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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.
Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.
Neurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
View Article and Find Full Text PDFML-based tooth shade assessment to prevent metamerism in different clinic lights.
Lasers Med Sci
January 2025
Erzincan University, 24002, Erzincan, Turkey.
The aesthetic understanding has found its place in dental clinics and prosthetic dental treatment. Determining the appropriate prosthetic tooth color between the clinician, patient and technician is a difficult process due to metamerism. Metamerism, known as the different perception of the color of an object under different light sources, is caused by the lighting differences between the laboratory and the dental clinic.
View Article and Find Full Text PDFLLMs, Truth, and Democracy: An Overview of Risks.
Sci Eng Ethics
January 2025
Department of Philosophy, University of Vienna, Vienna, Austria.
While there are many public concerns about the impact of AI on truth and knowledge, especially when it comes to the widespread use of LLMs, there is not much systematic philosophical analysis of these problems and their political implications. This paper aims to assist this effort by providing an overview of some truth-related risks in which LLMs may play a role, including risks concerning hallucination and misinformation, epistemic agency and epistemic bubbles, bullshit and relativism, and epistemic anachronism and epistemic incest, and by offering arguments for why these problems are not only epistemic issues but also raise problems for democracy since they undermine its epistemic basis- especially if we assume democracy theories that go beyond minimalist views. I end with a short reflection on what can be done about these political-epistemic risks, pointing to education as one of the sites for change.
View Article and Find Full Text PDFAn 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).
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