Journal of Yeungnam Medical Science

Original articles

Biomedical Engineering
Gene expression-based machine learning model for diagnosis, prognosis, and treatment response prediction in hepatocellular carcinoma: a retrospective study: Tan Thinh Nguyen, Thanh Dat Nguyen, Phu Qui Le Nguyen, Phuong Thi Bui, Minh Nam Nguyen; J Yeungnam Med Sci. 2026;43:21. Published online March 4, 2026; DOI: https://doi.org/10.12701/jyms.2026.43.21

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Abstract PDF Supplementary Material: Background
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, largely because of challenges in early diagnosis and the limited sensitivity of conventional biomarkers. Therefore, reliable molecular tools for early detection, prognostic stratification, and individualized treatment predictions are urgently required.
Methods
This retrospective study analyzed publicly available gene expression datasets. Candidate biomarkers were identified from the GSE14520 cohort using a multistep screening workflow that integrated differential expression analysis, diagnostic performance, and prognostic relevance. A 10-gene diagnostic model was constructed using least absolute shrinkage and selection operator logistic regression and subsequently validated across multiple independent cohorts. Survival outcomes were evaluated using the Kaplan-Meier analysis and treatment responses to sorafenib and transarterial chemoembolization (TACE) were assessed using receiver operating characteristic analysis.
Results
A 10-gene signature (TOP2A, CDK1, CYP3A4, MASP2, EPHX2, HAO1, RACGAP1, GLYAT, ADH1B, and CYP4A11) was established. The model demonstrated robust internal performance and consistent accuracy across external validation cohorts (area under the curve [AUC], >0.9). This signature effectively identified early-stage HCC and distinguished malignancy from cirrhosis. High-risk scores were significantly associated with poor overall survival and recurrence-free survival (p<0.05). Furthermore, the model could predict treatment sensitivity, with higher risk scores associated with better outcomes for sorafenib (AUC, 0.791), whereas lower risk scores correlated with an improved response to TACE (AUC, 0.768).
Conclusion
Our gene expression-based machine learning model provides a robust tool for HCC diagnosis, prognosis, and treatment response prediction, with potential as a supportive system for personalized clinical decision-making.

Dentistry
Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study: Hyun Jun Kong; J Yeungnam Med Sci. 2023;40(Suppl):S29-S36. Published online July 26, 2023; DOI: https://doi.org/10.12701/jyms.2023.00465

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Background
This study aimed to evaluate the accuracy and clinical usability of implant system classification using automated machine learning on a Google Cloud platform.
Methods
Four dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score.
Results
The accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix.
Conclusion
Deep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.

Citations

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Review article

Medical Education
Trends in the study on medical education over the last 10 years, based on paper titles: Seong Yong Kim; Yeungnam Univ J Med. 2019;36(2):78-84. Published online May 13, 2019; DOI: https://doi.org/10.12701/yujm.2019.00206

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2 Crossref

Abstract PDF

Medical education research subjects are incredibly diverse and have changed over time. This work in particular aims to compare and analyze research trends in medical education through the words used in the titles of these research papers. Academic Medicine (the journal of the Association of American Medical Colleges), Medical Teacher (the journal of the Association of Medical Education in Europe), the Korean Journal of Medical Education (KJME), and Korean Medical Education Review (KMER) were selected and analyzed for the purposes of this research. From 2009 to 2018, Academic Medicine and Medical Teacher published approximately 10 to 20 times more papers than the KJME and KMER. Frequently used words in these titles include “medical,” “student,” “education,” and “learning.” The words “clinical” and “learning” were used relatively often (7.80% to 13.66%) in Korean Journals and Medical Teacher, but Academic Medicine used these phrases relatively less often (6.47% and 4.41%, respectively). Concern with such various topics as problem-based learning, team-based learning, program evaluations, burnout, e-learning, and digital indicates that Medical Teacher seems to primarily deal with teaching and learning methodologies, and Academic Medicine handles all aspects of medical education. The KJME and KMER did not cover all subjects, as they publish smaller papers. However, it is anticipated that research on new subjects, such as artificial intelligence in medical education, will occur in the near future.

Citations

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