission and extended length of stay after trauma to the torso. • Our input data consisted of clinical parameters and CT imaging findings derived from radiology reports, and we found that combining the two significantly enhanced the prediction of both outcomes with either model. • The highest accuracy (83%) and highest area under the receiver operating characteristic curve (0.87) were obtained for artificial neural networks and support vector machines, respectively, by combining clinical and imaging features in the prediction of intensive care unit admission.

How to Use YouTube for Radiology Education.

YouTube, the most commonly used free video-sharing platform globally, is increasingly being used as an educational tool in Radiology. Trainees worldwide now have the opportunity to learn about medical imaging at their own pace in the comfort of their homes, without geographical and financial constraints. Unfortunately, because YouTube is an easily accessible platform, it also incurs the risk of disseminating erroneous medical information or low-quality educational content. This article outlines the primary considerations when creating educational content on YouTube, including technical aspects, best practices, and measures to maximize effectiveness and success. Additionally, we discuss the current usage of the platform for Radiology education and its advantages and disadvantages and list some of the most popular Radiology YouTube channels.

Knowledge and Myths about Palliative Care among the General Public and Health Care Professionals in Portugal.

Background: International research has shown that healthcare professionals (HCPs) and nonhealthcare professionals (NHCPs) are unaware of the goals and purposes of palliative care. This study evaluates the knowledge of palliative care among a sample of Portuguese adults and correlates their level of knowledge with age, gender, profession, and experience of family member's palliative care. Method: A cross-sectional online survey was carried out on a sample of 152 HCPs and 440 NHCPs who completed an anonymous questionnaire of sociodemographic, family, and professional data, and an instrument of 26 dichotomous (true or false) questions focusing on palliative care goals and purposes. Results: The 592 participants had a mean age of 31.3 ± 11.1 years, and most were female. Statistically significant differences between statements considered as correct by HCPs and NHCPs were found in 24 statements; HCPs had the highest percentage of correct answers. The terms most frequently associated with palliative care mentioned by NHCPs were chronic and progressive disease (n = 76), while HCPs mostly mentioned quality-of-life promotion (n = 29). Women, the elderly, and HCPs had a higher level of knowledge regarding palliative care (p < 0.001). Conclusions: Results clearly show gaps in knowledge of palliative care, especially among NHCPs. An integrated approach is needed to inform and clarify the philosophy and goals of palliative care in different settings in order to improve knowledge.

Solitary intramuscular myofibroma in an adult: Case report and MR imaging findings.

Myofibroma is a benign, soft tissue neoplasm that predominantly affects infants and young children. Most occur in the skin or subcutaneous tissues, with a predilection for the head and neck regions. We describe the magnetic resonance (MR) imaging and histophathologic findings of a rare case of intramuscular myofibroma of the right deltoid in a healthy 30-year-old male. MR imaging revealed a well-circumscribed intramuscular mass, with isointense signal on T1-weighted images, hyperintense signal on T2-weighed images, and a "target-sign" with peripheral rim enhancement after gadolinium administration. The lesion was surgically excised with no complications, and the histopathologic analysis revealed the typical morphologic and histochemical markers of a myofibroma. We conclude that, although rare, myofibroma can be considered in the differential diagnosis of adults with lesions the above signal characteristics.