Exploring the Mental Health of Japanese Graduate Trainees in Psychology:Comparison with Non-Trainees in Other Departments.

This study investigated the mental states, such as depression and anxiety, of Japanese psychology graduate trainees (N = 57) who aspired to become psychologists, compared with non-trainees (N = 80) in other departments. The results showed that, among trainees, 25% exhibited mild depression, 26% exhibited moderate depression, 9% exhibited moderately severe depression, and 5% exhibited severe depression. Among non-trainees, the corresponding figures were 31%, 10%, 6%, and 4%, respectively.

Experimental study using phantom models of cerebral aneurysms and 4D-DSA to measure blood flow on 3D-color-coded images.

Background: The current 3D-iFlow application can only measure the arrival time of contrast media through intensity values. If the flow rate could be estimated by 3D-iFlow, patient-specific hemodynamics could be determined within the scope of normal diagnostic management, eliminating the need for additional resources for blood flow rate estimation.

Objective: The aim of this study is to develop and validate a method for measuring the flow rate by data obtained from 3D-iFlow images - a prototype application in Four-dimensional digital subtraction angiography (4D-DSA).

Reliability and validation of the Japanese version of the cognitive distortion scale.

The cognitive distortion scale (CDS) is a self-rated measure to assess the degree of cognitive distortion which is 10 thinking errors commonly seen in depression. However, there is no scale to measure 10 types cognitive distortions specific to depression in Japan. Therefore, this study translated the CDS into Japanese (CDS-J), and examined its factor structure, validity, and reliability in a Japanese population.

Development of Machine Learning Model for Selecting the 1st Coil in the Treatment of Cerebral Aneurysms by Coil Embolization.

To achieve good treatment outcomes in coil embolization for cerebral aneurysms, it is important to select an appropriate 1st coil for each aneurysm since it serves as a frame to support the subsequent coils to be deployed. However, its selection as appropriate size and length from a wide variety of lineups is not easy, especially for inexperienced neurosurgeons. We developed a machine learning model (MLM) to predict the optimal size and length of the 1st coil by learning information on patients and aneurysms that were previously treated with coil embolization successfully.