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Macular patterns of neuronal and visual field loss in recovered optic neuritis identified by machine learning.
Sci Rep
December 2024
Neurology, Icahn School of Medicine at Mount Sinai, New York, USA.
We used machine learning to investigate the residual visual field (VF) deficits and macula retinal ganglion cell (RGC) thickness loss patterns in recovered optic neuritis (ON). We applied archetypal analysis (AA) to 377 same-day pairings of 10-2 VF and optical coherence tomography (OCT) macula images from 93 ON eyes and 70 normal fellow eyes ≥ 90 days after acute ON. We correlated archetype (AT) weights (total weight = 100%) of VFs and total retinal thickness (TRT), inner retinal thickness (IRT), and macular ganglion cell-inner plexiform layer (GCIPL) thickness.
View Article and Find Full Text PDFFourier analysis of signal dependent noise images.
Sci Rep
December 2024
Cancer Epidemiology Department, H. Lee Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA.
An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making comparisons with SDN in both the image domain (ID) and Fourier domain (FD).
View Article and Find Full Text PDFMolecular insights into kinetic stabilization of amorphous solid dispersion of pharmaceuticals.
Phys Chem Chem Phys
January 2025
Department of Physical Chemistry, University of Chemistry and Technology Prague, Technická 5, CZ-166 28 Prague 6, Praha, Czech Republic.
Poor aqueous solubility of crystalline active pharmaceutical ingredients (APIs) restricts their bioavailability. Amorphous solid dispersions with biocompatible polymer excipients offer a solution to overcome this problem, potentially enabling a broader use of many drug candidate molecules. This work addresses various aspects of the design of a suitable combination of an API and a polymer to form such a binary solid dispersion.
View Article and Find Full Text PDFReal-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non-Small Cell Lung Cancer.
JCO Clin Cancer Inform
December 2024
Onc.AI, San Carlos, CA.
Purpose: This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC) using real-world data (RWD) and clinical trial data.
Materials And Methods: Retrospective RWD of 1,829 patients with advanced NSCLC treated with PD-(L)1 ICIs were collected from 10 academic and community institutions in the United States and Europe. The RWD included data sets for discovery (Data Set A-Discovery, n = 1,173) and independent test (Data Set B, n = 458).
AI depictions of psychiatric diagnoses: a preliminary study of generative image outputs in Midjourney V.6 and DALL-E 3.
BMJ Ment Health
December 2024
Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
Article Synopsis
- This paper examines how advanced AI image models depict common psychiatric diagnoses, highlighting the implications for mental health discussions among various stakeholders.
- The authors used AI models Midjourney V.6 and DALL-E 3 to generate images based on mental health diagnostic terms, revealing that these images often embody cultural stereotypes and negative portrayals.
- The findings stress the need for continuous monitoring of AI outputs, collaboration between mental health professionals and AI developers, and further research to address and mitigate biases inherent in AI-generated imagery.
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