Post-Transplant Liver Monitoring Utilizing Integrated Surface-Enhanced Raman and AI in Hepatic Ischemia-Reperfusion Injury Animal Model
Bibliographic record
Abstract
Sanghwa Lee,1,* Hyunhee Kwon,2,* Jeongmin Oh,3 Kyeong Ryeol Kim,3 Joonseup Hwang,3 Suyeon Kang,3 Kwanhee Lee,3 Jung-Man Namgoong,2 Jun Ki Kim1,3 1Biomedical Engineering Research Center, Asan Medical Center, Seoul, 05505, Republic of Korea; 2Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea; 3Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea*These authors contributed equally to this workCorrespondence: Jun Ki Kim, Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea, Email kim@amc.seoul.kr Jung-Man Namgoong, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea, Email namgoong2940@naver.comBackground: While liver transplantation saves lives from irreversible liver damage, it poses challenges such as graft dysfunction due to factors like ischemia-reperfusion (IR) injury, which can lead to significant cellular damage and systemic complications. Current diagnostic tools for detecting IR injury have limitations, necessitating advanced methods for timely intervention. This study explores the integration of surface-enhanced Raman spectroscopy (SERS) with artificial intelligence (AI) to improve diagnostic accuracy for liver IR injury.Materials and Methods: IR injury was induced using a mouse model, and histopathological and hepatic functional evaluations were conducted alongside SERS measurements. Raman signals obtained via SERS chips, which selectively filter nano-biomarkers and enhance signals, were analyzed using machine learning algorithms.Results: The PC-LDA derived from spectra achieved an accuracy of 93.13%, while a machine learning algorithm based on PC-derived PLS-DA improved accuracy to 98.75%.Discussion: Our findings emphasize the potential of combining SERS with AI to detect and specifically identify dysfunction due to liver damage early, potentially advancing patient management in liver transplantation. Keywords: liver ischemia-reperfusion injury, liver function, surface-enhanced Raman spectroscopy, discriminant analysis, machine learning algorithm
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".