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- Computer Aided Diagnosis
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International Journal Of Radiology And Imaging Technology Impact Factor – Single Quasi-Symmetrical LED with High Intensity and Wide Beam Width for Surgical Fluorescence Microscope Applications Using Diamond-Shaped Image Reflectance Method
Characterization of weighted deep learning using particle swarm and ant lion optimization for cervical cancer diagnosis in Pap smear images
International Journal Of Radiology And Imaging Technology Impact Factor
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Pdf) Knowledge Of Radiation Among Radiology Professionals And Students
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Received: 13 June 2023 / Revised: 1 August 2023 / Accepted: 10 August 2023 / Published: 25 August 2023
This comprehensive review presents a detailed account of artificial intelligence (AI) making its way into radiology, a movement that is driving transformative changes in the healthcare landscape. It follows the evolution of radiology, from the early discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, highlighting their main role in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimization. The profound impact of AI on diagnostic processes, personalized medicine, and clinical workflow is highlighted, with empirical evidence drawn from a series of case studies across multiple medical disciplines. However, integrating AI into radiology is not without its challenges. The review circles the hurdles related to AI-driven radiology – data quality, the “black box” issue, infrastructure and technical complexities, as well as ethical implications. Looking to the future, the review claims that the road ahead for AI in radiology is paved with promising opportunities. It supports continuous research, embraces avant-garde imaging technologies, and promotes strong collaboration between radiologists and AI developers. The conclusion underscores the role of AI as a catalyst for change in radiology, a position rooted in a steadfast commitment to continuous innovation, dynamic partnerships, and ethical responsibilities.
Radiology, since its inception, has experienced a revolutionary journey, with its profound influence preventing modern medicine. From the discovery of X-rays to the subsequent integration of artificial intelligence (AI) and machine learning (ML), this multifaceted discipline is constantly evolving, transforming itself and the healthcare ecosystem that underpins it.
This comprehensive review assesses the interaction of AI and ML in radiology, exploring their fundamental principles, historical development, practical applications, main challenges and ethical dilemmas. By enriching the understanding of the contribution of AI and ML in radiology, the review aims to promote insightful discussions among clinicians, researchers, and policy makers, ultimately guiding the field and patient outcomes. Improve results. Explore the fundamentals of AI and ML, their growing influence in radiology, practical integration strategies, and illustrative case studies in various medical specialties. In addition, it addresses challenges such as data quality, ethical concerns, and considers possible future directions in AI-driven radiology.
Advanced Ultrasound Technologies For Diagnosis And Therapy
Radiology, a medical discipline focused on the use of imaging methods to diagnose and treat disease, has emerged as a cornerstone of contemporary medicine, forming an important part of clinical practice. It extends beyond just disease detection to include treatment guidance and ongoing disease management. Expertise in diagnostic modalities such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, and X-rays to guide immediate clinical interventions, treatment monitoring, and a visual narrative of patient health. does The complex insights into anatomical, physiological and molecular disease processes provided by medical imaging have a significant impact on patient care, facilitating treatment tailoring, thereby improving treatment outcomes and reducing adverse effects [1, 2, 3]. .
Radiology serves as an important cog in the complex machinery of interdisciplinary medical teams. Radiologists provide accurate, timely imaging reports, thereby enhancing communication between different specialists and making critical decisions, which contribute to a holistic, patient-centered healthcare approach . As valuable consulting partners, radiologists provide key insights into the selection and interpretation of appropriate imaging studies, playing a critical role in radiation safety and dose management while their expertise illuminates the clinical picture, providing insights that can Clearly affect patient management [5, 6].
The metamorphosis of modern medical imaging technology, from Wilhelm Roentgen’s pioneering discovery of X-ray technology in 1895 to contemporary advanced techniques, demonstrates the relentless pursuit of scientific progress and its profound impact on radiology (Figure 1). Roentgen’s unique X-ray innovation provided a non-invasive view of the human body, forming the foundation of modern imaging. Despite its initial limitations in 2D representation and soft tissue contrast, this basic concept has laid the foundation for more sophisticated, noninvasive imaging approaches .
The advent of CT by Sir Godfrey Hounsfield and Alan Cormack in 1973 marked an important milestone, overcoming the limitations of 2D imaging by introducing a three-dimensional (3D) format . CT uses the basic principle of contrast absorption but combines it with a coordinated rotation of X-ray sources and detectors around the patient’s body, coupled with sophisticated computational algorithms, enabling the reconstruction of 3D volumetric data from the collected 2D images [7 ].
Computer Aided Diagnosis
Introduced in the 20th century, ultrasound imaging marked a shift away from ionizing radiation-based technologies by using high-frequency sound waves to create real-time images of internal body structures. Its non-ionizing radiation nature, real-time imaging capability, and cost-effectiveness have allowed wide application in various clinical fields, such as obstetrics, gynecology, cardiology, and emergency medicine . As an essential tool in emergency and critical care medicine, its changing role, particularly through point-of-care ultrasound (POCUS), has facilitated rapid bedside assessments and accelerated clinical decision-making .
In the 1970s, Paul Latterber and Sir Peter Mansfield led the development of MRI, a technology that uses a powerful magnetic field and radio waves to produce exceptionally detailed images of the body, particularly soft tissue structures [10, 11]. The non-ionising nature of MRI, combined with its unique soft tissue differentiation capability, has revolutionized medical imaging. Manipulation of RF pulse sequence timing in MRI has further enhanced its diagnostic utility, enabling the acquisition of different image types and the diagnosis of different tissues and pathologies .
Parallel to the disruptive innovations in imaging modalities, another important change occurred in the late 20th century: the introduction of film-based digital radiography and picture archiving and communication systems (PACS). This transition has fundamentally improved the efficiency of image acquisition, storage, and retrieval, while enabling the seamless sharing and transfer of images within and across healthcare institutions .
The change in medical imaging technology did not stop with these innovations. Functional imaging techniques such as PET, which is characterized by the use of specific radiolabeled biochemical substances, and single-photon emission computed tomography (SPECT), employing gamma-absorbing radionuclides to detect biological processes, are Elucidating metabolic and biological processes, opening a window. providing valuable insight into cellular function and organ functional status [13, 14].
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3D imaging has made significant advances in medical imaging by providing a more precise understanding of spatial relationships within the body, thereby improving diagnostic accuracy and surgical planning. Subsequent developments in four-dimensional (4D) imaging further strengthened the frontier by incorporating a time element, allowing for real-time monitoring of physiological processes [ 15 ].
The combination of functional and anatomical imaging has given rise to hybrid imaging technologies such as PET/CT and SPECT/CT. These methods combine the strengths of both techniques, providing comprehensive diagnostic information. For example, PET/CT combines the metabolic insight of PET with the detailed anatomical context of CT, significantly increasing the accuracy of lesion localization and characterization [ 16 ].
Finally, the emerging field of interventional radiology, which leverages imaging to guide during minimally invasive procedures, has changed the health care landscape. By providing real-time visualization of the target area, these procedures offer improved precision, potentially improving patient outcomes and reducing recovery time. For example, image-guided biopsy offers a safer and less invasive alternative to surgical biopsy, leading to fewer complications and shorter hospital stays .
The future of radiology promises to change through the integration of virtual/augmented reality (VR/AR) and AI, ushering in a new era of medical imaging. Emerging from the gaming and entertainment industries, VR/AR technology is gradually advancing radiology, providing a useful environment for radiology training and clinical practice. Lately,