Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine.

Background: Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying and classifying patients with LBP. Hence, this study aimed to investigate the role of artificial intelligence (AI) models in the prediction of LBP using T2 weighted MRI image of the lumbar spine.

Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain- a pilot study.

Background: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blotchy appearance and decreased resolution for subtle contrasts. The deep-learning image reconstruction (DLIR) algorithm, which integrates a convolutional neural network (CNN) into the reconstruction process, generates high-quality images with minimal noise.

Evaluation of positioning accuracy, radiation dose and image quality: artificial intelligence based automatic versus manual positioning for CT KUB.

Background: Recent innovations are making radiology more advanced for patient and patient services. Under the immense burden of radiology practice, Artificial Intelligence (AI) assists in obtaining Computed Tomography (CT) images with less scan time, proper patient placement, low radiation dose (RD), and improved image quality (IQ). Hence, the aim of this study was to evaluate and compare the positioning accuracy, RD, and IQ of AI-based automatic and manual positioning techniques for CT kidney ureters and bladder (CT KUB).

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

Background: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further.

Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study.

Background: Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC).

Impact of slice thickness on reproducibility of CT radiomic features of lung tumors.

Background: Radiomics posits that quantified characteristics from radiographic images reflect underlying pathophysiology. Lung cancer (LC) is one of the prevalent forms of cancer, causing mortality. Slice thickness (ST) of computed tomography (CT) images is a crucial factor influencing the generalizability of radiomic features (RF) in oncology.

Classification of low- and high-grade gliomas using radiomic analysis of multiple sequences of MRI brain.

Background: Gliomas are frequent tumors of brain parenchyma, which have histology similar to that of glial cells. Accurate glioma grading is required for determining clinical management. The background of this study is to investigate the accuracy of magnetic resonance imaging (MRI)-based radiomic features extracted from multiple MRI sequences in differentiating low and high-grade gliomas.

Correction: Metal-organic-framework derived Co-Pd bond is preferred over Fe-Pd for reductive upgrading of furfural to tetrahydrofurfuryl alcohol.

Correction for 'Metal-organic-framework derived Co-Pd bond is preferred over Fe-Pd for reductive upgrading of furfural to tetrahydrofurfuryl alcohol' by Saikiran Pendem , , 2019, , 8791-8802, https://doi.org/10.1039/C9DT01190K.

Diagnostic reference levels for computed tomography examinations in pediatric population - A systematic review.

Computed tomography (CT) has vital role in diagnosis of various pathologies using cross sectional images. Besides the advantages of CT in pediatric radiology, radiation dose has a significant adverse effect as children are more vulnerable than adults. Establishing Diagnostic Reference levels (DRLs) will determine unusual increase in radiation doses and therefore helps in optimizing the radiation dose by maintaining optimum diagnostic image quality.

Effectiveness of QSM over R2* in assessment of parkinson's disease - A systematic review.

The incidence and prevalence of Parkinson's (PD) are increasing rapidly in developing countries. PD is difficult to diagnose based on clinical assessment. Presently, magnetic resonance imaging (MRI) methods such as R2* and Quantitative Susceptibility Mapping (QSM) were found to be useful in diagnosing the PD based on the iron deposition in different regions of the brain.

Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer.

Objectives: We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk.

Materials And Methods: This is a retrospective case-control study.

Metal-organic-framework derived Co-Pd bond is preferred over Fe-Pd for reductive upgrading of furfural to tetrahydrofurfuryl alcohol.

Combined noble-transition metal catalysts have been used to produce a wide range of important non-petroleum-based chemicals from biomass-derived furfural (as a platform molecule) and have garnered colossal research interest due to the urgent demand for sustainable and clean fuels. Herein, we report the palladium-modified metal-organic-framework (MOF) assisted preparation of PdCoO and PdFeO nanoparticles encapsulated in a graphitic N-doped carbon (NC) matrix via facile in situ thermolysis. This provides a change in selectivity with superior catalytic activity for the reductive upgrading of biomass-derived furfural (FA).

Interface Engineering of Graphene-Supported Cu Nanoparticles Encapsulated by Mesoporous Silica for Size-Dependent Catalytic Oxidative Coupling of Aromatic Amines.

In this study, graphene nanosheet-supported ultrafine Cu nanoparticles (NPs) encapsulated with thin mesoporous silica (Cu-GO@m-SiO) materials are fabricated with particle sizes ranging from 60 to 7.8 nm and are systematically investigated for the oxidative coupling of amines to produce biologically and pharmaceutically important imine derivatives. Catalytic activity remarkably increased from 76.