ArticleBiomedicines2022
Hyperspectral Imaging during Normothermic Machine Perfusion-A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks.
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Machine perfusion distribution across clinical phenotypes in kidney transplantation: A national cohort study using unsupervised clustering.World journal of transplantation · 2026Article
- Label-free viability assessment ofBiomedical optics express · 2026Article
- Review
- Spectral Analysis of Extrahepatic Bile Ducts During Normothermic Liver Machine Perfusion.Bioengineering (Basel, Switzerland) · 2025Article
- Real-Time Viability Assessment of Ex Vivo Mouse Kidneys for Transplant Applications Using Dynamic Optical Coherence Tomography.bioRxiv : the preprint server for biology · 2025Article
- Research on a Burn Severity Detection Method Based on Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2025Article
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Spectral characterization of intraoperative renal perfusion using hyperspectral imaging and artificial intelligence.Scientific reports · 2024Article
- Hyperspectral imaging and its applications: A review.Heliyon · 2024Review
- Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.Sensors (Basel, Switzerland) · 2024Article
- Deep Learning in Medical Hyperspectral Images: A Review.Sensors (Basel, Switzerland) · 2022Review
- Hyperspectral Imaging for Viability Assessment of Human Liver Allografts During Normothermic Machine Perfusion.Transplantation direct · 2022Article
- Preoperative Function Assessment of Ex Vivo Kidneys with Supervised Machine Learning Based on Blood and Urine Markers Measured during Normothermic Machine Perfusion.Biomedicines · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Facing an ongoing organ shortage in transplant medicine, strategies to increase the use of organs from marginal donors by objective organ assessment are being fostered. In this context, normothermic machine perfusion provides a platform for ex vivo organ evaluation during preservation. Consequently, analytical tools are emerging to determine organ quality. In this study, hyperspectral imaging (HSI) in the wavelength range of 550-995 nm was applied. Classification of 26 kidneys based on HSI was established using KidneyResNet, a convolutional neural network (CNN) based on the ResNet-18 architecture, to predict inulin clearance behavior. HSI preprocessing steps were implemented, including automated region of interest (ROI) selection, before executing the KidneyResNet algorithm. Training parameters and augmentation methods were investigated concerning their influence on the prediction. When classifying individual ROIs, the optimized KidneyResNet model achieved 84% and 62% accuracy in the validation and test set, respectively. With a majority decision on all ROIs of a kidney, the accuracy increased to 96% (validation set) and 100% (test set). These results demonstrate the feasibility of HSI in combination with KidneyResNet for non-invasive prediction of ex vivo kidney function. This knowledge of preoperative renal quality may support the organ acceptance decision.
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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.