A Cascaded Deep Learning–Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging

2021 ◽  
Author(s):  
Sherif Mehralivand ◽  
Dong Yang ◽  
Stephanie A. Harmon ◽  
Daguang Xu ◽  
Ziyue Xu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Lesion Detection ◽  
Resonance Imaging ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

scholarly journals Backpropagation Neural Network Artificial Intelligence Algorithm-Based Magnetic Resonance Imaging Image Feature Analysis in the General Anesthesia Hip Arthroplasty

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yufang Li ◽  
Xin Wang ◽  
Qian Zhao ◽  
Xiaoqing Zhang ◽  
Manyun Bai
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Hip Arthroplasty ◽  
Bp Neural Network ◽  
Resonance Imaging ◽  
Backpropagation Neural Network ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

Objective. This study aimed to present an investigation of the clinical significance of magnetic resonance imaging (MRI) images obtained based on the backpropagation neural network (BPNN) artificial intelligence algorithm for hip arthroplasty under general anesthesia. Methods. In this study, a case-review method was used to collect 100 patients requiring total hip replacement. They were then randomly divided into an observation group and a control group. Based on the neural network algorithm, the images of the two groups of patients were analyzed to judge their accuracy. Then the sensitivity, specificity, and accuracy of MRI images based on neural algorithms were compared with those processed by radiologists. Results. It was found that MRI processed by BP neural network had good accuracy in the diagnosis of hip joint diseases compared with CT. Meanwhile, the images processed by BP neural network had good specificity and accuracy compared with the images processed by radiologists. Conclusion. Imaging images obtained by BPNN artificial intelligence algorithm were more accurate than CT images, which had more guiding value for surgeons in operation.

scholarly journals Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 959
Author(s):  
Jasper J. Twilt ◽  
Kicky G. van Leeuwen ◽  
Henkjan J. Huisman ◽  
Jurgen J. Fütterer ◽  
Maarten de Rooij
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
External Validation ◽  
Lesion Detection ◽  
Resonance Imaging ◽  
Clinical Workflow ◽  
The Impact ◽  
Lesion Classification

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.

scholarly journals A Gadolinium(III) Complex Based on the Thymine Nucleobase with Properties Suitable for Magnetic Resonance Imaging

2021 ◽  
Vol 22 (9) ◽  
pp. 4586
Author(s):  
Marta Orts-Arroyo ◽  
Amadeo Ten-Esteve ◽  
Sonia Ginés-Cárdenas ◽  
Isabel Castro ◽  
Luis Martí-Bonmatí ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Mr Imaging ◽  
Lesion Detection ◽  
Resonance Imaging ◽  
X Ray Diffraction ◽  
Functional Images ◽  
High Relaxivity ◽  
Contrast Enhanced ◽  
Phantom Studies

The paramagnetic gadolinium(III) ion is used as contrast agent in magnetic resonance (MR) imaging to improve the lesion detection and characterization. It generates a signal by changing the relaxivity of protons from associated water molecules and creates a clearer physical distinction between the molecule and the surrounding tissues. New gadolinium-based contrast agents displaying larger relaxivity values and specifically targeted might provide higher resolution and better functional images. We have synthesized the gadolinium(III) complex of formula [Gd(thy)2(H2O)6](ClO4)3·2H2O (1) [thy = 5-methyl-1H-pyrimidine-2,4-dione or thymine], which is the first reported compound based on gadolinium and thymine nucleobase. 1 has been characterized through UV-vis, IR, SEM-EDAX, and single-crystal X-ray diffraction techniques, and its magnetic and relaxometric properties have been investigated by means of SQUID magnetometer and MR imaging phantom studies, respectively. On the basis of its high relaxivity values, this gadolinium(III) complex can be considered a suitable candidate for contrast-enhanced magnetic resonance imaging.

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