ArticleInternational journal of computer assisted radiology and surgery2018
Computer-assisted liver graft steatosis assessment via learning-based texture analysis.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization.Journal of medical systems · 2019Pooled it
- A Scoping Review of the Photographic Assessment of Donor Liver Steatosis in Transplantation Using Artificial Intelligence.Clinical transplantation · 2026Review
- Deep learning for automated boundary detection and segmentation in organ donation photography.Innovative surgical sciences · 2025Article
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- Application possibilities of artificial intelligence in facial vascularized composite allotransplantation-a narrative review.Frontiers in surgery · 2023Review
- Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons.Frontiers in surgery · 2023Article
- Application of machine learning in liver transplantation: a review.Hepatology international · 2022Review
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation.Sensors (Basel, Switzerland) · 2021Article
- Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans.Medical & biological engineering & computing · 2020Article
- Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning.Sensors (Basel, Switzerland) · 2020Article
- Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring.Transplantation · 2019Review
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeFast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process.
methodsForty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was [Formula: see text]. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ([Formula: see text]), and gray-level co-occurrence matrix ([Formula: see text]) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance.
resultsWith the best-performing feature set ([Formula: see text]) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively.
conclusionsThis research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR.
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