Purpose
The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy.
Methods
We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software.
Results
In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis.
Conclusion
This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Objectives: This study explored the experiences of medical students enrolled in an elective course titled "Healthcare Innovation and Women's Ventures II" at Ewha Womans University College of Medicine. The research questions were as follows: First, what motivated medical students to participate in the experiential entrepreneurship course? Second, what experiences did the students have during the course? Third, what changes did the students undergo as a result of the course?

Methods: Focus group interviews were conducted with six medical students who participated in the experiential entrepreneurship course from February 13 to 23, 2024.

Results: The analysis identified three domains, seven categories, and 17 subcategories. In terms of motivations for enrolling in the experiential entrepreneurship course, two categories were identified: "existing interest" and "new exploration." With respect to the experiences gained from the course, three categories emerged: "cognitive experiences," "emotional experiences," and "behavioral experiences." Finally, two categories were identified concerning the changes participants experienced through the course: "changes related to entrepreneurship" and "changes related to career paths."

Conclusion: Students were motivated to enroll in this course by both their existing interests and their desire to explore new areas. Following the course, they underwent cognitive, emotional, and behavioral changes. Their perceptions of entrepreneurship and career paths were significantly altered. This study is important because it explores the impact of entrepreneurship education in medical schools from the students' perspective.

Citations

• Unresolved policy on the new placement of 2,000 entrants at Korean medical schools and this issue of Ewha Medical Journal
  Sun Huh
  The Ewha Medical Journal.2024;[Epub]     CrossRef

Objectives:

This study aimed to develop an accurate pediatric bone age prediction model by utilizing deep learning models and contrast conversion techniques, in order to improve growth assessment and clinical decision-making in clinical practice.

Methods:

The study employed a variety of deep learning models and contrast conversion techniques to predict bone age. The training dataset consisted of pediatric left-hand X-ray images, each annotated with bone age and sex information. Deep learning models, including a convolutional neural network , Residual Network 50 , Visual Geometry Group 19, Inception V3, and Xception were trained and assessed using the mean absolute error (MAE). For the test data, contrast conversion techniques including fuzzy contrast enhancement, contrast limited adaptive histogram equalization (HE) , and HE were implemented. The quality of the images was evaluated using peak signal-to-noise ratio (SNR), mean squared error, SNR, coefficient of variation, and contrast-to-noise ratio metrics. The bone age prediction results using the test data were evaluated based on the MAE and root mean square error, and the t-test was performed.

Results:

The Xception model showed the best performance (MAE=41.12). HE exhibited superior image quality, with higher SNR and coefficient of variation values than other methods. Additionally, HE demonstrated the highest contrast among the techniques assessed, with a contrast-to-noise ratio value of 1.29. Improvements in bone age prediction resulted in a decline in MAE from 2.11 to 0.24, along with a decrease in root mean square error from 0.21 to 0.02.

Conclusion:

This study demonstrates that preprocessing the data before model training does not significantly affect the performance of bone age prediction when comparing contrast-converted images with original images.

Citations

• Gender equity in medicine, artificial intelligence, and other articles in this issue
  Sun Huh
  The Ewha Medical Journal.2024;[Epub]     CrossRef