ArticleJournal of clinical medicine2024
Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence.
Article in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping the knowledge domains of literature on hepatocellular carcinoma and liver failure: a bibliometric approach.Frontiers in oncology · 2025Pooled it
- Couinaud segment-aware deep learning on point clouds for major liver resection planning.International journal of computer assisted radiology and surgery · 2026Article
- Machine Learning-Driven Risk Prediction Models for Posthepatectomy Liver Failure: A Narrative Review.Medicina (Kaunas, Lithuania) · 2026Review
- Editorial: Regenerative liver surgery.Frontiers in surgery · 2026Article
- Radiomics Beyond Radiology: Literature Review on Prediction of Future Liver Remnant Volume and Function Before Hepatic Surgery.Journal of clinical medicine · 2025Review
- Predictors of Post-Hepatectomy Liver Failure in Klatskin Tumors: The Role of Preoperative Glucose, Future Liver Remnant to Spleen Ratio, and Early Bilirubin Monitoring.Diagnostics (Basel, Switzerland) · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: Advancements in the field of liver surgery have led to a critical need for precise estimations of preoperative liver function to prevent post-hepatectomy liver failure (PHLF), a significant cause of morbidity and mortality. This study introduces a novel application of artificial intelligence (AI) in determining safe resection volumes according to a patient's liver function in major hepatectomies. (2) Methods: We incorporated a deep learning approach, incorporating a unique liver-specific loss function, to analyze patient characteristics, laboratory data, and liver volumetry from computed tomography scans of 52 patients. Our approach was evaluated against existing machine and deep learning techniques. (3) Results: Our approach achieved 68.8% accuracy in predicting safe resection volumes, demonstrating superior performance over traditional models. Furthermore, it significantly reduced the mean absolute error in under-predicted volumes to 23.72, indicating a more precise estimation of safe resection limits. These findings highlight the potential of integrating AI into surgical planning for liver resections. (4) Conclusion: By providing more accurate predictions of safe resection volumes, our method aims to minimize the risk of PHLF, thereby improving clinical outcomes for patients undergoing hepatectomy.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.