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Linear and volumetric analysis are the typical methods to measure tumor size. 3D volumetric analysis has risen in popularity; however, this is very time and labor intensive limiting its implementation in clinical practice. This study aims to show that an AI-led approach can shorten the length of time required to conduct 3D volumetric analysis of VS tumors and improve image processing accuracy. From Yale New Haven Hospital and public patient recruitment, 143 MRIs were included in the ground truth dataset. To create the tumor models for the ground truth dataset, an image processing software (Simpleware ScanIP, Synopsys) was used. The helper (DPP V1.0) was trained using proprietary AI- and ML-based algorithms and information. A proof-of-concept AI model achieved a mean DICE score of 0.76 (standard deviation 0.21). After the final testing stage, the model improved to a final mean DICE score of 0.88 (range 0.74–0.93, standard deviation 0.04). Our study has demonstrated an efficient, accurate AI for 3D volumetric analysis of vestibular schwannomas. The use of this AI will enable faster 3D volumetric analysis compared to manual segmentation. Additionally, the overlay function would allow visualization of growth patterns. The tool will be a method of assessing tumor growth and allow clinicians to make more informed decisions. [ABSTRACT FROM AUTHOR]

The current paper evaluates psychiatric needs of voluntary admissions in a large urban psychiatric hospital through a retrospective chart review, as this research is limited within the United States. A total of 581 voluntary adult psychiatric admission charts were reviewed. Continuous variables were evaluated using an ANOVA while associations between variables were examined by an unadjusted Pearson correlation coefficient a stepwise linear regression analysis. Men were significantly more likely to have a past admission for psychiatric services (p =.016), suicidal ideation (p <.001) and test positive for substances (p <.001) than women, and were more likely to be unemployed, homeless and without insurance. Women were more likely to have a past suicide attempt and a depressive disorder. A significant relationship between gender and rationale for seeking voluntary admission (p <.001) was found. This study offers understanding of male and female voluntary admissions, and a foundation for improving treatment interventions to reduce recurrent readmissions. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the rate of 72-hour letters (written requests for discharge, with 72 hours indicating the time the hospital has to discharge or seek retention) placed by voluntary psychiatric inpatients at a New York City hospital and determine whether there are factors contributing to the rates of discharge requests. Charts from all voluntary psychiatric hospitalizations during the calendar year 2007 were retrospectively reviewed. Included were all single voluntary admissions by adults (age 18 years and older) to the hospital. A confirmatory factor analysis (CFA) was conducted with the following factors: urine toxicology, cigarette use on admission, suicidal ideation upon presentation, employment, past inpatient psychiatric admission, and admission day. A linear regression was used to evaluate the relationship between discharge requests and these factors. Of the total sample (N = 581), 119 (20.5%) patients submitted 72-hour letters. The stepwise linear regression analysis confirmed a positive relationship between letter placement and admission day (M = 3.5, SD = 1.7), unemployment (M = 4.7, SD = 2.1), suicidal ideation (M =.5, SD =.5), positive urine toxicology (M =.47, SD =.5), previous psychiatric hospitalization (M =.7, SD =.5) and cigarette usage (M =.5, SD.5) R2 =.043, (6, 461) = 3.42, p =.003). These specific variables accounted for 55.6% of likelihood of a patient submitting a 72-hour letter. Several factors, related to substance and tobacco use, employment, and recurrent use of inpatient services, likely contribute to requests for early discharge. Addressing these factors may help improve inpatient care, reduce costs and improve patient outcomes in the long term. [ABSTRACT FROM AUTHOR]