AI Medical Compendium Topic

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Microscopy, Confocal

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[Artificial intelligence in ex vivo confocal laser scanning microscopy].

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete
BACKGROUND: Visual data, such as clinical photographs or pictures from imaging examination methods, such as ex vivo confocal laser scanning microscopy (CLSM), are particularly suitable for machine learning techniques.

Multiview confocal super-resolution microscopy.

Nature
Confocal microscopy remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-d...

Change patterns in the corneal sub-basal nerve and corneal aberrations in patients with dry eye disease: An artificial intelligence analysis.

Experimental eye research
We aimed to investigate the change patterns in corneal sub-basal nerve morphology and corneal intrinsic aberrations in dry eye disease (DED). Our study included 229 eyes of 155 patients with DED and 40 eyes of 20 healthy control. We used the Oculus k...

A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation.

Scientific reports
DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has become one of the most reliable and sensitive biomarkers of DNA DSBs. However, the γ-H2AX foci assa...

Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.

Journal of biomedical optics
SIGNIFICANCE: Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues struct...

Segmentation and Evaluation of Corneal Nerves and Dendritic Cells From In Vivo Confocal Microscopy Images Using Deep Learning.

Translational vision science & technology
PURPOSE: Segmentation and evaluation of in vivo confocal microscopy (IVCM) images requires manual intervention, which is time consuming, laborious, and non-reproducible. The aim of this research was to develop and validate deep learning-based methods...

Novel aspects of Raman spectroscopy in skin research.

Experimental dermatology
The analytical technology of Raman spectroscopy has an almost 100-year history. During this period, many modifications and developments happened in the method like discovery of laser, improvements in optical elements and sensitivity of spectrometer a...

DeepGrading: Deep Learning Grading of Corneal Nerve Tortuosity.

IEEE transactions on medical imaging
Accurate estimation and quantification of the corneal nerve fiber tortuosity in corneal confocal microscopy (CCM) is of great importance for disease understanding and clinical decision-making. However, the grading of corneal nerve tortuosity remains ...

Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy.

Scientific reports
Simultaneous point-by-point raster scanning of optical and acoustic beams has been widely adapted to high-speed photoacoustic microscopy (PAM) using a water-immersible microelectromechanical system or galvanometer scanner. However, when using high-sp...