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Isaac Flores
Isaac Flores

Download High Quality Shree Ram Images for Free - Graphic Resources


There are AI-generated images of Lord Ram that have been circulating on social media. These images depict Lord Ram at the age of 21 and have been created using artificial intelligence technology. The images have been widely shared and appreciated by people who have an interest in mythology and Indian history. Some of these images have been created by artists and shared on social media platforms.


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Generative AI is a form of artificial intelligence that is capable of generating creative content, such as images, music, text, and even entire videos, without any human intervention. It uses machine learning algorithms to analyze and learn from large datasets, and then uses this knowledge to create new content that resembles what it has learned.


Another popular application of generative AI is image and video generation, where the algorithm learns from a large set of images or videos and then generates new content that is similar in style and composition. This can be used for a variety of applications, such as creating realistic 3D models, generating new art, or even producing entire movies.


The above logo design and the artwork you are about to download is the intellectual property of the copyright and/or trademark holder and is offered to you as a convenience for lawful use with proper permission from the copyright and/or trademark holder only. You hereby agree that you agree to the Terms of Use and that the artwork you download will be used for non-commercial use without infringing on the rights of the copyright and/or trademark holder and in compliance with the DMCA act of 1998. Before you use or reproduce this artwork in any manner, you agree to obtain the express permission of the copyright and/or trademark holder. Failure to obtain such permission is a violation of international copyright and trademark laws subject to specific financial and criminal penalties.


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Background: Anterior cruciate ligament (ACL) injuries are common in sports and are critical knee injuries that require prompt diagnosis. Magnetic resonance imaging (MRI) is a strong, noninvasive tool for detecting ACL tears, which requires training to read accurately. Clinicians with different experiences in reading MR images require different information for the diagnosis of ACL tears. Artificial intelligence (AI) image processing could be a promising approach in the diagnosis of ACL tears.


Objective: This study sought to use AI to (1) diagnose ACL tears from complete MR images, (2) identify torn-ACL images from complete MR images with a diagnosis of ACL tears, and (3) differentiate intact-ACL and torn-ACL MR images from the selected MR images.


Methods: The sagittal MR images of torn ACL (n=1205) and intact ACL (n=1018) from 800 cases and the complete knee MR images of 200 cases (100 torn ACL and 100 intact ACL) from patients aged 20-40 years were retrospectively collected. An AI approach using a convolutional neural network was applied to build models for the objective. The MR images of 200 independent cases (100 torn ACL and 100 intact ACL) were used as the test set for the models. The MR images of 40 randomly selected cases from the test set were used to compare the reading accuracy of ACL tears between the trained model and clinicians with different levels of experience.


Results: The first model differentiated between torn-ACL, intact-ACL, and other images from complete MR images with an accuracy of 0.9946, and the sensitivity, specificity, precision, and F1-score were 0.9344, 0.9743, 0.8659, and 0.8980, respectively. The final accuracy for ACL-tear diagnosis was 0.96. The model showed a significantly higher reading accuracy than less experienced clinicians. The second model identified torn-ACL images from complete MR images with a diagnosis of ACL tear with an accuracy of 0.9943, and the sensitivity, specificity, precision, and F1-score were 0.9154, 0.9660, 0.8167, and 0.8632, respectively. The third model differentiated torn- and intact-ACL images with an accuracy of 0.9691, and the sensitivity, specificity, precision, and F1-score were 0.9827, 0.9519, 0.9632, and 0.9728, respectively.


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