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Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans.
Curr Eye Res
January 2025
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Purpose: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
Methods: The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups.
An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.
Sci Rep
January 2025
Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Budh Nagar, Noida, 201313, India.
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent's potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM).
View Article and Find Full Text PDFGender-Equity Model for Liver Allocation using Artificial Intelligence (GEMA-AI) for waiting list liver transplant prioritization.
Clin Gastroenterol Hepatol
January 2025
Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain. Campus Universitario de Rabanales, Albert Einstein Building. Ctra. N-IV, Km. 396. 14071, Córdoba, Spain; Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain. Av. Menéndez Pidal, s/n, Poniente Sur, 14004 Córdoba, Spain.
Background & Aims: We aimed to develop and validate an artificial intelligence score (GEMA-AI) to predict liver transplant (LT) waiting list outcomes using the same input variables contained in existing models.
Methods: Cohort study including adult LT candidates enlisted in the United Kingdom (2010-2020) for model training and internal validation, and in Australia (1998-2020) for external validation. GEMA-AI combined international normalized ratio, bilirubin, sodium, and the Royal Free Glomerular Filtration Rate in an explainable Artificial Neural Network.
The application of design of experiments and artificial neural networks in the evaluation of the impact of acidic conditions on cloud point extraction.
J Chromatogr A
January 2025
Department of Physical Pharmacy and Pharmacokinetics, Poznań University of Medical Sciences, Rokietnicka 3 Street, Poznań 60-806, Poland. Electronic address:
This study aimed to analyze the impact of acidic conditions on the recovery of ciprofloxacin and levofloxacin for cloud point extraction with the Design of Experiments and Artificial Neural Networks. The design included 27 experiments featuring three repetitions of the central point for both drugs. The tested parameters included Triton X-114 concentration, HCl concentration, NaCl concentration, and incubation temperature, which were coded at five levels.
View Article and Find Full Text PDFExploring continual learning strategies in artificial neural networks through graph-based analysis of connectivity: Insights from a brain-inspired perspective.
Neural Netw
January 2025
Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France.
Artificial Neural Networks (ANNs) aim at mimicking information processing in biological networks. In cognitive neuroscience, graph modeling is a powerful framework widely used to study brain structural and functional connectivity. Yet, the extension of graph modeling to ANNs has been poorly explored especially in terms of functional connectivity (i.
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